Citation
Identification of Preferred Performance Measures for the Assessment of Truck Level of Service

Material Information

Title:
Identification of Preferred Performance Measures for the Assessment of Truck Level of Service
Creator:
Ko, Byungkon
Copyright Date:
2007
Language:
English

Subjects

Subjects / Keywords:
Business management ( jstor )
Focus groups ( jstor )
Freeways ( jstor )
Highways ( jstor )
Motor vehicle traffic ( jstor )
Roads ( jstor )
Travel ( jstor )
Truck drivers ( jstor )
Trucking ( jstor )
Trucks ( jstor )

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
Copyright Byungkon Ko. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Embargo Date:
7/12/2007
Resource Identifier:
659806745 ( OCLC )

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Full Text





IDENTIFICATION OF PREFERRED PERFORMANCE MEASURES FOR THE
ASSESSMENT OF TRUCK LEVEL OF SERVICE




















By

BYUNGKON KO


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2007





































O 2007 Byungkon Ko

































To the graduate students of the University of Florida









ACKNOWLEDGMENTS

It may not have been possible for me to complete my Ph.D. study without the advice or

assistance of those who have supported me throughout my life in Gainesville. They are my

family members, friends, and University of Florida faculties and administrative staffs. Although

I cannot adequately describe how blessed I am to have been with them, I would like to show my

gratitude to them with this opportunity.

I am especially grateful to my parents, wife, and four siblings. Their consistent care and

support have been a true driving factor for me to succeed in preparing for my future as well as

earning the degree.

I would like to express my appreciation to Dr. Scott S. Washburn who has served as my

academic advisor for my Ph.D. study. He supported me financially and his exceptional

knowledge and wisdom has provoked academic curiosity that encouraged me to enhance my

research ability onto a higher level.

I also would like to thank my Ph.D. supervisory committee members for their time and

effort to provide me with invaluable advice for my research. Dr. Lily Elefteriadou has guided

me into the right direction academically and also provided many graduate students with

convenient study environment as the director of Transportation Research Center. Dr. Yafeng

Yin lent me his acute insight on mathematical expressions and recommended better ways to

organize the dissertation. Dr. Siva Srinivasan helped me to improve statistical data analysis and

modeling part of my work. Dr. Joseph Geunes in the Industrial Engineering department served

as an external committee member. His consistent concerns and advice about my work has

become a motivation to improve overall performance of my study.










My friends in Korea or Gainesville have always cheered me up for my successful career at

the University of Florida. I will never forget the time and memory I have shared with them.

I certainly appreciate the assistance of University of Florida faculties and staffs. They

have always tried to answer my academic or administrative questions in spite of their tight time

schedules.












TABLE OF CONTENTS


page

ACKNOWLEDGMENTS .............. ...............4.....


LIST OF TABLES ............ ...... .__ ...............10...


LIST OF FIGURES .............. ...............13....


LIST OF TERMS ............_ ..... ..__ ...............14...


AB S TRAC T ............._. .......... ..............._ 17...


CHAPTER


1 INTRODUCTION ................. ...............20.......... ......


1.1 Background ........._..... ...._... ...............20....
1.2 Problem Statement ........._..... ...._... ...............21....
1.3 Study Obj ectives and Tasks ........._..... ...._... ...............23..
1.4 Organization of the Dissertation ........._..... ...._... ...............25...

2 LITERATURE REVIEW ........._.._.. ...._... ...............26....


2.1 Truck Level of Service .............. ...............26....
2.2 User-Perceived Level of Service ............... ...............30....
2.3 Trucking Community Focus Groups and Surveys............... ..... .............3
2.4 Florida Trucking Community Focus Group Participant Recruitment Sources ........._.....52
2.5 Trucking Community Survey Methods .............. ...............54....

3 RESEARCH APPROACH .........._.... ...............59._.._. ......


3.1 Focus Group Sessions ........._...... ...............61._.__. ....
3.1.1 Participant Recruitment ................. ...............62........... ..
3.1.2 Participant Selection ................. ...............63...............
3.1.3 Focus Group Questionnaires .............. ...............66....
3.1.4 Conducting Interviews. ................ ................ .................. ....__..68
3.1.5 Summary of Discussions .......................... .................. ................69
3.2 Survey Studies .............. ...............70....
3.2. 1 Question Types ............... ... ...............71.......... ...
3.2.1.1 Interval-rating questions............... ...............7
3.2. 1.2 Ratio-scale questions ................. ...............72...............
3.2.1.3 Forced-ranking questions .............. ...............73....
3.2. 1.4 Discrete-choice questions ................. ...............74...............
3.2.1.5 Fixed-sum questions............... ...............7
3.2.2 Survey Development .............. ...............76....
3.2.3 Data Collection............... ...............8











3.2.4 Data Reduction ................ ...............88................
3.2.5 Data Analysis................. ...............8
3.2.5.1 Descriptive statistics............... ...............8
3.2.5.2 Exploratory factor analysis............... ...............90
3.2.5.3 Multiple comparison test ................. ...............97..............
3.2.5.4 Non-parametric test ................. ...............99........... ...
3.2.5.5 Chi-squared test............... ...............101.
3.3 Truck LOS Measurement .............. ...............103....
3.3.1 Truck LOS Service Measures............... ... .............10
3.3.1.1 Single performance measure approach .............. ..... ............... 10
3.3.1.2 Multiple variable approach............... ...............10
3.3.2 Truck LOS Estimation Model .............. ...............105....
3.3.2.1 Data collection............... ..............10
3.3.2.2 Statistical modeling ................ ......... ....___ ...........10

4 FOCUS GROUP STUDY RESULTS ........._._. ......... ...............115.


4. 1 Perceptions of Truck Drivers ........._..... ........._.__ ...............115..
4. 1.1 Truck Route and Departure Time Selection ................. ............................116
4. 1.2 Factors Affecting Truck Trip Quality............... .................118
4. 1.2. 1 Factors affecting truck trip quality on freeways............_. .. ........._._ ...118
4. 1.2.2 Factors affecting truck trip quality on urban arterials .............. .... ........._..122
4. 1.2.3 Factors affecting truck trip quality on two-lane highways......................... 126
4. 1.2.4 Factors affecting truck trip quality on hub facilities .............. ................. 128
4. 1.3 Improvement Priority of Transportation Services ................. .......................128
4. 1.4 Truck Delivery Schedule Reliability ................. ...............129........... ..
4.2 Perceptions of Truck Company Managers .............. .....................130
4.2. 1 Truck Route and Departure Time Selection ................. ............................131
4.2.2 Factors Affecting Truck Trip Quality............... ...............133
4.2.2.1 Factors affecting truck trip quality on freeways............... .................3
4.2.2.2 Factors affecting truck trip quality on urban arterials .............. .... ........._..136
4.2.2.3 Factors affecting truck trip quality on two-lane highways.........................140
4.2.2.4 Factors affecting truck trip quality on hub facilities ................. ...............141
4.2.3 Truck Delivery Schedule Reliability .............. ... .. .......... ....... ....... ........14
4.3 Perceptional Difference between Truck Drivers and Truck Company Managers..........1 43

5 SURVEY DATA ANALYSIS RESULTS .............. ...............145....


5.1 Backgrounds of the Participants ................ ...............145....._... ..
5.1.1 Truck Driver Participants ................. .......... ...............145...
5.1.2 Truck Company Manager Participants ................. ........... ............... ....14
5.2 Perceptions on the Relative Importance of Each Factor on Freeways .................. .........147
5.2. 1 Relative Importance of Each Factor ............... ............ ..............__ ....14
5.2.2 Applicability of Single Hypothetical Performance Measure to Estimate Truck
Trip Q uality............... ..... .. .... .... ... ......... 5
5.3 Perceptions on the Relative Importance of Each Factor on Urban Arterials. ........._.......155
5.3.1 Relative Importance of Each Factor ........._._.._......_.. ...............155.











5.3.2 Applicability of Single Hypothetical Performance Measure to Estimate Truck
Trip Q uality............... ..... .. .... .. ..... ......... 5
5.4 Perceptions on the Relative Importance of Each Factor on Two-Lane Highways......... 162
5.4. 1 Relative Importance of Each Factor ................. .... ... ... ...... ... .............. .... 16
5.4.2 Applicability of Single Hypothetical Performance Measure to Estimate Truck
T rip Q uality................ .. ....... .. ......... .. .. ......... 6
5.5 Relative Importance of Each Category of Factors to Quality of a Truck Trip ...............168
5.5.1 Relative Importance of Each Category of Factors for Freeways ................... ......169
5.5.2 Relative Importance of Each Category of Factors for Urban Arterials ................169
5.5.3 Relative Importance of Each Category of Factors for Two-lane Highways ........170
5.6 Comparisons of the Importance of Each Factor Category on Various Roadway
Facilities ........... __...... .. ..... ... .._ ... .. ...........17
5.7 Perceptions on the Improvement Priority of Various Roadway Types ........................171
5.8 Relationships between Truck Drivers' Backgrounds and Their Perceptions on the
Applicability of Each Hypothetical LOS Performance Measure .............. .. .................172
5.8.1 Truck Drivers' Backgrounds that Explain Their Perceptions on the
Importance of Each Hypothetical LOS Performance Measure on Freeways ............173
5.8.2 Truck Drivers' Backgrounds that Explain Their Perceptions on the
Importance of Each Hypothetical LOS Performance Measure on Urban Arterials...175
5.8.3 Truck Drivers' Backgrounds that Explain Their Perceptions on the
Importance of Each Hypothetical LOS Performance Measure on Two-Lane
H ighw ays ................ .... .. .. .. ................17
5.9 Perceptions on Truck Driving Environment by Time of Day ................. ........._......180
5.9. 1 Current and Preferred Truck Driving Time of Day ................. .... ....._._.........181
5.9.2 Relationships between Truck Drivers' Backgrounds and Their Perceptions on
Preferred Truck Driving Time of Day ................. ........ ....._. ...........8
5.10 Other Transportation Service Issues for Truck Drivers ........__. ........ _.._.............186
5.10.1 Freeway Truck Operations .............. ...............186....
5. 10.2 Urb an Arteri al Truck Operati ons ....._.__._ ........___ ...............187
5.10.3 Two-Lane Highway Truck Operations............... ..............18

6 CONCLUSIONS AND RECOMMENDATIONS .............. ...............239....

6.1 Conclusions.................... .................23
6. 1.1 Quality of a Truck Trip on Freeways .............. ...............240....
6. 1.2 Quality of a Truck Trip on Arterials............... ...............24
6. 1.3 Quality of a Truck Trip on Two-Lane Highways............... .. ................ 24
6. 1.4 Improvement Priority of Various Transportation Facilities for Trucks ...............244
6. 1.5 Improvement Priority of the Factors on Each Transportation Facility for
Trucks .............. ... .. ......... .. .............24
6. 1.6 Preference on Truck Driving Time of Day ................. .. ........... ..... ............. ..245
6. 1.7 Relationships between Truck Drivers' Backgrounds and Their Perceptions on
Truck Trip Quality ................. ... .. ....... .. ..........4
6. 1.8 Overall Effectiveness of Research Approach ....._.__._ .........__ ................ 247
6.2 Recommendations............... ............. ..........4
6.2.1 Truck LOS Estimation Model Development............... ..............24
6.2.1.1 Truck LOS on freeways .............. ...............248....











6.2. 1.2 Truck LOS on arterials ........._._. ...._._ ...............251.
6.2.1.3 Truck LOS on two-lane highways ................ ......... ... .........5
6.2.2 Transportation Service Improvement for the Trucking Community ........._._.......252
6.2.3 Trucking Community Surveys .............. ...............253....

APPENDIX


A COOPERATION REQUEST LETTER SENT TO FTA .............. ..... ............... 25

B FOCUS GROUP INSTRUCTION .............. ...............259....

C GUIDELINES FOR FOCUS GROUP PARTICIPANT SELECTION SENT TO FTA......261

D FOCUS GROUP MODERATOR' S GUIDE ...._. ......_._._ .......__. ...........6

E FOCUS GROUP PARTICIPANTS' BACKGROUND SURVEY RESULTS .........._.......268

F TRUCK DRIVER FOCUS GROUP BACKGROUND SURVEY FORM.............._._........272

G TRUCK COMPANY MANAGER FOCUS GROUP BACKGROUND SURVEY
FORM ........._._ ...... .. ...............275...

H TRUCK DRIVER SURVEY FORM............... ...............278.

I TRUCK COMPANY MANAGER SURVEY FORM .............. ...............285....

J IMPROVEMENT PRIORITY SCORE ...._. ......_._._ ......._. .............9

K PO STAGE-PAID TRUCK DRIVER SURVEY FORM ........._.._.. .....___ ..............294

L PO STAGE-PAID TRUCK COMPANY MANAGER SURVEY FORM................_..__........298

M POSTAGE-PAID MANAGER SURVEY COVER LETTER ....._____ ....... ......_........3 02

N SURVEY DATA FILTERING CRITERIA ................ ...............304........... ...

LIST OF REFERENCES ................. ...............307................

BIOGRAPHICAL SKETCH ................. ...............3.. 11.............










LIST OF TABLES


Table page

2-1 Comparison of Survey Methods .............. ...............58....

3-1 Survey Development............... ..............10

3-2 Postage-Paid Truck Driver Survey Response Rates ................. .......... ................1 09

3-3 Survey Participation of the Selected Carriers by Each Conference and Chapter ............ 110

3-4 Survey Collection by Each Conference and Chapter ................. ......... ................11 1

3-5 FTDC Truck Driver Survey Data Usability ................. ...............111........... ..

3-6 Postage-Paid Truck Driver Survey Data Usability ................. .............................111

3-7 Overall Truck Driver Survey Data Usability ................. ...............112........... ..

3-8 Overall Truck Company Manager Survey Data Usability ................. ............ .........112

3-9 Current HCM Service Measures used for LOS Determination .........._.... ........._......114

5-1 Truck Driver Survey Respondent Background Summary Statistics ............... .... ........._..188

5-2 Additional Truck Driver Survey Respondent Background Summary Statistics ..............189

5-3 Truck Company Manager Survey Respondent Background Summary Statistics ...........190

5-4 Additional Truck Company Manager Survey Respondent Background Summary
Statistics ................ ...............191................

5-5 Truck Drivers' Perceptions on Each Factor on Truck Travel Quality of Service on
Freew ay s .............. ...............192....

5-6 Managers' Perceptions on Relative Importance of Each Factor on Freeways ................193

5-7 Exploratory Factor Analysis Results .............. ...............194....

5-8 Importance of Each Factor on Truck Travel Quality of Service on Freeways ................195

5-9 Truck Drivers' Perceptions on Applicability of Single Performance Measure to
Determine Truck Travel Quality of Service on Freeways ....._____ ..... ....__ ..........196

5-10 Truck Company Managers' Perceptions of Relative Importance of Each Truck
Driving Condition on Freeways for trucking business .............. ...............196....

5-11 Games-Howell Post Hoc Test Results ...._.._.._ .... .._._. ...._.._ ...........9











5-12 Truck Drivers' Perceptions of Each Factor on Truck Travel Quality of Service on
Urban Arterial s............... ...............19

5-13 Managers' Perceptions on Relative Importance of Each Factor on Urban Arterials.......199

5-14 Exploratory Factor Analysis Results .............. ...............200....

5-15 Importance of Each Factor on Truck Travel Quality of Service on Urban Arterials ......201

5-16 Truck Drivers' Perceptions of Applicability of Single Performance Measure to
Determine Truck Travel Quality of Service on Urban Arterials .............. ...................202

5-17 Truck Company Managers' Perceptions of Relative Importance of Each Truck
Driving Condition on Urban Arterials for trucking business .............. ....................20

5-18 Games-Howell Post Hoc Test Results ...._.._.._ .... .._._. ...._.._ ...........0

5-19 Truck Drivers' Perceptions of Each Factor on Truck Travel Quality of Service on
Two-Lane Highways............... ...............20

5-20 Managers' Perceptions of Relative Importance of Each Factor on Two-Lane
Highways .............. ...............205....

5-21 Exploratory Factor Analysis Results .............. ...............206....

5-22 Importance of Each Factor on Truck Travel Quality of Service on Two-Lane
Highways .............. ...............207....

5-23 Truck Drivers' Perceptions of Applicability of Single Performance Measure to
Determine Truck Travel Quality of Service on Two-Lane Highways ............................208

5-24 Truck Company Managers' Perceptions of Relative Importance of Each Truck
Driving Condition on Two-Lane Highways for trucking business............... ................20

5-25 Games-Howell Post Hoc Test Results ...._.._.._ .... .._._. ...._.._ ...........0

5-26 Definitions of Independent Variables used in Statistical Tests ........._.._... ........_.._... ...217

5-27 Definitions of Independent Variables used in Statistical Tests ........._.._... ........_.._... ...218

5-28 Kruskal-Walli s and Mann-Whitney Test Stati stics ................. ................. ..........21 9

5-29 Kruskal-Walli s and Mann-Whitney Test Stati stics ................. ................. ..........220

5-30 Kruskal-Walli s and Mann-Whitney Test Stati stics ................. ................. ..........22 1

5-3 1 Kruskal-Walli s and Mann-Whitney Test Stati stics ................. ................. ..........222

5-32 Kruskal-Walli s and Mann-Whitney Test Stati stics ................. ................. ..........223










5-33 Kruskal-Walli s and Mann-Whitney Test Stati stics ................. ................. ..........224

5-34 Mann-Whitney Test Statistics............... ..............22

5-35 Kruskal-Walli s and Mann-Whitney Test Stati stics ................. ................. ..........226

5-36 Kruskal-Walli s and Mann-Whitney Test Stati stics ................. ................. ..........227

5-37 Mann-Whitney Test Statistics............... ..............22

5-38 Chi-Squared Test Statistics 1 .............. ...............232....

5-39 Chi-Squared Test Statistics 2 ................ ...............233........... ..

5-40 Chi-Squared Test Statistics 3 .............. ...............233....

5-41 Chi-Squared Test Statistics 4 ................ ...............234........... ..

5-42 Other Factors Affecting Truck Trip Quality on Freeways............... ...............23

5-43 Other Drivers' Behavior Affecting Truck Trip Quality on Freeways .............................236

5-44 Other Factors Affecting Truck Trip Quality on Urban Arterials............... ................3

5-45 Other Factors Affecting Truck Trip Quality on Two-Lane Highways ................... .........238

6-1 Truck Trip Quality of Service Determinants on Freeways ................. ......................254

6-2 Truck Trip Quality of Service Determinants on Arterials .............. ....................25

6-3 Truck Trip Quality of Service Determinants on Two-Lane Highways ...........................255

6-4 Top Six Factors in the Need for the Improvement on Each Roadway Type for Trucks .256

E-1 1st Truck Driver Focus Group Participants' Background Survey Results .......................268

E-2 1st Truck Company Manager Focus Group Participants' Background Survey Results...269

E-3 2nd Truck Driver Focus Group Participants' Background Survey Results ......................270

J-1 Improvement Priority Scores ................. ...............292...___ ....










LIST OF FIGURES


Figure page

3-1 Research Approach ................ ...............108................

3-2 Truck Driving Competition Course ................. ......... ...............109 ....

3-3 Survey Table Setup at Truck Driving Competition ................ ............................110

3-4 Example of a Path Diagram for an EFA Model by Principle Component Extraction
M ethod ........... ..... .._ ...............113..

3-5 Normal Q-Q Plot of the Importance of 'Ease of Obtaining Useful Traveler
Information' on Freeways ........... ..... .._ ...............113..

5-1 Relative Importance of Each Factor Category for Freeways.............__ ..........___.....210

5-2 Relative Importance of Each Factor Category for Urban Arterials .............. .... ........._...21 1

5-3 Relative Importance of Each Factor Category for Two-lane Highways. ................... ......212

5-4 Relative Importance of Roadway Conditions on Different Roadway Types ................ ..213

5-5 Relative Importance of Traffic Conditions on Different Roadway Types ......................214

5-6 Relative Importance of Other Drivers' Behavior on Different Roadway Types .............215

5-7 Improvement Priority of Various Roadway Facilities for Truck Trip Quality ........._.....216

5-8 Truck Drivers' Current and Preferred Truck Driving Times of Day ............... .... ...........229

5-9 Truck Driving Time of Day Preference of Current Users ................. ............ .........230

5-10 Truck Company Managers' Preference on Truck Driving Times of Day ................... ....23 1









LIST OF TERMS

American Association of State Highway Officials (now AASHTO)

American Association of State Highway and Transportation Officials

Analysis of Variance

American Trucking Association

Bureau of Transportation Statistics

Computer-Aided Telephone Interview

Central Business District

Citizen Band Radio

Commercial Driver' sLicense

Center for Economic Development and Research

Changeable Message Sign

Commercial Motor Vehicle

Exploratory Factor Analysis

Quarterly Census of Employment and Wages

Florida Department of Highway Safety & Motor Vehicles

Florida Department of Transportation

Federal Highway Administration

Florida Intrastate Highway System

America' s traveler information phone number

Florida Trucking Association

Florida Truck Driving Championship

Global Positioning System

Highway Advisory Radio

Highway Capacity Manual


AASHO

AASHTO

ANOVA

ATA

BTS

CATI

CBD

CBR

CDL

CEDR

CMS

CMV

EFA

ES202

FDHSMV

FDOT

FHWA

FIHS

511

FTA

FTDC

GPS

HAR

HCM









HIS Highway Information System

IRI International Roughness Index

KYTC Kentucky Transportation Cabinet

LOS Level of Service

LSD Fisher' s Least Significance Difference multiple comparison test

LTL Less-than-TruckLoad

NATSO National Association of Truck Stop Operators

NHS National Highway System

NTA National Truckers Association

NYC New York City

ODOT Oregon Department of Transportation

OTA Ontario Trucking Association

P&D Pick-up and Delivery

PCE Passenger Car Equivalent

PSI Present Serviceability Index

PSR Present Serviceability Rating

PTBF Percent-Ti me-B ei ng-F followed

PTSF Percent-Time- Spent-F following

QOS Quality of Service

Q-Q plot Quantile-Quantile plot

RCI Roadway Characteristics Inventory

RMI Relative Maneuverability Index

RV Recreational Vehicle

SIS Strategic Intermodal System

SMC Safety Management Council










Student-Newman-Keuls multiple comparison test

Southwestern Pennsylvania Regional Planning Commission

Sport-Utility Vehicle

Traveler Information System

TruckLoad

Third Party Logistics

Transportation Technical Services, Inc

Text Unit

Text Unit Block

Urban Distribution Centre

University of Hawaii at Manoa

Variable Message Sign

a satellite radio service


S-N-K

SPRPC

SUV

TIS

TL

3PL

TTS

TU

TUB

UDC

UHM

VMS

XM radio









Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial
Fulfillment of the Requirements for the Degree of Doctor of Philosophy

IDENTIFICATION OF PREFERRED PERFORMANCE MEASURES FOR THE
ASSESSMENT OF TRUCK LEVEL OF SERVICE

By

Byungkon Ko

May 2007

Chair: Scott S. Washburn
Major: Civil Engineering

Commercial trucks, the leading transportation mode for freight movement, are vital to our

economy and people's lives. The importance of this mode has become greater as the demand for

just-in-time delivery, lower inventory, electronic commerce, and Less-than-TruckLoad (LTL)

shipping has increased. This study was conducted under the Florida' s Strategic Intermodal

System (SIS) plan for the Florida Department of Transportation (FDOT) in an effort to better

understand the needs of the Florida trucking community by investigating their perceptions of

truck trip quality on various roadway facilities.

The current Highway Capacity Manual (HCM) provides analytical methods to evaluate

performance levels of transportation facilities and estimate Level Of Service (LOS) perceived by

the users. The HCM methodologies yield a single LOS value for all the users in a traffic stream.

However, due to the unique size and operating characteristics of trucks, it is possible that truck

mode users perceive LOS on various roadway facilities based on different criteria from those of

the other mode users. This study focused on identifying the determinants of LOS perceived by

truck mode users and measuring their relative importance based on which truck LOS estimation

methodologies should be developed.









Three focus group sessions (two with truck drivers and the other one with truck company

managers) were held to elicit the factors affecting truck LOS on various transportation facilities

and the relative importance of those factors were evaluated through a follow-on survey study

with a larger audience. A total of 459 truck drivers and 38 truck company managers responded

to the written surveys collected at Florida Truck Driving Championship (FTDC) event or the

postage-paid mail-back surveys distributed at four agricultural inspection stations. The survey

responses were analyzed with various statistical methods such as descriptive statistics,

Exploratory Factor Analysis (EFA), multiple comparisons of the means, non-parametric tests,

and chi-squared tests of independence.

The quality of a truck trip generally was found to depend on three issues: travel safety;

travel time; and physical and psychological driving comfort. Truck drivers showed more

concerns about the driving comfort, while truck company managers were more concerned with

travel time. The travel safety aspect of a truck trip was a major concern for both groups. 'Speed

Variance' and 'Pavement Quality' were the two most important determinants of truck trip quality

on freeways. Truck trip quality on arterials was a function of multiple factors including

'Pavement Quality', 'Turning Maneuvers', 'Speed Variance', and 'Traffic Density'. 'Percent-

Time-Being-Followed' (PTBF), 'Percent-Time-Spent-Following' (PTSF), 'Travel Lane and

Shoulder Widths and their Pavement Quality' were identified as truck LOS determinants on two-

lane highways. Among many factors contributing to the quality of a truck trip, 'Passenger Car

Drivers' Road Etiquette and Knowledge about Truck Driving Characteristics', 'Traffic

Congestion', and 'Frequency and Timing of Construction Activities' were perceived to be in

significant need of improvement regardless of the type of roadway facility. Truck travel

restrictions (e.g., truck lane restrictions) on freeways, inadequate curb radii and poor traffic










signal coordination on arterials, and inappropriate shoulder width and condition and poor night-

time lighting condition on two-lane highways were also considered to be greatly in need of

improvement.

The results of this study provide the FDOT with guidelines and recommendations to

develop truck LOS estimation methodologies to effectively assess how well it is addressing the

needs of freight transportation on the state roadway system and offer transportation service

providers and other stakeholders valuable insights for the prioritization of transportation

improvement proj ects for commercial truck traffic.









CHAPTER 1
INTTRODUCTION

1.1 Background

Transportation services for freight are vital to our economy and people's daily lives.

Various kinds of freight are relocated daily in and out of Florida through several transportation

modes such as truck, train, ship, and plane. Among all the modes, trucks moved 76.3 percent of

freight value, 79.4 percent of tonnage, and 66.9 percent of ton-miles of the total freight

originating in Florida in 2002, according to the Commodity Flow Survey (CFS) (Bureau of

Transportation Statistics, 2004). This makes the truck mode the leading mode for freight

movement in the U.S. Truck traffic is also expected to grow significantly throughout the State of

Florida over the next couple of decades, along with increasing population, as noted in the Freight

Analysis Framework (FAF) (U. S. Department of Transportation and Federal Highway

Administration, 2002). In addition, the increased demands for just-in-time delivery, low

inventory, electronic commerce (e-commerce), Less-than-TruckLoad (LTL) shipping, and more

distributed manufacturing, make it necessary for transportation service providers and other

stakeholders to look for ways to better accommodate truck traffic on existing roadway systems.

Florida's Strategic Intermodal System (SIS) plan was established by the Florida

Department of Transportation (FDOT) in 2003 to support transportation facilities that are

necessary for Florida' s rapidly growing and ever changing economy (FDOT, 2003). The main

goal of the SIS program is to provide safe, efficient, and convenient transportation services for

all types of transportation users on the most critical transportation facilities in Florida. The SIS

facilities were selected based on national or industry standards for measures of transportation and

economic activity. The SIS includes three different types of facilities; hubs, corridors, and

intermodal connectors. Hubs are ports and terminals, corridors are highways, rail lines, or









waterways that connect maj or markets, and connectors are highways, rail lines, or waterways

that connect hubs and corridors. As one of its first research proj ects under the SIS program, this

study has been conducted for FDOT to better understand the needs of the Florida trucking

community by investigating its perceptions and opinions about transportation services for trucks

on various state roadway facilities.

1.2 Problem Statement

The Highway Capacity Manual (HCM) (Transportation Research Board, 2000) provides

analytical methods to estimate capacity and key performance measures for a wide variety of

roadway facilities. The HCM also provides methods for translating performance measure values

into a Level Of Service (LOS) value. Level Of Service (LOS) is a qualitative measure used to

describe general operating conditions within a traffic stream. The HCM uses a scale of 'A' to

'F' for LOS, with 'A' corresponding to excellent operating conditions and 'F' corresponding to

extremely poor operating conditions. These LOS values are typically based on performance

measures such as speed and travel time, freedom to maneuver, traffic interruptions, and comfort

and convenience, and are intended to reflect drivers' perception of those conditions. The

selected performance measures, upon which LOS is based, referred to in the HCM as service

measures, can vary from one roadway facility to another. The methods of the HCM have been

widely adopted by the transportation community as the primary means of measuring system

performance. They are often used as valuable tools for most transportation agencies to monitor

or improve the performance levels of existing transportation facilities, or plan for future

transportation facilities.

The current HCM utilizes engineering-based measures such as density, delay, and average

travel speed in evaluating the performance level of transportation facilities. These measures are

conceptually reasonable and easy to apply, but are not necessarily consistent with the measures









that the actual roadway users base their perception of the facility performance level on.

Furthermore, for the roadway facilities in the HCM (such as arterial, highways, and freeways),

the HCM methods result in a single LOS value for the entire traffic stream; that is, they do not

distinguish between different modes within the same traffic stream. The methods are generally

designed to establish LOS for the passenger vehicle mode. However, commercial trucks

(hereafter referred to as just 'trucks') are unique in their size and operating characteristics among

various vehicle types in traffic streams. Trucks require more space and time to maneuver due to

their difference in size, weight, off-tracking, acceleration, and braking. So they are often used as

design vehicles for roadway facilities, and sometimes subj ect to various restrictions such as lane,

route, speed, or time-of-day. In addition, most truck drivers operate their trucks primarily for

business purposes and spend a significant amount of time driving, while the trip purposes of

other vehicle users vary and they usually drive less frequently.

Due to these special characteristics of truck operations on transportation facilities, it is

possible that truck mode users perceive LOS on various roadway facilities based on different

criteria from those of the other mode users. However, the current HCM (Transportation

Research Board, 2000) only accounts for the mode of trucks in the traffic stream through the

heavy vehicle adjustment factor (f, ). This adjustment factor is based upon the percentage of

heavy vehicles (e.g., trucks, buses, or recreational vehicles) in the traffic stream and their

passenger car equivalents (PCEs). A PCE is the number of passenger cars expected to have

same effect on a traffic stream as a single heavy vehicle under specific roadway and traffic

conditions. The PCE is used to convert a traffic stream flow rate with some portion of heavy

vehicles into an equivalent one with passenger cars only. So, although the current HCM

procedures account for the effects of trucks in the traffic stream, the overall methodology does










not provide any exclusive LOS evaluation criteria or procedures to estimate how well a

transportation facility accommodates truck traffic in a traffic stream.

1.3 Study Objectives and Tasks

The primary obj ective of this study was to determine the roadway, traffic, and/or control

factors important for Florida' s trucking community and estimate the relative significance of each

factor on overall quality of truck operation on the SIS facilities. Based on information obtained

about the perceptions of truck mode users, this study provides the FDOT with guidelines and

recommendations to develop actual LOS estimation methodologies to effectively assess how

well it is addressing the needs of freight transportation on the state roadway system. These

efforts provide transportation service providers, researchers, and transportation agencies with

valuable insights as to what should be prioritized to improve truck operations on various

transportation facilities.

To achieve this obj ective, it was essential to obtain input from both truck drivers and truck

company managers, as these two groups represent the maj or stakeholders with regard to truck

operations. For this study, data collection was confined to focus group and written survey

methods. The maj or tasks performed to complete this study are described below and presented

in their chronological order:

Task 1: Literature review. A literature review was performed to facilitate and enhance

this study with regard to the following five areas: 1) trck LOS studies; 2) user-perception based

LOS studies; 3) trucking community focus group and/or survey studies, 4) Florida trucking

community focus group participant recruitment sources, and 5) trucking community survey

methods.

Task 2: Preparation for focus group sessions. In advance of the focus group meetings,

the methodology for conducting the focus group studies was determined. This includes









clarification of the purpose and scope of the interviews, participant selection guidelines,

participant recruitment plans, moderator selection, development of participant background

surveys, moderator's guide regarding focus group questionnaires and instructions, focus group

discussion recording, and methods of analysis.

Task 3: Focus group sessions of truck drivers and/or truck company managers.

Several focus group meetings were held with selected participants to explore the perceptions and

opinions of the trucking community on the factors important to truck operations on various

transportation facilities.

Task 4: Summary of focus group findings. The focus group discussions were

summarized to identify the factors affecting quality of a truck trip on each facility and by

different user groups (truck drivers and truck company managers). The summary results were

then used as inputs to a follow-on survey.

Task 5: Survey development. Survey studies were intended to reconfirm the focus group

Endings with a broader audience and to measure the relative importance of each factor

quantitatively. Two different survey forms were prepared; one for truck drivers and the other for

truck company managers. Both forms contain questions about participants' personal and

working backgrounds and their perceptions about the relative importance of each factor

identified in the previous focus group studies.

Task 6: Survey data collection. Truck driver surveys and truck company manager

surveys were distributed at multiple locations with the assistance of the FDOT and the Florida

Trucking Association (FTA).

Task 7: Analysis of survey data. Collected surveys were analyzed statistically to

determine the relative importance of each factor on truck trip quality and/or overall trucking










business. Relationships between the participants' backgrounds and their perceptions were also

explored.

Task 8: Study results and recommendations. The factors influencing truck trip quality

and their relative importance were determined based on the focus group and survey results.

Potential performance measures to estimate truck LOS were recommended by each

transportation facility type.

1.4 Organization of the Dissertation

The remainder of this dissertation is as follows. Chapter 2 provides a summary of relevant

literature. Chapter 3 describes the study methods and detailed steps taken to complete this study.

The study results are presented in Chapters 4 and 5. Chapter 4 discusses focus group study

findings and Chapter 5 discusses the results of statistical analyses on the survey data. In chapter

6, conclusions are presented and recommendations are made with regard to roadway facility

improvement priorities for truck LOS considerations. Potential truck LOS service measures are

also presented.









CHAPTER 2
LITERATURE REVIEW

This chapter describes previous research efforts by other researchers related to this study,

conceptually or methodologically. It provides descriptions of the research methodologies and

results of those studies, and a summary of the findings. First, the studies regarding truck LOS

development are presented. They contain some implications on potential truck LOS service

measures. Other research efforts to identify and test potential LOS service measures on a

specific roadway facility are described in the next section of this chapter. They provide some

insights about how to elicit the perception of travelers and convert the information to develop

user-perceived LOS. The final section reviews previous focus group and/or survey studies of the

trucking community. Potential sources of focus group recruitment and characteristics of various

survey methods were separately summarized to help develop the methodologies suitable for the

purpose and scope of this study.

2.1 Truck Level of Service

A study by Washburn (2002) explored and demonstrated the development of a potential

method to assess level of service specific to the truck mode. The current LOS performance

measure for a freeway segment (density) applies to the traffic stream as a whole, regardless of

vehicle types, although they typically have very different physical and operating characteristics

from passenger cars in the traffic stream. To assess the level of service of heavy trucks on a

freeway segment, a 'relative density' concept was proposed. Relative density for trucks could be

obtained by dividing density for the traffic stream by a Relative Maneuverability Index (RMI),

which is a function of the ratio of percentage of free-flow speed of trucks to percentage of free-

flow speed of passenger cars. Under low density, ideal conditions, percentage of free-flow speed

for both trucks and cars should be at or near 100%, yielding the same LOS for both modes (e.g.,










'A'). Likewise, under high density, the percentage of free-flow speed for both trucks and cars

would both be very low, producing the same LOS for both modes (e.g., 'F'). At densities in

between, the percentage of free-flow speed for trucks will probably be less that that for cars,

resulting in higher relative density values for trucks, which can be referenced to the current HCM

density thresholds for determining LOS. Other possible measures for determining LOS for

trucks have also been introduced in the document; acceleration noise, passing opportunities,

percent-time spent-following, and heavy vehicle factor.

A study by Hostovsky and Hall (2004) focused on the perceptions of tractor-trailer drivers

on the factors affecting quality of service on freeways. A focus group meeting was conducted

for this study with 7 Road Knight Team members at the annual convention of the Ontario

Trucking Association (OTA) in November, 2001. The participants were asked what makes for a

good or bad trip for themselves to drive trucks on a freeway. The discussion was then

transcribed to be analyzed through NUD.IST (Non-numerical Unstructured Data Indexing

Searching and Theorizing), an industry standard qualitative data analysis software program using

five criteria (intensity, relevance, frequency, universality, and emphasis). The OTA, Canadian

Trucking Association (CTA), and American Trucking Association (ATA) showed an agreement

with the study's conclusions. The study results were organized in this paper by the number of

Text Unit Blocks (TUB), which represent the number of times a certain theme was discussed by

the participants. With respect to freeway conditions (TUB = 34), the factors identified were road

surface (pavement condition, snow removal, road debris), lane restrictions, signage, and lane

width and markings.

As far as traffic conditions (TUB = 22) are concerned, congestion, steady flow, and

maneuverability were discussed. Attitudes toward other vehicles (TUB = 27) were another big









issue mentioned in the meeting. Courteous interplay by automobile drivers was found to be

important for them to have a good trip. In terms of safety (TUB = 3), road etiquette, weather,

and rubbernecking were considered to influence their trip quality. The participants also

mentioned the hazard of aggressive driving and road rage (TUB = 23) and regionally different

perceptions of trip quality (TUB = 23) with respect to such various factors as level of congestion,

courteous interplay by other drivers, driving skills of other drivers around them in snow

condition, and low speed limits. With all the findings mentioned above, the authors indicated

that what really matters to truck drivers in terms of traffic condition is traffic flow, not traffic

density, which is a service measure used in the current HCM. In this respect, steadiness of traffic

flow and an ability to drive at a comfortable operating range of highway speeds without much

braking or accelerating were considered to be important for truck drivers' perception of the

quality of service.

A study by Hall, et al. (2004) developed a method to evaluate the access routes for large

trucks between intermodal or other truck-traffic-generating sites and the National Highway

System (NHS) and used it to prioritize and program the truck access routes for improvement.

The study began with identifying clusters of truck-traffic generating facilities based on total

trucks per day, distance to NHS, and recommendations on the sites with truck access problems

by transportation planners from highway district and area development district offices. Then

telephone surveys were conducted with the operators/managers of the selected sites to identify

problem routes to be evaluated in this study. Each route was evaluated with respect to three

types of features: subjective, point, and continuous. Using the Kentucky Transportation Cabinet

(KYTC) statewide Highway Information System (HIS) database and some field observations, the

following characteristics of each route were collected: 1) point features: curve off-tracking,









maximum safe speed on horizontal curves, stopping sight distance, turning radii; 2) continuous

features: grade, lane width, shoulder, route LOS. The point and continuous elements on each

route were ranked as "preferred", "adequate", and "less than adequate" based on the

recommendations in AASHTO (American Association of State Highway and Transportation

Officials)' s "A Policy of Geometric Design of Highways and Streets" and "Roadway Design

Guide." The features subjectively evaluated by the researchers include clear zone, pavement

condition, accident history, parking, pedestrian traffic, land use conflicts, dust/noise issues, and

so on. The rankings of point and continuous elements were converted to a relative urgency

rating by assigning a relative weight with respect to truck volume and section length. Problem

truck-points and problem truck-miles were calculated based on these rankings to prioritize the

problem routes. They were adjusted by predominant subj ective features, where appropriate.

Finally, with all the evaluation data of each problem route, the researchers inspected the routes

and graded them on a subj ective scale of 1 to 10 (10 represents good access for trucks).

All the three studies presented in this section focused on developing methods to evaluate

operating conditions on a specific roadway type for truck traffic. Washburn (2002) devised the

RMI concept to evaluate truck LOS on freeways, based on a hypothetical reasoning that

maneuverability of trucks is inferior to that of passenger cars because of unique physical and

operating characteristics of trucks. A study by Hostovsky and Hall (2004) found that traffic flow

is more important than traffic density to truck drivers' perception of trip quality on freeways.

The two above mentioned studies imply that traffic density, the current HCM service measure

for freeway facility, may not reflect the perception of truck drivers adequately. Steady traffic

flow and maneuverability may be more important to truck drivers than traffic density,

considering the large size, heavy weight, low acceleration and deceleration capability of trucks.










Hall, et al. (2004) developed a methodology for evaluating large truck access routes between

NHS and truck-traffic generating facilities to prioritize and program the routes for improvement.

Overall quality of each route for truckk access was determined by many various factors. They

included geometric adequacy measures, pavement condition, clear zone, accident history, traffic

LOS, and other subj ective measures such as parking, pedestrian traffic, land use conflicts,

dust/noise issues, etc. As this study investigated so many different characteristics of each route,

it may not be appropriate to identify one or two performance measures to effectively estimate

quality of an access route for trucks.

2.2 User-Perceived Level of Service

A study by Hall, et al. (2001) conducted two focus groups (one with Hyve members, and the

other with seven members) to explore what freeway users perceive as important to level of

service on freeways. Focus group participants were chosen by a snowball sample selection

process to identify commuters going from the city of Toronto, Ontario, Canada to McMaster

University (the research center) in Hamilton, Ontario Canada. The participants were all

university faculty members in various departments, and were selected to ensure that all the

participants traveled the same stretch of freeway so that all knew about the situations that were

being discussed and all had had relatively similar experiences. Both groups were moderated by

the same member of the research team to ensure consistency in technique across groups. The

following general questions were asked in the discussions;

* Tell me about the usual freeway route you take when you are commuting.

* Have your perceptions of the drive changed over time?

* I want you to talk about both the ideal or best trip you've ever made and also the worst trip
ever.









The factors important to perceptions of trip quality were listed with respect to the number

of Text Units (TUs) in which each theme was mentioned in the two discussions, as follows;

* Travel time (TUs = 103 (6%))
* Density/maneuverability (TUs = 86 (5%))
* Road safety (TUs = 81 (5%))
* Commuter information and communication (TUs = 65 (4%))
* Civility (TUs = 38 (2%))
* Photo radar (TUs = 31 (2%))
* Weather (TUs = 23 (1%))

The participants indicated travel time as the first thing to describe the quality of particular

trips. Having time constraints on their arrival from a trip was thought to greatly increase the

stress involved in the trip. Time spent commuting was also considered to be lost time.

Maneuverability also came up in the discussions in light of travel time, accident avoidance,

adequate gaps between cars, weaving in and out of traffic, and changing lanes. Another

important issue for participants was safety. They were concerned about accidents not only in

terms of congestion but also because of the risk to their personal safety. Materials from trucks

and other debris on the road were concerns for many participants, and the effects of sport-utility

vehicles (SUVs) and minivans on other drivers' visibility were also mentioned in the safety

perspectives. Having adequate information about what was happening to traffic while they were

on the road was also important to the participants. This issue was discussed along with the

possibility for them to avoid traffic congestion by finding alternative routes. Some other factors

were mentioned occasionally in the focus groups as well; driver civility, the use of photo radar,

and weather and its effect on driving conditions. Passengers' perceptions also were different

from drivers' with respect to travel time, mainly due to their ability to undertake activities other

than driving. The participants indicated that travelers on the bus would have different

perspectives; they usually do not notice the traffic conditions when they are in the bus. The









authors also indicated that the participants do not think of freeways in terms of distinct segments

classified by the HCM (basic segments, ramp-freeway junctions, and weaving segments),

proposing further studies about LOS breakpoints.

The factors that affect traveler-perceived quality of service on rural freeways were

explored using in-field surveys of motorists traveling on rural freeways (Washburn, et al., 2004).

A total of 233 responses from a good mix of respondents were collected at several different

locations along I-75 and the Turnpike in Florida. The researchers decided to perform an 'in-

field' survey data collection effort, as opposed to a mail-back survey or something similar, as

this provided the advantage of obtaining input while the specific characteristics of the traveler' s

trip were still fresh in their mind. The surveys at the rest stops, which offered respondents a $2

food discount voucher, also showed a good overall productivity (about six respondents per hour).

The importance of 16 traffic and roadway factors was ranked by respondents on a seven-

point ordinal scale (1 being not at all important, and 7 being extremely important). The six top

ranked factors based on mean scores and/or percent time in top three are as follows:

* Ability to consistently maintain your desired travel speed (mean = 6.09, % time in top
three = 64.3%)

* Ability to change lanes and pass other vehicles easily (mean = 5.79, % time in top three =
33.3%)

* Smooth and quiet road surface condition (mean = 5.68, % time in top three = 20.3%)

* Ability to travel at a speed no less than the posted speed limit (mean = 5.58, % time in top
three = 33.0%)

* Other drivers' etiquette/courtesy (mean = 5.38, % time in top three = 22.1%)

* Infrequent construction zones (mean = 5.37, % time in top three = 23.4%)

A probit modeling technique was used to discover any possible relationships between

personal characteristics and current trip information of the respondents, and their opinions about









the top four ranked factors (from 1 to 4 above). For the first factor, the more highly educated an

individual, the more likely they were to rank this factor highly. The respondents that were

driving a tractor-trailer, or with higher estimated average speeds, were also more likely to rank

this factor highly. For the second factor, the respondents that had higher education or with

higher estimated average speeds, were more likely to rank this factor highly. Travelers that

indicated they were experiencing mostly very dense or dense traffic flow conditions on their trip

were less likely to rank this factor highly. For the third factor, travelers with higher income were

more likely to rank this factor highly. Travelers who indicated their trip purpose was 'other',

that is, neither business nor leisure, were less likely to rank this factor highly. Travelers that

were on a business trip and very familiar with the route they were traveling were also less likely

to rank this factor highly. For the fourth factor, older people were more likely to rank this factor

highly. On the other hand, the respondents that were strictly drivers on the trip or driving a large

auto (pickup truck, SUV, minivan, or full-size van) were less likely to rank this factor highly.

The authors indicated that in addition to density, there are some factors that are just as

important to travelers, such as speed variance and percent of free-flow speed. Some non-traffic

performance measures were also found to be important through the study, such as pavement

quality, and driver etiquette.

A web-based survey was conducted at the University of Hawaii at Manoa (UHM) in 2005

to see how road users evaluate signalized intersection LOS (Zhang and Prevedouros, 2005). E-

mail messages with group-specific survey hyperlinks were sent to selected samples asking them

to fill out the motorist survey on-line. Six groups comprise the sample; random UHM students,

faculty, and staff, Hawaii engineering and transportation professionals, drivers from other groups

in Hawaii, and drivers from the mainland U.S. The response rate was 12.2%, 16.0%, 18.9% for









UJHM students, faculty, and staff respectively, yielding 2,017 usable surveys. The survey

questions focused on factors important to drivers at signalized intersections, driver opinions on

protected left-tumn signals for various sizes of intersections, and trade-offs between perceived

safety risk and delay. Analysis of variance (ANOVA) was applied to dependent variables with

ratio- or interval-level data to assess if there were significant differences among the independent

groups. The influence of several independent variables such as gender, age, and driving

experience on the dependent variables were investigated simultaneously through ANOVA

(Analysis of Variance).

The ten factors important to drivers at signalized intersections were evaluated with a five-

point ordinal scale (1 being not important, and 5 being extremely important). The factors are

listed as follows in order of decreasing importance.

1. Traffic signal responsiveness
2 Ability to go through the intersection within one cycle of light changes
3 Availability of left tumn only lanes and protected left turn signal for vehicles turning left
4 Pavement markings for separating and guiding traffic
5 Availability of a protected left tumn signal for vehicles turning left
6 Availability of left turn only lanes for vehicles turning left
7 Pavement quality
8 Waiting time
9 Heavy vehicles such as trucks and buses that are waiting ahead
10 Availability of right-turn only lanes for vehicles turning right

The respondents were also asked to rate the difficulty in making a left turn without a

protected left tumn signal and their preference for a protected left turn signal, on a scale form 1

(not difficult or not preferred) to 5 (extremely difficult or extremely preferred). The difficulty

increases with intersection size, and female drivers perceived more difficulty than male drivers.

The preference also increases with intersection size, and female drivers prefer protected left tumn

signals more than male drivers. As high as 91% of the respondents stated that they much or









extremely prefer a protected left turn signal at an intersection where left turn vehicles have to

cross three lanes of opposing through traffic.

Safety was stated to be three to six times more important than delay, depending on the type

of conflict; drivers turning left are more concerned than drivers going through, and pedestrians

are more concerned than drivers turning left. Female drivers, non-risk-prone drivers, and

pedestrians valued safety more than delay to a significantly greater degree. Drivers and

pedestrians would be willing to wait a longer time at signalized intersections in exchange for

protected left turn signals. The average additional delay to exchange for protected left turn

signal is 20.6 sec, 25.4 sec, and 27.2 sec for drivers going through, drivers turning left, and

pedestrians respectively.

Overall findings suggest that the current measure, delay, should be supplemented by a

number of quantifiable attributes of signalized intersections for determining a LOS that

represents road user perceptions.

Some potential performance measures for LOS on rural freeways were evaluated with

microscopic traffic simulation (Kim, et al., 2003). Although density-based LOS is thought to be

well suited to the assessment of urban freeways, some have questioned whether density is also a

proper indicator of the quality of service on rural freeways because drivers may think more in

terms of psychological or emotional comfort for rural freeways, which generally serve long,

high-speed trips and rarely experience more than moderate congestion levels. Acceleration

noise, number and duration of cruise control applications, and percent time spent following

(PTSF) were examined as some possible means of determining Level of Service (LOS) of rural

freeway sections as they have at least an intuitive relationship to the concept of driver comfort.

The three measures were estimated for a hypothetical section of rural freeway. Acceleration










noise is a measure of the physical turbulence in the traffic stream. The instantaneous

acceleration of each vehicle at each second is computed from the speed differential with respect

to the previous second, and the standard deviation of acceleration for each vehicle is computed

over the 6,000 feet segment. A post-processing routine was written to analyze the simulation

results and infer the application and release of cruise control. Cruise control was applied to a

vehicle when it had been traveling at its desired speed for three or more seconds. Cruise control

was released when the vehicle began to decelerate for any reason. For PTSF, a vehicle is

considered to be following its leader if the relationship between its position and speed with

respect to the leader places it within the car-following influence zone. A nonlinear relationship

between acceleration noise and traffic volume shows that acceleration noise increases more

rapidly in the lower volume range and levels off as volume increases. It is also observed that the

nonlinear effect is most pronounced on single-lane freeways and diminishes as the number of

lanes increases. The nonlinearity for proportion of time with cruise control applied was only

discernible to any degree in the case of the single-lane freeway. The nonlinearity for number of

cruise control applications was too pronounced to be useful. The nonlinearity for PTSF was

more pronounced than that for acceleration noise, and there was no discernible difference

between two and three lane freeways. Based on the investigation of the nonlinear relationship

shapes between level of volumes and three candidate measures by number of lanes, the authors

concluded that a nonlinear relationship between acceleration noise and traffic volume on rural

freeways could be used directly as the basis for a new set of LOS criteria for rural freeways,

given further studies focusing on driver opinions, behavior, and field measurement to support

this finding. The other two measures also have conceptual appeal, but used individually would

not be suitable as the basis for determining LOS on a rural freeway.









The above described studies concerning the exploration of user-perceived level of service

performance measures identified some interesting findings. The study by Hall, et al. (2001)

found from two focus group meetings that total travel time is the most important LOS

determinant for travelers on a freeway. It was noted that passengers did not consider the travel

time as important as drivers did because they can do something else other than driving. It would

be worth investigating how freeway drivers' perceptions on importance of total travel time can

be influenced by their trip purposes. The study by Washburn, et al. (2004) found from surveys

of rural freeway travelers that "consistently maintaining desired travel speed" is more important

than "ability to change lanes and pass other vehicles easily" or "traveling at or not less than the

posted speed limit." This implied that the cruise control factor is more important than the

density factor or percent of free-flow speed factor. Other important factors included pavement

condition, other drivers' etiquette/courtesy, and infrequent construction zones. A web-based

survey study by Zhang and Prevedouros (2005) found that the current service measure of delay

for signalized intersections is less important than a number of other factors such as "traffic signal

responsiveness", "ability to go through the intersection within one cycle of light changes", etc. It

may be possible that drivers are more sensitive to stop and go conditions than actual delay

experienced at signalized intersections. Kim, et al. (2003) identified acceleration noise as a

potential service measure for rural freeways due to its desirable properties of relating to drivers'

sense of safety and comfort and its non-linear relationship with traffic volume. Many previous

studies of this type verified that some current service measures do not reflect the perception of

the users adequately.

2.3 Trucking Community Focus Groups and Surveys

With increasing importance on urban freight mobility, Morris, et al. (1998) conducted a

study on cost, time, and barriers related to moving freight into New York City's Central Business









District (CBD). The study consisted of 13 focus groups with different industry-sector senior

logistics executives and freight mobility interviews with logistics, transportation, or distribution

managers. The research team cooperated with the Center for Logistics and Transportation

Executive Committee in the development of a focus group moderator' s guide, recruitment of

focus group participants, pretest of the guide, and the guidance of the freight mobility interview.

Each focus group was scheduled at participants' convenience, allowing the use of

speakerphones, and included 2-4 participants to explore the issues in depth within a 2-hour time

frame. Participants also received the focus group guide and informational material before the

meetings so that they would have time to think about the issues in advance. At the meetings, a

flip chart listing seminal points for focus group questions and probes, and a large CBD area map

were displayed to reinforce attendees' attention to topics. Transportation barriers listed in the

order of greatest frequency of mention in all the meetings were; congestion, inadequate docking

space, curb space for commercial vehicles, security, and excessive ticketing of high-profile

companies. A freight mobility interview followed with logistics, distribution, or transportation

managers, who were recruited with the help of the previous focus group participants or from the

membership lists of Council for Logistics Management and the Center for Logistics and

Transportation. An on-site interview was planned initially, but the response was generally

negative. So phone interviews were requested to be scheduled after sending letters containing a

freight mobility interview, the study goals and purpose, and focus group findings. Follow-up

calls were made 2-4 weeks after the letters were sent. But due to the lower than expected

response rate, another attempt was made with follow-up calls made I week after sending letters,

resulting in 51 completed interviews. Data on costs, time, distance, product types, and major

barriers in the movement of freight into Manhattan's CBD by shippers and carriers were









collected. Maj or barriers to freight mobility identified through the interviews were; widespread

congestion, security, physical constraints, and institutional barriers.

In a study by Veras, et al. (2005), various types of efforts such as focus group studies, in-

depth interviews, and internet surveys were made to obtain the perceptions on challenges and the

potential of off-peak deliveries to congested areas. The targeted groups for this study included

private stake holders; shippers, receivers, third party logistics (3PLs), trucking companies, and

warehouses. Off-peak deliveries to the New York City (NYC) metropolitan region were

proposed to avoid traffic congestion and lack of parking spots hampering daytime commercial

vehicle deliveries. The strategy may reduce the price accompanied by congestion and illegal

parking, pollution, and frustration to the public, but it may pose additional costs to shippers,

receivers, and carriers mainly due to workers necessary for night shifting. It may also involve

regulatory and legal impediment. On January 20, 2004, two focus groups with truck dispatchers

were conducted in NYC as part of the Evaluation Study of the Port Authority of New York and

New Jersey's Value Pricing Initiative. The focus group findings indicated that both shipper costs

and receiver costs would increase due to the need of night time workers, regardless of feasible

toll discounts, or compensation due to traveling hours. In-depth interviews with 17 stakeholders

of various types (trucking companies, shippers, receivers, lobbyists, trucking-warehouse

combination companies, and shipping-trucking-warehouse combination companies) were

performed to explore the issues further. Companies with trucking operations prefer to make

deliveries during off-peak hours due to congestion and parking problems, but they are often

discouraged to do it because of the recruitment of night-shift workers, and security of drivers,

receivers, and products. The shippers were natural on the subject of off-peak deliveries. They

do not care when their products are delivered only if the products get to the destination on time.









The two receivers operating restaurants stated that off-peak deliveries would help reduce severe

parking problems, but fresh food is typically not delivered during off-peak hours. Thirty-three

internet surveys were gathered from private stakeholders (shippers, receivers, 3PLs, trucking

companies, warehouses) in several states. Seventy-Hyve percent of warehouses and 70% of

shippers, 3PLs, and trucking companies were performing off-peak deliveries. Many others at

least have considered using this alternative. None of the shippers was currently performing off-

peak deliveries, but maj ority of them indicated that they could do them if they are provided with

some incentives. Reasons given by stakeholders for performing off-peak deliveries included

faster deliveries, faster turn-around times, and lower costs. Reasons given for not performing

off-peak deliveries included businesses not being open at those times, customers not accepting

off-peak deliveries, and employee costs. With respect to incentives to implement off-peak

deliveries, most stakeholders were interested in all of the tax incentives and subsidies. A

significant number of trucking companies and 3PLs indicated that they would be responsive to a

request from many receivers to do off-peak deliveries. The author also brought up carrier-

centered policies, receivers as key stumbling blocks, Einancial incentives, and targeted maj or

traffic generators.

Two studies focused on developing an effective methodology to survey the freight

community (i.e., shippers and carriers) in Oregon state by comparing various survey data

collection methods (Lawson and Riis, 2001 and Lawson, et al., 2002). The researchers did an

extensive literature review on the previous trucking community survey studies and experimented

with several data collection methods to Eind out the most effective method to survey shippers and

carriers. Traditional structured interviews conducted in person or by telephone have a high

response rate from purposeful sampling procedure. These surveys can focus on broad issues and









are useful in obtaining detailed information about the specific topic, but may not accurately

reflect the large population. Computer-Aided Telephone Interviews use a script of

predetermined questions with random sampling techniques. This method allows for a large

sample size, and is less costly, but, reduces the opportunity to fully explore a certain aspect of

any given topic. The response rate from the method is lower than traditional structured

interviews, but higher than mail out surveys. Response rates for previous written surveys ranged

from 8 to 24 percent, but can be improved with telephone follow up. Written surveys are the

least costly, good for broad sampling, but it typically produces low response rates and does not

ensure that the right person will complete the survey.

Nine types of survey methods were tested as a pilot study to search for the best one to use

for surveying the freight community.

* Type 1 mail out/mail back questionnaire with follow up reminders (ES202 database,
response rate of 15%)

* Type 2 mail out/mail back questionnaire and a map, with follow up reminders (ES202
database, response rate of 11%)

* Type 3 postcard invitation to participate, for positive respondents, mail out/mail back
questionnaire with follow up reminders (ES202 database, response rate of 6%)

* Type 4 postcard invitation to participate, for positive respondents, mail out/mail back
questionnaire and a map, with follow up reminders (ES202 database, response rate of 4%)

* Type 5 telephone invitation to participate, for positive respondents, mail out/mail back
questionnaire with follow up reminders (ES202 database, response rate of 19%)

* Type 6 telephone survey (ES202 database, response rate of 60%)

* Type 7 telephone survey (Oregon DOT Motor Carrier Transportation Division truck
registration database, response rate of 64%)

* Type 8 mail out/mail back questionnaire with telephone follow up reminders (Oregon
DOT Motor Carrier Transportation Division truck registration database, response rate of
3 3%)










* Type 9 mail out/mail back questionnaire with follow up reminders (Oregon DOT Driver
and Motor Vehicle Services Division Commercial Driver' s License (CDL) Database,
response rate of 12%)

In every case, the respondents were given information on the availability of the survey via

e-mail or the website, or both. However, little evidence was found that the option to use the web

site or to communicate via e-mail was of interest to the survey participants. In addition to the

infrastructure problems, various other issues such as regulations, taxation, and enforcement were

covered in the survey. The survey response rates from telephone surveys were highest among all

the methods. The use of postcard invitation before a mail out survey resulted in very low

response rates. Telephone invitation with a mail out survey yielded a 19% response rate, while a

mail out survey with follow up phone calls produced a 33% response rate. Freight firms said that

a written survey is the most preferred method, but proved to yield a lower response rate than

phone surveys.

Prior to carrying out the Oregon State-wide Freight Shipper and Carrier Survey, 34

previous freight surveys already implemented were reviewed by Loudon (2000). Considerations

for conducting surveys of the freight movement community, and lessons learned from previous

freight research in Oregon are summarized. The options to be determined according to the

purpose of the surveys are:

* One company questionnaire versus separate company questionnaires for shippers and
carriers.

* Survey transportation managers only versus survey managers and drivers.

* Direct driver contact versus distribution to drivers by managers.

* Focus on truck movements only versus focus on all modes of freight.

* Explore problems through open-ended questions versus structured list of possible
problems.

* Ask the respondent to rank problems versus list problems only.










* Ask about problem and practices for inbound and outbound freight movements or
outbound only.

* Use a written (self-completing) questionnaire versus interviewing.

* Use cold mailing of written questionnaire versus pre-arranged participation.

Lessons learned from previous freight research in Oregon are:

* Freight movement logistics are complex. So, special considerations for flexibility should
be made to design each issue (shipment size, timing of shipments, etc).

* Methods of shipping freight are changing rapidly. For example, more inventories are
maintained in trucks on the highway rather than in manufacturing plants, warehouses, or
stores. The survey questionnaires must allow sufficient flexibility to pick up on the current
changes.

* It is not easy to get participation from private businesses.

* Limit the number of issues covered in the survey so that sufficient depth of understanding
on those issues can be achieved.

* Survey the right person. A transportation manager will generally know the most about
decisions about how goods are shipped and received, while an accountant of the company
will be able to say something about impact of congestion on costs and profitability.

* Explore the reasons why transportation bottlenecks are a problem and how they affect the
business

* Nonrecurring congestion is a significant problem, and access to the maj or highways was as
important as level of service on the maj or highways.

The feasibility and an estimation of the potential for using Urban Distribution Centres

(UDCs) in the city of Dublin were studied using two surveys (Finnegan, et al., 2004 and

Finnegan, et al., 2005). UDCs can fulfill a number of functions including warehousing,

transshipment, consolidation of loads, efficient dispatch and collection of goods. Several

associations or groups including Dublin City Centre Business Association have participated

consultation meeting to indicate freight transportation issues in Dublin City Centre prior to the

surveys. The fist survey was deployed on a weeklong survey of deliveries to Trinity College

Dublin. The intercept survey method was used at the gate of the campus, resulting in an 82%










response rate (299 participated out of 365 requested). The second survey was distributed to

several trade association members, capturing 906 individual deliveries with a 10% response rate.

Many aspects of freight movement in the Dublin City Centre (time of delivery arrival and

departure, types of goods, how packaged, quantity of packages, types of delivery vehicles, who

supplied the goods, location of supplier, loading and unloading places) were yielded by the

surveys to discover the use and location of a UDC. Some conclusions were made at this point;

Food related deliveries are expected to be improved by the use of a UDC in Dublin, a UDC

could assists in the process of backhauling, the operation of a delivery platform in the city centre

was suggested. But, no definite recommendations for the use, location, and impact of UDC were

presented.

The Southwestern Pennsylvania Regional Planning Commission (SPRPC) conducted a

broad mail survey of freight transportation users and service providers about various issues in

freight transportation (SPRPC, 1996). The surveys were drafted under the guidance of the

Freight Forum and distributed to the 700 freight service providers from SPRPC's Freight

Transportation Database, and 800 area manufacturers listed in the Southwestern Pennsylvania

Regional Development Council's Computer Assisted Product Search database (CAPS). The

postage-paid surveys were sent to them by mail. The original response rate was 4%, but the final

response rate was 9% after follow up phone call efforts. With various types of freight service

providers, and manufacturers answering the survey, 22% of manufacturers indicated that they

utilize intermodal transportation, and 42% of service providers said that they do. Specific

impediments to the respondents with locations and comments were also identified with their

percent frequencies; traffic congestion (46%), rush hour deliveries (32%), roadway turning

radius (25.5%), turning at traffic lights (24%), poor bridge or tunnel clearance (18%), curfews on










high or wide roads (17%), merge lanes (16.5%), at-grade railroad crossings (14%), lack of

receiving areas at malls (13%), lack of trailer drop-off and pick-up (12%), poor truck access to

airport air cargo area (1 1%), lack of adequate warehousing (8%), delays or other problems at

customs (5%), poor truck access to river terminals (4.5%), poor truck access to intermodal

facilities (4.5%), lack of rail to highway access (3.5%), poor signage (3%), highway interference

with railroad (2%). Women's compensation and other labor costs, regulations, and taxation were

also introduced to be pressing issues affecting the freight transportation industry.

In 1998, Regan and Golob (1999) studied the perceptions of motor carriers about traffic

congestion, congestion-relief policies, usefulness of information technologies, and efficiency of

intermodal facilities in California through computer-aided telephone interviews (CATI). A total

of 5258 freight operators, including California-based for-hire trucking companies, private fleets,

and for-hire large national carriers were chosen from the databases maintained by Transportation

Technical Services, Inc, producing an overall response rate of 22.4% (1 177 responses). Most

responding operators believe that traffic congestion will get worse over the next five years. Over

half the respondents indicated significant or maj or problems of increased fuel and maintenance

costs due to stop and go traffic, high numbers of accidents and insurance costs, driver

frustrations and morale, and scheduling problems due to unreliable travel times. Similarly, more

than half indicated that stop and go driving, speeders and other traffic violators, and poor road

surface quality are important causes of loss of equipment, damaged goods, or even injury to

drivers. Almost 90% of the freight operators also replied that at least sometimes schedules are

missed, drivers are re-routed due to congestion, or customer time-windows force drivers to work

in congestion. Preferred congestion relief policies included adding more freeway lanes, truck

only lanes on freeways or arterials, better traffic signal coordination along the arterials, and so









forth. The surveyed operators perceive that dedicated highway advisory radio, traffic reports on

commercial radio stations, and face-to-face reports among drivers at truck stops and terminals

are somewhat useful by drivers on the road, while traffic reports on television and computer

traffic maps on the Internet are very useful to dispatchers. With respect to intermodal operations,

about 45% of the operators pointed out that operations of carriers are often or very often

impacted by congestion or other problems at maritime ports, while only 25% of them indicated

this for airports or rail terminals.

The American Trucking Association (ATA) surveyed 470 stratified, randomly selected

private and for-hire motor carriers based in the Baltimore region (ATA, 1997). It yielded a

response rate of 13.1% (62 returned surveys) after two times mailing and follow-up calls. The

ATA worked with the Baltimore Metropolitan Council and the Maryland Motor Transportation

Association for the survey questions to check out various needs of transportation users in

Baltimore region. The survey questions included company characteristics, major routes of

travel, impediments in freight flows, infrastructure improvements needed, downtown freight pick

up and delivery, time of day travel, freight origins and destinations, intermodal freight activity,

company future plans. The survey results describe facts and motor carriers' perceptions about

freight transportation in the Baltimore region. Especially, traffic congestion was thought to be

one major structural impediment to freight movement. Intersection design/function, ramp

design, tools, constructions projects were listed by some motor carriers as well.

In 2001, two surveys were conducted to obtain the opinions of the public and large truck

drivers on road safety issues (Center for Public Policy, 2001). A computer-assisted telephone

surveys produced 2415 samples of randomly selected adult residents from Virginia (n=602),

Maryland (n=600), North Carolina (n=610), and West Virginia (n=603) with a cooperation rate










of 52%. For truck drivers, an intercept survey was used at three truck stops (Lee Hi Truck Stop

(n=206), Truck Stops of America (n=3 18), and White' s Truck Stop (n=102) on interstate 81 and

95, producing 618 samples. The surveys discovered some overall aspects of truck drivers and

difference in perspectives on safety issues from truckers and public respondents. The main

findings from the surveys are as follows:

* More than 70% of truckers in the survey were company drivers opposed to owner-
operators.

* Most truck drivers (about 70%) get paid based on miles driven.

* More than 90% of truckers in the survey are driving tractor-semi trailers.

* More than 80% of truckers spend 3-7 days away from home every week.

* Most truckers take time for a sleep break at private truck stops or public rest areas rather
than motels or roadside.

* Both truckers and public respondents perceived the highways driven on most often by
them to be somewhat safe.

* Truck drivers and public respondents tend to attribute conflicts or crashes between cars and
trucks to each other.

* Both truckers and public respondents agreed that the driving habits of large bus drivers are
considered to be the most safe and least aggressive around cars.

* Both truckers and public respondents agreed that drivers of large trucks drive somewhat
aggressively around cars.

* Truck drivers get information of crashes between cars and trucks mostly from other truck
drivers or citizen band (CB) radio, while public respondents get it from television or
newspaper mostly.

Truck drivers' and motorists' opinions of such restrictions at three sites (I-5 Southcenter

Hill, SR-520, and I-5 Southbound to Tacoma Mall) in the Puget Sound region of Washington

State were obtained through surveys in 1992 (Koehne, et al., 1997). The trucker survey was

performed at two truck stop locations (one day at each location), slightly more than four months

after the last restrictions were put into place on I-5 Southcenter Hill, yielding 129 completed









surveys. A mail-back survey was distributed to motorists who traveled each of the three

restricted sections of highway more than three months after the last restrictions. About 400

license plate numbers were collected from each site for this purpose. The survey produced 153

completed responses (response rate of about 16%). The maj or findings are as follows:

* A relatively high 3 1.4 percent of truckers indicated that they had disobeyed the lane
restrictions, while about 78 percent of motorists indicated that they have seen truckers
disobey them.

* Only about 32 percent of the truckers are in favor of keeping Puget Sound lane restrictions,
while 91 percent of the motorists are in favor of them. The negative view of most truckers
toward restrictions could also be simply because they believe they are not necessary since
trucks rarely use the leftmost lanes on ascending grades.

* About 65 percent of truckers and motorists indicated that it is not clear which vehicles or
which lanes are subj ect to the lane restrictions.

* Only about 30 percent of the truckers believe that lane restrictions improve freeway
operations, while 86 percent of the motorists do.

* Only about 3 1 percent of the truckers believe that lane restrictions improve safety, while
82 percent of the motorists do.

* About 66 percent of the truckers believe that lane restrictions should include buses, and 74
percent of the motorists do.

* Only about 21 percent of the truckers believe that lane restrictions should be expanded to
more freeway sections, while 83 percent of the motorists do.

Three logit models were also developed for exploring truckers' and motorists' favorability

and awareness of truck restrictions. There is a profile of a trucker who is least likely to favor

truck restrictions, one who admits to violating restrictions, frequently changes lanes to avoid

rough pavement, typically carries nonperishable cargo, is between 20 and 40 years old, and has

been licensed for many years. Motorists most likely to favor restrictions also fit a definite

profile; one who frequently changes lanes to avoid being followed by trucks, typically drives a

passenger car, is between 30 and 45 years old, and has been a long-time licensed driver. The

motorists most likely to be aware of Puget Sound truck lane restrictions are male passenger car










operators who have been licensed relatively few years. Efforts to improve motorist awareness

should focus on those motorists who do not fit this profile.

A study by Golob and Regan (2002) investigated the perceptions of truck company

managers on usefulness of different source of traffic information to trucking operations.

Managers from 1177 trucking companies including 34% private carriers and 66% for-hire

carriers were asked how useful they consider different sources of traffic information are to the

dispatchers and to their drivers. The surveys were distributed to 5258 companies containing 804

California-based for-hire companies, 2129 California-based private carriers, and for-hire large

national carriers based outside of California, overall response rate of 22.4%. The relationships

between 6 characteristics of the companies (load type, carrier type, primary service, location of

logistics manager, intermodal operations, and average length of load moves) and manager-

perceived usefulness of different source of traffic information to dispatchers or drivers, are

discovered through canonical correlation analysis. The respondents were asked to evaluate the

sources in one of the three categories (i.e., very useful, somewhat useful, and not useful).

With respect to the overall usefulness to dispatchers, "reports from the drivers on the road"

are judged to be most useful followed by "traffic reports on the radio." Least useful was "traffic

reports on television", followed by "internet traffic maps." "Reports from their own drivers" are

valued most highly by carriers with either rail or multiple intermodal operations, and "traffic

reports on commercial radio stations" are useful to general LTL carriers and operations based in

the Greater Los Angeles Area. "Traffic reports on television" are thought to be useful to movers,

and intermodal operations. "Internet traffic information" is judged to be useful to operators with

long moves, and "phone calls to Caltrans" are considered to be useful to operations based in

California, but outside of the two largest metropolitan areas.










Concerning overall usefulness to drivers, "changeable message signs (CMS)" was thought

to be most useful, followed by "CBR (Citizen Band Radio) or other radio reports from other

drivers." "Traffic reports on commercial radio station" were considered as useful as "face-to-

face reports among drivers", while "Dedicated highway advisory radio (HAR)" was rated to be

least useful for drivers. "Face-to-face reports among drivers at truck stops and terminals" are

useful with rail intermodal operations, and "CMS" was useful to common carriers and operations

from outside of CA carriers with long load moves. "CBR reports from other drivers" are deemed

to be useful exclusively to truckload carriers, and "HAR" is judged to be useful to common

carriers or carriers with long load moves.

As far as future traffic information sources are concerned, "HAR and CMS" are considered

to be most useful followed by "in-vehicle navigation system." "In-vehicle navigation",

"computer traffic map", "CMS", and "traffic information kiosk" are expected to be useful to

operations from outside of CA, and carriers with long moves, while "HAR" is thought to be

useful for carriers with both truckload and LTL in the future.

Crum, et al. (2001) developed a conceptual level of commercial motor vehicle (CMV)

driver fatigue model by reviews of the 55 literature and focus group meetings with industry

professionals. It was later used for developing a survey for truck drivers to explore driving

environment effects on driver fatigue and crash rates. The model categorized three factors

influencing driver fatigue and crashes; CMV driving environments, economic pressures, and

carrier support for driving safety. The CMV driving environments were subcategorized into

three issues as regularity of time, quality of rest, and trip control, under which a total of 25

individual measures fall, while fatigue and crash outcome measures included 15 items. The data

for analysis were collected at five truck stops each in different states, yielding 502 usable









surveys. A $10 cash inducement was offered to participants in 1999 with the assistance of the

National Association of Truck Stop Operators (NATSO). Twelve driving environment

indicators were found to be meaningfully related to 15 fatigue and crash outcome measures; two

regularity of time items, six measures of trip control, and four items for quality of rest. Factor

analysis identified three constructs underlying the 15 fatigue and crash measures; close call due

to fatigue, the perception of fatigue as a problem for self and other drivers, and crashes. From

the regression analysis, "long load time" and "start workweek tired" is found to be significant in

increasing frequency of close calls due to fatigue, while the frequency of the use of "6-hour time

zone" was negatively related to "close calls" unexpectedly. "Long load time" and "start

workweek tired" also were associated with more fatigue, while more "uninterrupted hours of

sleep", more use of "regular routes", and more times "driving the same hours" were associated

with less fatigue. More "average stops per day" and "start workweek tired" were found to

significantly increase the number of crashes.

At the front part of this study, it was decided to use focus group and survey methods in

obtaining the perceptions and opinions of the trucking community. Thus, some previous studies

relevant to this issue were reviewed in this section to search for an effective way to conduct

focus group and survey studies for the obj ective of this study. The authors of the studies provide

their experiences and recommendations about focus group preparation, survey data collection

and perceptions of the trucking community on various trucking-related issues as results. For the

sake of this study, potential Florida trucking community focus group and survey participant

recruitment sources, and advantages and disadvantages of various trucking community survey

methods are summarized in the following three sections, based on the findings from this section.









2.4 Florida Trucking Community Focus Group Participant Recruitment Sources

It is generally difficult to invite truck drivers to a meeting at one place and time. They

usually spend a significant portion of their time driving on the road, and their schedules are apt to

change for time-variant demand for deliveries. One survey on truck safety issues at three truck

stops (Center for Public Policy, 2001) showed that more than 80 percent of the total of 618

surveyed truck drivers spends 3 to 7 days away from home every week for deliveries. Given

these constraints, following potential recruitment sources were found for truck driver focus

groups:

* Florida Trucking Association (FTA) Road Team members
* Florida Truck Driving Championship (FTDC) participants
* Florida-based National Truckers Association (NTA) members
* Truck drivers from one or two trucking companies

Many previous trucking-related studies have benefited from the cooperation of national or

state trucking associations. The American Trucking Association (ATA) is a maj or national

association for United States trucking industry, which often provides assistance to the

researchers, even conducting research studies by itself (ATA, 1997). Most states have either the

state trucking association or motor vehicle carrier association, which is the state division of

ATA. The Florida Trucking Association (FTA) is in that category.

A focus group study was conducted in Toronto at the annual convention of the Ontario

Trucking Association (OTA) with its Road Knight Team members, which consist of 10

professional truck drivers (Hostovsky and Hall, 2004). FTA Road Team is the equivalent of the

OTA Road Knight Team. The FTA Road Team includes 8 professional drivers from 6 different

companies. They are highly informed professionals (each with more than 15 years of truck

driving experience) and care about their industry and profession enough to take time from their

daily jobs to speak at any public gatherings and give safety demonstration to the general public.









The Florida Truck Driving Championship (FTDC) is an annual competition of truck

drivers' driving skills and knowledge on how to operate trucks safely and efficiently. Each

participant competes for the championship in one of eight classes of trucks (i.e., single truck,

three-axle, four-axle, five-axle, tank truck, flat bed, sleeper berth, and twins). The champions of

this event represent Florida truck drivers in the annual National Truck Driving Championship

(NTDC). Florida-licensed truck drivers, who performed regular duties of a full-time professional

truck driver with no accident history for at least a year and no criminal record in the past 5 years,

are eligible to participate in the competition.

The National Truckers Association (NTA) is an organization designed to protect and

support the trucking business of independent (owner-operator) truck drivers. For the reason,

most NTA members are independent truck drivers.

One or two big-sized trucking companies may help get their drivers together for a focus

group meeting. The following potential sources of ticking company contacts were found:

* FTA membership directory
* Center for Economic Development Research (CEDR) Data Center (ES202)
* Florida Department of Highway Safety & Motor Vehicles (FDHSMV)
* Transportation Technical Services, Inc (TTS)

A total of 180 Florida-based trucking companies are listed in the FTA membership directory.

They are categorized by 5 chapters (geographical locations), or 6 conferences (carrier types).

One study (Lawson, et al., 2002) utilized the Oregon Employment Department database (ES202),

Oregon Department of Transportation (ODOT) Motor Carrier Transportation Division truck

registration database, and ODOT Driver and Motor Vehicle Services Division Commercial

Driver's License (CDL) database to survey freight shippers and carriers. Likewise, ES202

database from CEDR Data Center or FDHSMV database can be used to contact the trucking

companies in Florida. The information by TTS was used in the Golob and Regan (2002) study.









It includes national motor carrier directory, private fleet directory, and owner-operator directory

database. FDOT personnel also recommended the following companies as potential sources of

truck driver participant recruitment:

* Watkins Motor Lines, Inc (one of the nation' s largest LTL carriers)
* Landstar Systems, Inc (a big logistics and transportation provider)
* Roundtree Transport & Rigging, Inc

It seems to be also difficult to recruit manager-level participants for focus group meetings.

A study by Morris, et al. (1998) performed 13 industry sector focus groups, but with only 2-4

participants per group. Some of them also used a conference call. They seem to be tight in their

time schedules. The following potential recruitment sources were found for truck company

manager focus groups.

* FTA Leadership Conference participants
* FTA Safety Management Council (SMC) members
* Truck company managers from two or more trucking companies

In 2004, Veras, et al. (2005) conducted 2 focus groups with truck dispatchers as a part of

the Evaluation Study of the Port Authority of New York and New Jersey's Value Pricing

Initiative. FTA Leadership Conference is held annually to support and enhance trucking

business for Florida trucking community. Florida-based Truck company owners and managers

are the main attendees in this event. SMC members consist of professional safety managers from

FTA member companies. Their primary goal is to make trucking in Florida as safe as possible.

It is also possible to contact some of the trucking companies by using the same sources presented

earlier to recruit truck company managers.

2.5 Trucking Community Survey Methods

In-field survey methods have often been used by other researchers to collect truck driver

surveys. The perceptions of truck drivers on roadway safety issues (Center for Public Policy,










2001) were surveyed at three truck stops (Lee Hi Truck Stop, Truck Stops of America, and

White' s Truck Stop on interstate 81 and 95), producing a total of 618 surveys. A truck driver

survey by Koehne, et al. (1997) was performed at two truck stop locations (one day at each

location), yielding 129 completed surveys. Another study about truck driver fatigue and crash

rates (Crum, et al., 2001) conducted surveys at 5 truck stop locations: Maryland, Georgia,

California, Iowa, and Colorado. The National Association of Truck Stop Operators (NATSO)

assisted with the study, producing a total of 502 usable surveys (overall effective response rate

was 97.3% with $10 cash incentives). A week-long survey of deliveries to Trinity College

Dublin (TCD) was also conducted with an intercept survey method at the gate of the campus,

resulting in an 82% response rate (Finnegan et al., 2004). Surveying truck drivers at rest stops or

truck stops have following benefits:

* It usually shows good overall productivity with some types of incentives. The completed
surveys are collected promptly on site.

* It asks for the perceptions of truck drivers on truck operation issues while they are on a trip,
so their experiences are fresh.

* It enables face-to-face interactions between the participants and surveying staff. This helps
yield more completed surveys and reduce the risk of the participants' misunderstanding of
the questions.

However, it is required for the surveying staff to spend a fair amount of time and effort in the

field.

Considering the irregular working hours and j ob characteristics of the truck drivers, it

would be effective to distribute the surveys directly to them. It may be possible that written

surveys are filled out by truck drivers where they can take time to fill them out. At places where

truck drivers stay for a short time, written postage-paid surveys could be distributed to them so

that they can return them later by mail. Otherwise, written postage-paid surveys may be sent to a









number of ticking companies that can distribute the surveys to their drivers. The sources of

contacting trucking companies are listed in the previous section.

Different survey methods can be considered for surveying truck company managers.

Two studies by Lawson and Riis, and Lawson, et al. (2001 and 2002) compared several methods

to survey the freight community (i.e., shippers and carriers) by a literature review and a pilot

study. It was found that the most effective survey method in terms of response rates is a phone-

based survey although a written survey is preferred by the freight firms. In the pilot study, the

highest response rate (60-64%) was achieved by a telephone interview survey (with five

callbacks). The other study (Regan and Golob, 1999) obtained a response rate of 35% with a

phone survey. However, the response rates for previous written surveys of the trucking

community only ranged from 8-24% (Lawson and Riis, 2001).

General characteristics of the survey methods are described in Table 2-1. Mail-based

surveys are less costly and time-consuming while producing relatively low response rate. It

would be helpful to contact the potential respondents in person or by phone to improve the

response rate. In one study (SPRPC, 1996), follow-up phone calls resulted in an 5% increase of

the response rate (from 4% to 9 %). The other study (ATA, 1997) showed a response rate of

13.1% by mailing twice with follow-up phone calls. The surveys were personally distributed to

the potential respondents to improve the response rate in another study (Finnegan, et al., 2005).

This approach also enabled to obtain helpful feedback and comments about the survey issues

directly from them. The survey questions should be clear and simple to reduce the possibility

that the respondents misunderstand them. A postage-paid survey format is typically used to

reduce the respondents' efforts to return the surveys.









Phone-based surveys usually require more money and effort while yielding relatively

high response rate. The two studies (Lawson and Riis, 2001 and Lawson, et al., 2002) indicated

that a traditional structured phone survey is useful in obtaining detailed information about

specific issues, but is not suitable for a sample size large enough to represent the views of a big

population. The Computer-Aided Telephone Interview (CATI) allows for a large sample size

while reducing the opportunity to fully explore a certain aspect of any given topic. The CATI is

typically conducted by survey companies because it requires trained interviewers and interactive

CATI computer systems. The participants' responses are keyed directly into a computer and

administration of the interview is managed by a specifically designed software program. The

program does not accept invalid surveys. In a survey study of freight operators (Regan and

Golob, 1999 and Golob and Regan, 2002), the CATI was conducted by Strategic Consulting and

Research, an Irvine, California-based survey company, yielding a response rate of 35%.

The Web-based survey method is less costly and easy to administer, but the response rate

from this method may greatly depend on publicity and advertisement of the surveys. Some types

of contacts (e.g., phone, email, mail) with potential respondents are highly encouraged to

increase the response rate. Little is known about the use of this method for surveying the

trucking community. The Idaho Technology Transfer Center, in conjunction with the ATA, is

conducting a web-based study about the perceptions of truck drivers or fleet managers on the

benefits of anti-icing chemicals and reductions of potential vehicle corrosion (Alexander and

Moore, 2006).

Regardless of the survey methodss, a clear indication of the contributions) of a survey

study to the trucking community or industry may encourage the potential respondents to

participate in the survey. The sponsorships of trcking-related associations or institutes (e.g.,










FTA, ATA, or FDOT, etc.) may help reinforce the importance of the survey study, resulting in


an improvement of the response rate as well.

Table 2-1. Comparison of Survey Methods


Survey Methods
Phone-based Survey
* Relatively high
response rate
* Chance to correct
misunderstandings
* Chance to get more
detailed information
* No respondents' efforts
required to retum
surveys


0 More costly and time-
consuming
0 Dependent on
respondent availability
o Not suitable for large
sample size
0 Can not be used for
non-audio information
0 May present lack of
uniformity


Characteristics
Advantages


Mail-based Survey
* Less costly and time-
consuming
* Interviewer bias is not
introduced
* Uniform survey method
* Provide respondents
with enough time to
give thoughtful answers
* Suitable for obtaining
larger and more
representative sample
* Relatively low response
rate
* Potential long time
delay
* Hard to ensure that the
right person will
complete the survey
* Potential
misunderstanding of the
questionnaires by the
respondents
* Respondents' efforts
required to return
surveys
8-24%


Lawson and Riis (2001)
Lawson, et al. (2002)
Finnegan, et al. (2005)
SPRPC (1996)
ATA (1997)


Web-based Survey
* Less costly, easy to
administer
* Fast results
* Provide respondents
with enough time to
give thoughtful answers
* No respondents' efforts
required to return
surveys


* Response rates may
greatly depend on
publicity/advertisement
of the surveys
* Hard to ensure that the
right person will
complete the survey








Highly variable


Alexander and Moore
(2003)


Disadvantages















Typical Range
of Response
Rates
Studies in which
used


35-64%


Lawson and Riis (2001)
Lawson, et al. (2002)
Regan and Golob (1999)
Golob and Regan (2002)









CHAPTER 3
RESEARCH APPROACH

This exploratory study was aimed at discovering the factors important to estimate LOS on

existing roadway systems, as perceived by truck mode users. This was intended to provide the

FDOT with the information about the determinants of truck LOS and the levels of their

significance with which it can develop methods to effectively evaluate LOS provided for trucks

on various roadway facilities. To accomplish this, it was necessary to obtain the perceptions and

opinions of the trucking community on what factors are important for the quality of a truck trip

on various transportation facilities and the relative significance of each of those factors. Focus

group and survey studies were conducted to satisfy this requirement.

Truck drivers and truck company managers are the two maj or stakeholder groups for the

LOS provided for trucks. Truck drivers deliver goods by driving trucks while truck company

managers operate the trucks and drivers to make a profit. Truck drivers are the most important

group concerning truck LOS, in that they are the ones who actually drive the trucks on the road,

so their performance and satisfaction is directly affected by the LOS provided by various

transportation facilities. Their performance levels also have major effects on trucking business

in such aspects as on-time performance, operating cost, etc. Truck company managers are

primarily concerned with those trucking business concerns, but in most cases they need to

manage their fleet on existing roadway facilities based on the perceptions and opinions of their

drivers due to the lack of regular truck driving experience. Thus, the focus was on the

perceptions of truck drivers, while the perceptions of truck company managers were also sought

to be compared with those of truck drivers.

Initially, very limited knowledge was present on the factors determining LOS perceived by

the trucking community. This required a small number of observations to be obtained through









some type of qualitative study to get enough information to develop a more formal survey, which

would then be used to obtain the perceptions of a larger sample to represent the trucking

community. Typically, there are two types of qualitative study methods; personal interviews and

focus group studies. Individual interviews can require a considerable amount of time and effort.

A respondent is also apt to be unwittingly influenced by an interviewer, or, often not able to

come up with his/her opinions about various aspects of the subj ects during the interview.

Homogeneous and information-rich participants in a focus group study can boost the diverse

conversation about the subjects with little guidance from a moderator. Thus, focus group studies

were performed for this study.

Focus group studies do not provide enough observations for quantitative analysis. Thus, a

follow-on survey study was conducted to confirm the focus group findings with a broader

audience and measure the relative importance of each factor quantitatively. The surveys focused

only on potentially relevant and important items.

Based on the focus group and survey studies, the guidelines for developing truck LOS

estimation methodologies were developed to provide the FDOT with potential service measures

for truck LOS and/or a list of factors that should be considered to develop truck LOS estimation

models on various roadway facilities. The overall description of the research approach of this

study is presented in Figure 3-1.

To develop actual truck LOS estimation models in future studies, experimental data should

be collected to quantitatively measure the correlations between the service measures and/or the

list of factors identified in this study and the perceptions of a representative sample of truck

drivers on trip quality. This often requires some experimental efforts with in-field driving, video

simulation, or driving simulator methods.









3.1 Focus Group Sessions

The primary obj ective of this focus group study was to identify the roadway, traffic, and

control factors that are important to the trucking community for truck trip quality on various

transportation facilities and to explore the perceptional differences between truck drivers and

truck company managers on the relative importance of each factor.

Ultimately, this study sought to inform transportation service providers of what should be

focused on to better accommodate truck traffic on existing roadway facilities. Thus, the factors

that cannot be controlled by the transportation service providers were of less interest in the

discussions, although the discussions were open to any factor important to the quality of a

complete truck trip from origin to destination. The focus was on the operational and design

policies and issues relative to truck operations on various roadway facilities (e.g., lane widths,

traveler information systems, etc.) as opposed to truck industry regulation issues such as number

of continuous hours of driving, maximum non-permit weight loads, etc. Weather-related factors

were also less of a concern.

Three focus group interviews were conducted to elicit the factors affecting LOS on various

transportation facilities perceived by truck mode users (truck drivers and truck company

managers). The participants for each focus group session were recruited by the cooperation of

the Florida Trucking Association (FTA). Several discussion topics were selected carefully for

the overall obj ective of this study, which is to Eind out what should be focused on to better

accommodate truck traffic on the existing roadway system. During each focus group session, the

topics were introduced to the participants with several open-ended questions so that they discuss

each topic amongst themselves with only a little guidance from the moderator. The focus group

discussions were transcribed and summarized to be used as inputs to a follow-on survey.









3.1.1 Participant Recruitment

Based on the review and considerations of focus group participant recruitment presented in

Chapter 2, it was decided that the most effective and efficient approach to recruiting candidates

for the focus group sessions would be with the cooperation of the FTA. With the FTA' s contacts

and presence in the industry, they would be much more capable of identifying willing

participants for this study. Thus, assistance from the FTA was solicited (Appendix A for a

cooperation request letter), and they were happy to assist. FTA staff were pleased to hear that

the FDOT was conducting a research proj ect targeted at the LOS needs of the trucking

community .

For the focus group participant recruitment, several documents were provided to the FTA

to inform them of preferred participant characteristics, what questions would be asked

throughout the sessions, and how the sessions would be conducted. The documents included

focus group instructions (Appendix B), guidelines for participant selection (Appendix C), and a

focus group moderator's guide (Appendix D). For the truck drivers' focus group sessions, the

FTA recruited members from its Road Team. Two other truck driver candidates were also

recruited. Once they provided a list of these candidates, a follow-up recruitment letter was

emailed to them to ask for confirmation. Focus group instructions and a map to the meeting

place were attached to the email as well. Two focus group sessions were held with the truck

drivers as follows:

* 5 people including 4 FTA Road Team members, 2.5 hours of discussion, on November
15th, 2005

* 4 people including 3 FTA Road Team members, 2.5 hours of discussion, on December 8th,
2005









It was initially planned to also hold two focus group sessions with truck managers, but the

FTA found the recruitment of these individuals to be much more difficult. Ultimately, just one

2-hour focus group session was held with three managers on November 17th, 2005.

3.1.2 Participant Selection

When the FTA agreed to help recruit the participants for the focus group meetings, the

guidelines for selecting the participants were developed and provided to the FTA (Appendix C).

This was intended to ask the FTA to consider the guidelines to obtain a representative sample of

the Florida trucking community. They describe characteristics of eligible participants and the

desirable participant composition for each focus group. This section explains the reasoning

behind the development of the guidelines.

Initially, a total of four focus group interviews were proposed (two with drivers and two

with managers), balancing the scope and resources available for this study. The two distinct

groups of participants were separately invited to different meetings because they were expected

to have different perspectives with regard to the discussion topics. Truck drivers may show more

concerns about the traffic, roadway, and/or control factors affecting truck driving comfort and

amount of earning while truck company managers may be more concerned with the factors

contributing to their trucking businesses. The separation of the two groups not only made the

participants comfortable to share their opinions, but also helped the research team to clearly

identify the perceptions of each group. The desired number of participants in each focus group

was originally 8-10. However, it proved challenging for the FTA to obtain this number of

participants. Thus, about half this number of participants for each focus group was obtained.

Nonetheless, these group sizes worked out well given the depth and breadth of the participants in

each session, and that each of the participants had much to contribute to the discussions.









There are several maj or socio-economic or working characteristics of the trucking

community that may be highly correlated with their perceptions on the factors affecting LOS on

transportation facilities. It is not realistic to take into account all the characteristics of the focus

group participants with the several focus group meetings, but consideration would help recruit a

more representative sample of the Florida trucking community. The following characteristics of

the trucking community were considered in the guidelines:

Hauling distance: Long-haul trucking with frequent travel on the Florida' s SIS facilities

was of main interest in this exploratory study as opposed to local or short-haul trucking, which

only use small portion of the Florida' s SIS facilities.

Carrier type: It was desired to include the participants from both for-hire and private

truck companies. Private trucks moved 32.7 percent, and for-hire trucks transported 43.3 percent

of the total freight value originating in Florida in 2002 (BTS, 2004). The rest of the percent (24

percent) was delivered by other modes such as train, ship, plane, or multiple modes. One survey

study (Golob and Regan, 2002) verified the perceptional difference between private and for-hire

truck companies about various sources of traffic information.

Load type: It was desired to include truck drivers from both Truckload (TL) and Less-

than-Truckload (LTL) carriers. It is likely that the perceptions of TL drivers on truck trip quality

issues are different from those of LTL driver due to the weight of the equipment and goods. The

TL and LTL operations are defined as follows:

* TruckLoad (TL): The quantity of freight required to fi11 a truck. Usually in excess of
10,000 pounds. When used in connection with freight rates, the quantities of freight
necessary to qualify a shipment for a truckload rate.

* Less-than-TruckLoad (LTL): A quantity of freight less than that required for the
application of a truckload rate. Usually less than 10,000 pounds and generally involves the
use of terminal facilities to break and consolidate shipments.









Truck type: It is likely that perceptional difference among the drivers operating different

types of trucks exists due to their discrete size and operational characteristics. The majority of

the trucks on the roads were categorized into the following three types of trucks in terms of truck

configurations:

* Straight Truck (single unit truck)
* Truck-Trailer (truck-trailer, truck-double trailer)
* Tractor-Trailer (tractor-semitrailer, twin-trailer, rocky mountain double, turnpike double)

It was desired that each truck driver focus group include at least three truck drivers, each

operating different three types of the trucks.

Fleet size: It was desired to include truck drivers that represented companies with a

variety of fleet sizes. About 87 percent of the carriers in the U. S. operated 6 or fewer trucks, 9

percent operated the fleet size between 7 and 20, and 4 percent operated more than 20 trucks in

2004 (ATA, 2005).

Others: ATA (2005) showed that 29.4% of the total drivers in the U.S. were minorities in

2003 and the percentage has been going up gradually from the year 1996. Only 4.6 % of the

total truck drivers in the U.S. were women in 2003, but the percentage has been going down

since 2001. So, inclusion of the minority or woman truck drivers in focus group sessions was

desired, but this was obviously difficult to attain, given their limited proportions.

Overall, a good composition of participants was recruited for the two truck driver focus

groups in terms of carrier type and primary load type. The participants were all from major

trucking companies with at least 16 years of truck driving j ob experience. The truck types they

operate include various tractor-trailers. However, no straight truck driver, truck trailer driver,

female driver, or minority driver was present on the meetings. Only one truck company manager

focus group was conducted with three managers. They were all from the TL carriers with at least










5 years of truck company manager experiences. One was from private company while the other

two were from for-hire companies. A detailed description of the focus group participants'

backgrounds is provided in Appendix E.

3.1.3 Focus Group Questionnaires

For each focus group interview, several discussion topics were introduced to the

participants by the moderator through several open-ended questions. Then, the participants

talked about the topics amongst each other with a little guidance of the moderator. An identical

set of questions was presented for each meeting to compare and contrast the perceptions of truck

drivers with those of truck company managers. The issues to be covered were selected according

to the overall obj ectives of this focus group study, which is to investigate which factors

transportation service providers should focus on to better accommodate truck traffic. Following

four topics were considered as most relevant to this study, therefore were covered during each

focus group meeting.

Truck travel route and departure time selection:

* Who is responsible for selecting a truck travel route and departure time for a delivery?

* When selecting a travel route and departure time for a delivery, what factor are considered
and what is their relative significance?

Truck trip quality on various transportation facilities:

* When you drive a truck on Florida's roadway facilities (i.e., freeways, arterials, and two-
lane highways) for a delivery, what factors affect the quality of a truck trip and how
significant is each of those factors for it? (for truck driver groups)

* When you consider truck operations on Florida' s roadway facilities (i.e., freeways,
arterials, and two-lane highways), what factors affect the quality of a truck trip and how
significant is each of those factors for it? (for truck company manager groups)

Transportation service improvement priority for trucking industry:

* What types of transportation facilities (e.g., freeways, urban arterials) would you
emphasize most for improving truck operations in Florida?










* What are your top priorities for improving trip quality/travel condition for commercial
trucks?

Truck delivery schedule reliability:

* What factors affect the truck drivers' ability to reach a destination on time?
* How often does a late delivery take place?
* What are the typical consequences for you/your company of a late delivery?

The most important contributors to quality of a truck trip perceived by the trucking

community were thought to be the factors that they consider for truck route and departure time

decision, so they were asked in the first place. The participants provided their valuable insight

about in what respect certain routes and times of day are preferred or avoided by them and which

specific factors contributes to each of those aspects. The performance levels of those factors

have most significant impacts on their trucking business and truck trip quality perceived by them.

Secondly, the participants were directly asked to list the factors affecting quality of a truck

trip. The topic was introduced by each roadway type, in that it was generally admitted in the first

focus group meeting that the importance of a factor on truck trip quality vary by which type of a

roadway it is on. For example, it was discussed that the importance of shoulder width and

condition on truck trip quality for two-lane highways was considered to be much bigger than that

on freeways. The participants also indicated how and how much each factor influences truck trip

quality based on their direct or indirect experiences.

Improvement priorities among various transportation issues and facilities were asked next.

It was intended to search for the facilities or specific factors which are getting relatively less

attention by transportation service providers compared to their relative importance on truck trip

quality. The participants' comments on this issue will be a good reference for prioritizing

transportation improvement plans.









As the truck volume and the demand for just-in-time deliveries have increased in Florida,

the importance of truck delivery schedule reliability has become critical. Thus, at the last part of

the discussion, the participants were asked to list the factors contributing to the issue and indicate

their level of impact on it. Truck company manager participants explicitly mentioned that their

customers assess the performance level of their truck companies based primarily upon on-time

delivery performance. The factors affecting on-time performance, frequency and consequences

of a late delivery were explored within this topic.

The factors identified as important to any of the topics covered in the focus group

discussion were used as a primary input for a follow-on survey so that the importance of each of

those factors could be verified by a larger audience and the relative significance among the

factors on truck trip quality could be quantitatively investigated.

3.1.4 Conducting Interviews

The focus group meetings were held at the Civil Engineering Conference Room or

Transportation Research Center Conference Room at the University of Florida. All the meetings

were moderated by a researcher who is knowledgeable to direct the discussions for the purpose

of this study. Upon the arrival of the participants, they were asked to fi11 out an informed

consent form and a two-page participant background survey (Appendix F and G). During the

main session, the moderator introduced the selected topics to the participants with several

general open-ended questions (Appendix D). Each question was displayed on a big screen to

help keep the participants focused on the issue being discussed. The participants discussed each

topic amongst each other with just a little guidance from the moderator. Although all the issues

were planned to be covered within the two hours, it turned out to not be enough time because

they had so much to say about each topic. The truck driver focus groups were extended by about










30 minutes with the permission of the participants. Participation in the focus group meetings

was voluntary.

The audio from the focus group meetings was recorded on a laptop computer through

external microphones and a specialized software product that enabled every comment to be

associated with the corresponding speaker. The recorded audio mies (.way mies) were

transcribed into an electronic text format. The transcriptions were reviewed in detail by the

research team to ensure their accuracy. After the transcriptions were complete and accurate, the

focus group discussions were summarized and used as guidance in the development of the

follow-on surveys.

3.1.5 Summary of Discussions

The results of the focus group studies were summarized by each issue covered in the

discussions. The participants did not always focus on the issue being discussed. They

sometimes jumped onto the previously discussed issues or the issues to be discussed later. As

long as the discussions remained on relevant topics, the moderator did not redirect the

conversation. Thus, it was required to collect participants' comments about each issue spread

over the transcriptions. Some of the comments were paraphrased for clarity and brevity, but care

was taken not to place any personal perceptions on the summary. The factors perceived to affect

truck trip quality were categorized by each transportation facility type (e.g., freeways). Each of

the factors was listed and described with the participants' comments about its contribution to the

truck trip quality and their direct or indirect experiences with it. The perceptions of truck

company managers were separately summarized to be compared with those of truck drivers.

The focus group meetings covered many issues in a limited amount of time. The

discussions were directed by the moderator to draw out a list of the factors important to truck trip

quality, not spending too much time on one or two specific factors. Thus, it was not appropriate









to make quantitative implications of the discussions on the relative importance of each factor. It

was investigated quantitatively in a follow-on survey, which was developed based on the focus

group discussion summary.

3.2 Survey Studies

The primary obj ective of this survey study was to verify the importance of each factor

identified in the previous focus group study with a larger audience and to quantitatively measure

the relative significance of each of those factors on truck trip quality on Florida' s roadway

system. Preference of the respondents on truck driving time of day was also investigated to

explore how quality of truck driving environment varies by time of day. The background

characteristics of the respondents that were correlated with their perceptions on truck trip quality

or preference on truck driving time of day were identified.

The survey included following three parts: working and socio-economic backgrounds of

the respondents; their perceptions on the relative importance of each factor on truck trip quality;

and their opinions about the importance of each hypothetical truck LOS performance measure.

Two sets of survey forms were distributed; one for truck drivers and the other for truck company

managers. Some questions about the backgrounds of the respondents differed in the two survey

forms, but identical sets of factors and hypothetical performance measures were evaluated in

both surveys. They were carefully selected based on the previous focus group study results.

Only truck trip quality determinants on the following three roadway types were investigated

considering the lengths and complexity of the surveys: freeways; urban arterials; and two-lane

highways.

A total of 459 truck drivers and 38 truck company managers responded to the written

surveys collected at Florida Truck Driving Championship (FTDC) event or the postage-paid

mail-back surveys distributed at four agricultural inspection stations. The survey responses were










analyzed with various statistical methods such as descriptive statistics, Exploratory Factor

Analysis (EFA), multiple comparisons of the means, non-parametric tests, and chi-squared tests

of independence.

3.2.1 Question Types

When the issues to be questioned in the survey were selected, a questions type for each of

those issues was carefully determined to effectively carry out the study. It involved such various

considerations as applicability of statistical analyses, respondent burden, respondent error (or

complexity level), measurement accuracy, etc. Following fiye question types were utilized in

this survey study: interval-rating questions; ratio-scale questions; forced-ranking questions;

discrete-choice questions; and Eixed-sum questions. This sections describes characteristics

(advantages and disadvantages) of those question types based on which the question types for

each survey issue of this study were determined.

3.2.1.1 Interval-rating questions

This question type is often used to ask for the perceptions of the respondents. Respondents

are asked to present their perceptions on the level of importance, satisfaction, or agreement for

each item on an interval-rating scale (e.g., 1 = Not at all Important, 7 = Extremely Important).

The accuracy of the participants' opinions may be improved as the number of selectable points in

a rating scale increases. However, more points require the respondent to think about the

differentiation along the extended scale, increasing response burden and time to complete the

survey. It is typical to have at least Hyve points on the scale, since fewer than five points

generally do not generate normally distributed data which are usually obtained when the data are

truly interval in nature. Seven-and ten-point scales are most common. Even-numbered scale

may be used when there is a need to force the respondents to commit to one side or the other, but










it typically creates a downward bias as they tend to choose five as a neutral point in a ten-point

scale.

A critical differentiator between interval-rating and ordinal scale questions is that equal

intervals exist between each adj oining pair of response options. If all response options are

literally presented (e.g., 1 = Not At All Important, 2 = Little Important, 3 = Important, 4 = Very

Important, 5 = Extremely Important), the difference between each adj oining pair of response

options are not the same, resulting in ordinal scale data. Thus, the response values from an

ordinal scale question do not have numerical meaning, and categorical or nonparametric

statistical analyses can only be applicable to the data. It is also true for a forced-ranking question,

which is one type of the ordinal scale questions. The responses from an interval-rating scale

question are appropriate for the numerical interpretation (although not perfectly), enabling

further statistical analyses (e.g., parametric statistical analyses). However, an interval-rating

scale question allows respondents to indicate that everything is almost equally

important/satisfied, making the distinction among the factors difficult, while a forced-ranking

question require them to make clear distinctions among the factors.

3.2.1.2 Ratio-scale questions

Respondents are asked to provide their answer as a ratio to a basic unit of measurement

(e.g., year, dollar, hour, etc.) assigned to each question. The ratio scale of measurement is the

most informative scale and widely used to obtain participants' background data (e.g., age,

number of children, how long their phone call was on hold, etc.). It has zero position indicating

the absence of the quantity being measured and is an open-ended interval-rating scale, in that

there is no designated upper-end point.

The use of a ratio-scale question for respondents' perceptions or opinions (e.g., perceived

importance or satisfaction levels) may not be a good idea. It may decrease a response rate,










increase respondent error, and bring about unreasonably large variances among the responses or

participants, rather than increase level of precision in their responses with numerous numbers of

points in the scale.

3.2.1.3 Forced-ranking questions

Respondents are given a number of factors and asked to place them in order based on a

certain criterion (e.g., importance level). For example, six factors might be presented and the

respondent is asked to place a "1" next to the most important factor, a "2" next to the second

most important factor, and so on. Again, the response values from this scale do not have a

meaning numerically, so the data should be analyzed categorically or nonparametrically. These

data are often analyzed with a cumulative frequency distribution for each factor. For example,

"90 percent of the respondents perceived that factor A is at least secondly important among the

listed factors." This approach forces them to clearly distinguish one factor from others, usually

producing a larger variance among the responses than an interval-rating scale question.

A forced ranking question is heavily prone to respondent error. Respondents might

interpret it as an interval-rating question, use the top and bottom rankings more than once, or

rank the top and bottom items skipping the middle ones. In a telephone survey, you could train

the interviewers to prompt the respondent for full and correct answers. For a web-based survey,

you could insert an error feedback system, not letting the respondents move through the survey

without completing the question correctly. These practices are as likely to annoy the respondent

as to get real answers. It could also require the respondents to make a considerable effort to

complete the survey as the number of factors to be ranked increases. Rank ordering of 10 factors

is, in fact, extremely difficult.









3.2.1.4 Discrete-choice questions

Respondents are given a list of items and asked to choose one answer that best applies, or

specific number of answers that best applies, or all the choices that apply. A survey developer

decides which type of discrete-choice question is appropriate for a survey issue, considering the

characteristics of the issue, study purpose, and respondents' burden accompanied by the question.

This question type is widely used in a survey to ask for the respondents' background

characteristics as well as their perceptions on certain issues. The responses are nominal (or

categorical), so it is easier for the respondents to reply to the question than other question types.

The discrete-choice data are often presented by (cumulative) frequency distributions of each

option category. If the respondents' perceptions are asked with a discrete-choice question, logit

modeling technique can be applied where the responses data are used as a response variable and

other background characteristic data as explanatory variables. Relationships between the

perceptions and background characteristics of the respondents are identified by the logit

modeling technique, which provides an equation to predict the response once a set of background

characteristics are given. This question type is often used to ask about the topics that may be

potentially sensitive to the respondents (e.g., income level) because it is less personally obtrusive

than a ratio-scale question to the respondents. In that case, the response data can be used in logit

modeling process as a potential explanatory variable.

Multiple choices, multiple responses: Respondents are given multiple response

categories and asked to select either all the response choices that apply, or specific number of

response options that apply. The former question type is usually used for some background

characteristics (e.g., choose all the types of foods you can cook) and the latter is often used to

investigate the perceptions of the respondents (e.g., select 2 types of foods you like the most).

The response data are often analyzed by (cumulative) frequency distributions. When the former










question type is used, the data are often converted to binary data for each response category and

used as potential explanatory variables for various statistical modeling (e.g., logit, probit, or

regression modeling). The latter question type requires less respondent burden than other

question types for respondents' perceptions. The respondents only have to choose a certain

number of factors (e.g., 2, 3, or 5 factors) from a list of factors according to their perceptions.

Two or three factors are most commonly asked to be selected, but the number of factors to be

selected basically should be determined based on the purposes of a study. One study by

Washburn, et al. (2004) used this question type to identify most important user-perceived trip

quality determinants on rural freeways. The respondents were asked to select 3 factors most

important to their perception of trip quality among 16 listed factors. The importance of each

factor was presented by the percent of the respondents who selected the factor in their top 3 most

important factors.

Multiple choices, single response: Respondents are asked to select only one most

appropriate response out of multiple response options. A survey designer should be careful to

direct the respondents to select only one most appropriate response, not just any responses that

may apply. If the perceptions of the respondents are asked with this question type, the

relationships between their perceptions and background characteristics can be modeled by multi-

category logit or probit modeling technique, which provides a means of predicting response

probability, given a set of background characteristics. This question type can also be used to ask

for some background characteristics of the respondents (e.g., race). In that case, the responses

are often converted to a set of binary data to be used as explanatory variables for other models.

Binary choice: The respondent is asked to select one out of two choices. Typically, these

choices are true or false, or yes or no (e.g., existence of any dependent). If the perceptions of the










respondents are asked with this question type, the relationships between their perceptions and

background characteristics can be modeled by binary logit or probit modeling technique, which

provides a means of predicting response probability, given a set of background characteristics.

This question type can also be used to ask for some background characteristics of the

respondents (e.g., gender) and defined as an explanatory variable for other models. In all cases,

the data can be easily represented by frequency distributions.

3.2.1.5 Fixed-sum questions

Fixed-sum or fixed-allocation questions are combination of the interval-rating scale and

the forced-ranking questions. Respondents are given a set of factors and asked to allocate a total

of 100 points to the factors for a certain aspect (e.g., importance level). They are encouraged not

only to consider rankings of all the listed factors before the allocations, but also to present level

of each factor for the aspect by a number. This question type is also used to obtain some

background characteristics of the respondents (e.g., percent of your trip purposes: business;

leisure; social). The block of points to allot is typically 100 since most people are comfortable

thinking in percentages, but allocated scores often tend not to add up to 100 as the number of

factors in question increases. Time-consuming data cleansing is required for the responses not

summing to 100. For this reason, it is common to present not more than 10 factors and state

what an equal weighting would be. Typically, 4, 5, or 10 factors are listed to be evaluated

because the equal weighting (i.e., 25, 20, and 10) is easily recognized by the respondents. The

response data are often presented with descriptive statistics and can be considered as a response

variable or an explanatory variable for various statistical analyses.

3.2.2 Survey Development

The survey issues covered in this study were selected for the ultimate obj ective of this

study which is to find out what should be focused on by transportation service providers to better









accommodate truck traffic on current roadway systems. They are presented in Table 3-1 with

their corresponding question types and analysis methods utilized in the survey to address those

issues. Two different survey forms were prepared (Appendix H for truck driver survey and

Appendix I for truck company manager survey). Identical sets of factors were evaluated in the

two surveys to compare the perceptions of the two groups, but most questions regarding

respondents' background characteristics necessarily differed between the two surveys.

Participant's background: Background characteristics of each participant were asked in

the first part of the survey. It was intended to discover the relationships between their

backgrounds and perceptions on truck trip quality. Thus, the characteristics suspected to explain

the potential variances in their perceptions were included in the first place.

The background survey section includes the questions about socio-economic status and

working characteristics of the respondents. Various question types were used for this section

according to the characteristics of the selected issues. Discrete-choice questions were used to

ask for socio-economic backgrounds (e.g., age, annual income) to be less personally intrusive to

the respondents than ratio-scale questions. All the response options for working characteristics

questions (e.g., types of goods hauled, truck types used, duties of truck company managers, etc.)

were determined through extensive literature search and discussions with members of Florida

trucking industry to reflect the current state of Florida trucking industry.

Preference on truck driving time of day: Truck driver respondents were asked to

present current and preferred truck driving times of day, while only preferred truck driving times

of day could be asked in the manager survey. It was intended to investigate how quality of truck

driving condition varies by time of day. This offered valuable information about the preference

of the trucking community on night-time delivery and how the preference of truck drivers on









truck driving time of day is different by their background characteristics. Time of day was

divided into 6 time periods and preference on each of the periods was asked with a binary choice

question to reduce respondent burden.

Relative importance/satisfaction of each factor on truck trip quality: Relative

importance of each factor on truck trip quality was asked simultaneously with relative

satisfaction of each factor on overall Florida roadway facilities. Importance-sati sfaction (or

importance-performance) analysis approach is often used in the field of marketing research to

prioritize attributes of a product for improvement (Martilla and James, 1977). That is,

manufacturers should firstly focus on improving the attributes of a product that are perceived to

be important, or are unsatisfied by the customers. In this study, this approach provided

transportation service providers with valuable insights about what should be firstly focused on to

improve LOS for trucks (i.e., the factors that are perceived to be important, but are not well

satisfied by truck drivers).

Basically, the factors identified in the previous focus group studies were presented in the

survey to be evaluated, but a couple of other factors that the research team considered to be

important were also included (e.g., frequency of faster vehicles following your truck). Weather

factors (e.g., thunder storms, heavy rain falls) were excluded from this survey study even though

they were perceived to be fairly important in the focus group studies, because they cannot be

controlled by transportation service providers. The relative significance/sati sfaction of each

factor on following three roadway facilities was evaluated: freeways; urban arterials; two-lane

highways. Truck trip quality issues on multilane highways were not covered in this study to

keep the proper length of the surveys. It was indicated in the focus group studies that the

trucking community was least concerned about truck trip quality on multilane highways among









those on various roadway facilities (i.e., freeways, urban arterials, two-lane highways, and

multilane highways).

The importance/satisfaction of each factor was asked on a 7-point relative interval rating

scale (-3 = Least Important (or Satisfied), O = As Important (or Satisfied) As Others, +3 = Most

Important (or satisfied)). It was not appropriate to use typical interval-rating scale, ordinal scale,

or ranking scale types of questions. As mentioned in a previous section, a typical interval-rating

scale question allows respondents to indicate that everything is almost equally important, making

the distinction among the factors difficult. An ordinal scale or forced-ranking questions do not

allow mathematical interpretation of the survey responses, restricting the applicability of various

statistical analyses. The number of factors to be evaluated ranged from 18 to 19 by roadway type,

so the use of a forced-ranking scale question was strongly discouraged because it would

significantly increase respondents' burden and error, or decrease the response rate. Bubble-

shaped option boxes were displayed for participants to reply to this survey question easily. A 7-

point scale was used for this question, balancing the precision in measuring respondents'

perceptions and the respondent burden. A small number of points in scale decrease level of

measurement precision, while a large number of scale increase the respondent burden.

Improvement Priority Score (IP~S): Although not asked directly in the survey,

improvement priority of each of the listed factors could be assessed through a combination of

Relative Importance Score (RIS) and Relative Satisfaction Score (RSS). Improvement priority is

proportional to RIS and inversely proportional to RS~S. That is, the more important or the less

satisfied a factor was, the more the factor is in need of improvement. Based on this reasoning,

Equation 3.1 was devised to calculate the Improvement Priority Score (IPS) for each factor.










SRIS \
IPS =I x (RIS -RSS) (3.1)
SRSS

where,

IPS = Improvement Priority Score (-42 +42)

RIS = Relative Importance Score (1 7)

RSS = Relative Satisfaction Score (1 7)

a = +1 if RIS >= RS~S, otherwise -1

RIS and RS~S of each factor collected on an interval-rating scale of -3 to +3 were converted

to a scale of 1 to 7 for calculating IPS for each factor. The greater the IPS of a factor is, the more

improvement on the factor is anticipated. When the importance level of a certain factor is equal

to the satisfaction level, IPS will be zero. A total of 49 (7 RIS x 7 RS~S) possible responses and

their corresponding IPS are tabulated in Appendix J. One survey response presents one IPS for

each factor. An average IPS of each factor from all the responses is used to estimate its relative

need of improvement.

Relative importance of each factor on overall trucking business: Relative importance

of each factor on Operating Cost (OC), On-time Performance (OP), and truck drivers' Trip

Satisfaction (75) was asked individually on a 7-point relative interval rating scale (-3 = Least

Important, O = As Important As Others, +3 = Most Important) in the manager survey. The

percent allocation of the managers' concerns on each of these issues was also asked with a fixed-

sum question. OC, OP, and TS values were converted to a scale of 1 to 7 and relative importance

of each factor on Overall Trucking Business (OTB) was calculated by summing the OC, OP, and

TS values weighted by their corresponding portion of the managers' concerns.









Relative importance of each category of factors on truck trip quality: All the listed

factors for each roadway type were divided into four categories for each roadway type and the

relative importance of each of the categories on truck trip quality was evaluated with a forced-

ranking question (1-4, 1 = Most Important, 4 = Least Important). This was intended to

investigate which types of transportation service are relatively important to LOS perceived by

truck mode users for each roadway type and how this importance priority varies for various

roadway facilities. The relative importance of four identical categories were evaluated for

freeway and two-lane highway facilities (i.e., physical roadway condition, traffic condition,

traveler information systems, and other drivers' behavior), but 'signal condition' replaced

'traveler information systems' category to be evaluated for urban arterial facilities. A forced-

ranking question was used for this issue to focus on the distinctions among the importance of the

categories of factors. The use of an interval-rating scale question was not appropriate because

there is much chance that most respondents simply state that every category is equally extremely

important.

Applicability of single hypothetical performance measure to estimate truck trip

quality: Based on the focus group studies, several hypothetical truck LOS performance measure

were developed by the research team for each roadway facility (e.g., Ease of Driving at or above

the Speed Limit for freeways). This was intended to investigate whether it is possible to use only

one or two performance measures to adequately evaluate truck trip quality on each roadway type.

The hypothetical performance measures for each roadway type were selected in a way they are

independent one another. That is, each performance measure presents different aspect of truck

driving condition. Each performance measure can be considered as a function of multiple

specific factors that were evaluated in a former part of the survey. The applicability of each










hypothetical performance measure solely to evaluate truck LOS was asked on a typical 7-point

interval-rating scale (1 = Not at all Applicable, 7 = Perfectly Applicable). It was expected to

obtain large enough variances for the distinction amongst the factors since it is not reasonable for

the respondents to indicate that most of the different performance measures are perfectly

applicable solely to assess truck LOS. Again, a 7-point scale was used for this question,

balancing the precision in measuring respondents' perceptions and the respondent burden.

Relative improvement priority for each roadway type: The relative improvement

priority among various roadway types was asked at the last part of the survey. It was intended to

find out which roadway facility types are more in need of improvement for the trucking

community among the four roadway facility types (i.e., freeways, urban arterials, two-lane

highways, and multilane highways). A forced-ranking question was used for this issue to focus

on the distinctions among the roadway facility types while keeping the respondent burden at a

reasonable level (1-4, 1 = Most in Need of Improvement, 4 = Least in Need of Improvement).

The survey data is beneficial for prioritizing the improvement needs of various roadway facility

types for truck traffic.

3.2.3 Data Collection

Truck drivers and truck company managers, as the maj or truck mode users on the Florida' s

SIS facilities, are the target population in this study. A truly random sample is impossible to

obtain considering expected difficulties with recruitment, time, and budget. However, an effort

has been made to get a reasonably representative sample for this study.

Based on the review and considerations of truck driver survey methods discussed in

Chapter 2, two different approaches were used for survey data collection of the truck drivers.

The first method involved the distribution of the written surveys at the Florida Truck Driving










Championship (FTDC) event. The second method consisted of distributing the postage-paid

surveys at several agricultural inspection stations.

The first truck driver survey was conducted at the FTDC event (on June 1-3, 2006 in

Tampa). This event is co-sponsored by the FTA and they assisted the research team with the

distribution of the surveys. They were given to the drivers while they were in a session where

they were required to fill out other paper work as well.

There were some concerns expressed by the research team about the original length (six

pages) of the survey, and the research team discussed this with the FDOT and the FTA.

However, the FTA representatives felt that the length would not be a problem because they

would require the drivers to fill them out as part of their participation in the event, so it was

decided to leave it at that length.

The first two pages of the survey included the questions regarding the socio-economic and

working characteristics of the respondents. The relative importance/sati sfaction of each factor

on truck trip quality on freeways, urban arterials, and two-lane highways were asked on pages 3,

4, and 5 of the survey, respectively. The last page was used to ask about the applicability of

several different performance measures being suitable as a single performance measure to

estimate truck trip quality on each roadway type (Appendix H for the truck driver survey form).

As FTA recommended, a total of 220 truck driver survey forms were provided for

distribution at the driving competition. This number was based on the number of registered

participants in the event. Of this total, 148 surveys were returned to the researchers.

Unfortunately, only 3 8 respondents out of the total of 148 respondents (25.7%) completed all

sections of the survey as directed. Most participants chose to not fill out the survey in its entirety,

even though they were instructed to do so. Most respondents may have not taken the surveys










seriously, or they might have thought that the survey was a bit long or hard for them to complete,

especially the survey sections for relative importance/satisfaction of each factor on each roadway

type.

Given these results, it was decided to also conduct an in-field survey data collection effort.

With the assistance of FDOT personnel, postage-paid mail-back surveys were distributed at

several agricultural inspection stations. For this survey effort, a reduced version of the surveys

was prepared in hopes of obtaining a good response rate and a higher level of completion of the

surveys. The response rate statistic from the FTDC survey data indicated that the survey sections

for relative importance/sati sfaction of each factor required the most amount of respondent burden

among all the sections in the survey. Most survey participants did not have problems or

difficulties filling out the last page on applicability of a single performance measure. Thus, the

survey was reduced to three pages in length; the first page for background information, the

second page for relative importance/sati sfaction of each factor on one of the 3 roadway types

(freeways, arterials, or two-lane highways), and the last for applicability of a single performance

measure. This survey (one with freeway related factors on the second page) is included in

Appendix K. A total of 4000 postage-paid surveys were supplied to the FDOT for distribution at

the inspection stations. A total of 1000 surveys were distributed at each of four inspection

stations.

The four stations were located at northern border locations, and included:

* Pensacola station on I-10 (West)
* Suwannee station on I-10 (near Live Oak)
* Hamilton station on I-75
* Yulee station on I-95

The previous focus groups indicated that most truck drivers' concerns are on freeways or

two-lane highways as alternatives to freeways. A significant portion of their trips are also on









freeways, distance-wise as well as time-wise. Thus, of the 1000 surveys distributed at each site,

500 surveys were freeway related (i.e., the second page asked questions about relative

importance and satisfaction of factors affecting freeway quality of service), 250 surveys were

two-lane highway related, and 250 were arterial related. The research team was informed by

FDOT that only about 3 percent of all the truck drivers traveling on the Interstates could bypass

the agriculture stations. These vehicles generally include pre-pass users, car haulers, and empty

flat beds. The surveys were distributed during the week of August 14-20, 2006. A return date

of September 1st was put on the surveys. Most surveys were returned by that date, but some

were still received up to a month later. Surveys received after October 1st were not included in

the data set because data reduction was complete at that point and analysis had started. A total of

311 surveys were returned by October 1st, yielding a response rate of 7.8%. As shown in Table

3-2, the response rate for the freeway-related surveys was much higher than those for arterials or

two-lane highways. It implies that this particular population of truck drivers was more

concerned or interested about transportation services on freeways. The completion rate of these

surveys was much higher than those from the FTDC. It is likely that the reduced length and

greater flexibility of when they could fill them out contributed to this.

According to the advantages and disadvantages of the various survey methods reviewed

and discussed in Chapter 2, the phone-based survey method was considered as the most efficient

way to survey truck company managers. However, this method was not well suited for this study

because it does not allow the respondents to complete the second section of the survey without

bias. This section requires the respondents to present their perceptions on the relative

importance/sati sfaction of each factor among a total of ~18-19 factors. It would be possible only

when the respondents can skim through all the factors simultaneously.









With these considerations, truck company managers were surveyed with two different

approaches. One method was the in-field survey during the FTDC event. The other method was

the postage-paid mail-back survey with a number of trucking companies listed in the FTA

membership directory.

A survey data collection effort for truck company managers was made on June 1, 2006 at

the Fairgrounds in Tampa, where the truck driving championship was taking place (Figure 3-2).

There was a modest crowd present on this day, with a mix of company managers, other truck

company employees, as well as family and friends. The research team set up a table with several

chairs, covered by a large umbrella. A survey poster describing the purpose and background of

this research proj ect was placed on the table to solicit participation of truck company managers

(Figure 3-3). The announcers for the driving competition also made several announcements

throughout the day about our effort. Eleven truck company managers filled out the surveys, with

seven of them answering most of the survey questions as directed (Appendix I for the truck

company manager survey form).

For the second survey data collection effort of truck company managers, potential

recipients were identified from the FTA membership directory, which contains a list of all carrier

and allied members with their affiliation and contact information. All the Florida-based carriers

in the directory (a total of 180 trucking companies) were considered as potential survey

participating trucking companies. All the allied members were removed from consideration

because they do not operate trucks. They support trucking companies by offering various

services such as accident investigation, insurance, truck driver training, truck sales or rentals, etc.

In the FTA directory, the carriers can be divided into 5 chapters (geographical locations), or 6

conferences (carrier types). A total of 50 Florida-based carriers were selected for the survey










using the stratified random sampling procedure that preserves the proportional composition of all

the Florid-based carriers in terms of conference and chapter (a total of 30 strata).

A reduced version of the manager survey (one with freeway related factors on the second

page) was used (Appendix L). The background section of the manager survey is different from

that of the driver survey. For the rest of the sections, the same sets of roadway, traffic, and/or

control factors are presented, but questioned differently. For instance, the relative importance of

each factor on operating cost, on-time performance, and truck drivers' trip quality were asked

respectively in the manager survey, while the importance and satisfaction of each factor on truck

trip quality were asked in the driver survey.

Five postage-paid surveys were mailed to each of the 50 FTA carrier members (5 x 50 =

250 surveys) with a cover letter asking them to distribute the surveys to the managers

(transportation, safety, dispatch, logistics, etc.) at their companies (Appendix M for the cover

letter). As can be seen in the cover letter, it was mentioned that this study was being conducted

with the cooperation of the FTA. It was hoped that this would encourage managers to respond to

the survey. Follow-up phone contacts were made to each of the companies to whom surveys

were mailed (about one week later) to confirm that they received them and ask for their support

in filling them out. Initially, a total of 300 surveys were prepared in case some the 50 carriers do

not receive the surveys. Of 50 additional surveys, 5 surveys were resent to one carrier that did

not receive the survey and 45 surveys were mailed to 9 different newly-selected carriers. As a

result, 27 surveys were obtained from all the Florida regions except for West Florida. Nineteen

surveys were from common (for-hire) carriers, 6 from private carriers, and 2 from tank carriers.

Table 3-3 shows the number of carriers that participated in the survey from each conference by










each chapter, out of the total of 59 carriers. Table 3-4 indicates the number of surveys received

from each conference by each chapter, out of the total of 300 surveys.

3.2.4 Data Reduction

It turned out that most respondents completed only parts of the surveys and there was also

some evidence that some of the respondents did not pay enough attention to fi11 out the surveys

as directed. The length, or the perceived complexity, of the survey may have kept the

participants from completing them correctly. Considering that the purpose of this survey is to

potentially identify ways of improving the working environments of the participants, there is no

other source of non-response bias expected in this study. Thus, given an overall low response

rate, it was decided that in addition to complete surveys, partially completed surveys would be

utilized for data analyses once surveys with unreliable responses were screened out according to

certain criteria. The usability of survey responses for data analysis was determined for each

survey question. Survey data filtering criteria were developed to assess the validity of the survey

responses (Appendix N). However, for a few surveys, it was still difficult to determine their

validity with the filtering criteria. The research team had to make decisions in those cases

through discussions. In that process, the researchers were stricter on the validity of survey

responses collected during FTDC than of the postage-paid surveys due to the larger respondent

burden to complete the surveys distributed at the FTDC event. Tables 3-5, 3-6, and 3-7 illustrate

the number of valid surveys out of total number of returned truck driver surveys from the FTDC

event, the postage-paid surveys, and both combined. Table 3-8 describes the number of valid

surveys out of the total number of returned manager surveys from all the survey data collection

sources.









3.2.5 Data Analysis

A variety of statistical methods were used to analyze the survey data to satisfy the

obj ectives of this study. All the variables observed in the survey were first summarized by

descriptive statistics and/or (cumulative) frequency distribution to investigate overall

distributions of the responses. The following five statistical methods were applied to the survey

data for further analyses: Exploratory Factor Analysis (EFA); Games-Howell multiple

comparison test; Kruskal-Wallis test; Mann-Whitney test; and chi-squared test. Exploratory

Factor Analysis (EFA) was performed with Relative Importance Score (RIS) of all the factors to

look for common latent factors that are important to truck trip quality. Each pair of the mean

importance of hypothetical truck LOS performance measures for each roadway type was

compared with Games-Howell tests to find out which performance measure is more important

than others statistically. Potential relationship between each background characteristics of the

respondents and their perceptions on each potential truck LOS performance measure was

investigated through nonparametric tests such as Kruskal-Wallis test and Mann-Whitney test.

Chi-squared test, in particular, was performed to discover potential relationship between each

background characteristics of the respondents and their preference on each truck driving time of

day.

3.2.5.1 Descriptive statistics

Two most important descriptive statistics were calculated for all the interval-rating and

ratio-scale questions; mean and standard deviation. A central tendency of each variable was

presented by its mean, while its standard deviation was presented to measure a typical degree of

spread of the variable. For nominal (categorical) data, (cumulative) frequency distributions were

presented to describe overall distribution of the survey responses. Histograms and scatter plots

were used to display the results.









3.2.5.2 Exploratory factor analysis (EFA)

Exploratory Factor Analysis (EFA) is a statistical method to explain a large number of

metric variables in terms of their common, underlying dimensions, that is, latent factors. Latent

factors are unobserved entities that influence a set of measures (variables) and are extracted from

the correlations among the variables. EFA results provide how the variables are grouped into a

small number of latent factors from the respondents' perspectives and how much each latent

factor is correlated with each of the variables. Factor analysis is a multivariate interdependence

technique with which all the variables are simultaneously considered, rather than multiple

regression, discriminate or canonical analyses, in which one or more variables are explicitly

considered as dependent variables (Hair, et al., 2005). This technique is often used in social

science research to summarize the data by identifying a set of latent factors that influence each

set of variables, which correlate highly amongst each other. The following seven steps are

typically taken to perform EFA (Field, 2005).

Calculation of correlation matrix: The starting point of factor analysis is to create a

correlation matrix, in which the inter-correlations between each pair of observed variables are

presented. A basic assumption of a factor analytic procedure is that a group of variables that

significantly correlate with each other do so because they are measuring the same common,

underlying dimension. Thus, if a group of variables seem to correlate highly with each other

within the group, but correlate very badly with variables outside of that group, they are

considered to well measure a common, underlying dimension, which is called a 'latent factor'.

The ultimate obj ective of the EFA procedure is to reduce the correlation matrix to a factor matrix,

which provides the correlations between the latent factors and each of the observed variables (i.e.,

factor loadings). This can be done by various factor extraction methods that are introduced later

in this section.









Factorability investigation: To identify common underlying dimensions that explain

the patterns of collinearity among the variables, the observed variables have to be inter-

correlated enough to be factorable, but they should not correlate too highly. It is important to

avoid multicollinearity (i.e., variables that are very highly correlated) and singularity (i.e.,

variables that are perfectly correlated) as these would cause difficulty in determining the unique

contribution of the variables to a factor.

The communality of a variable is the sum of the loadings of the variable on all extracted

factors. This represents the proportion of the variance in that variable that can be accounted for

by all extracted factors. Thus, if the communality of a variable is high, the extracted factors

account for a large proportion of the variables' variance. This means that this particular variable

is reflected well via the extracted factors, and hence the factor analysis is reliable. When the

communalities are not high, the sample size has to be large enough to compensate for this. To

examine whether the sample is large enough to elicit a meaningful factor solution, the Kaiser-

Meyer-Olkin (KMO) measure of sampling adequacy is used. The KMO statistic varies between

0 and 1. A value of 0 represents that the sum of partial correlations is large relative to the sum of

correlations, indicating that factor analysis is likely to be inappropriate. A value close to 1

represents that patterns of correlations are relatively compact, so factor analysis should yield

distinct and reliable factors. Typically, a KMO statistic value of greater than 0.5 is acceptable to

perform factor analysis.

Bartlett' s test of spherity tests the null hypothesis that the original correlation matrix is an

identity matrix. When the correlation matrix is an identity matrix, there would be no correlations

between the variables, eliminating the need for a factor analytic procedure. Thus, this test has to










be significant. A significance value (p value) less than 0.05 is usually necessary to justify the

factor analytic procedure.

Multicollinearity and singularity problems can be detected by investigating the

determinant of the correlation matrix. Usually, it is considered to not be a problem if the

determinant is greater than 0.00001.

Extraction of factors: A variety of statistical methods have been developed to extract

latent factors from an inter-correlation matrix of the observed variables. They include the

principal component extraction method, the principle axis extraction method, the maximum

likelihood extraction method, the unweighted least-squares extraction method, the generalized

least squares extraction method, the alpha extraction method, and the image factoring extraction

method.

The two most commonly used extraction methods are the principle component and

principle axis methods. There are three types of variance in the variables: common, specific, and

error. Common variance is the variance in a variable which is shared with all other variables in

the analysis. Specific (unique) variance is the variance associated with only a specific variable.

Error variance is the inherently unreliable random variation. The principle component method

Einds latent factors that maximize the amount of total variance (i.e., sum of common, specific,

and error variances) that is explained, while the principle axis method Einds latent factors that

maximize the amount of common variance that is explained. The main difference between the

two types of methods lies in the way the communalities are used. Communality of a given

variable is the proportion of its variance that can be accounted for by extracted factors. In the

principle component method, it is assumed that all the communalities are initially one (unities

are inserted in the diagonal of the correlation matrix). That is, the total variance of the variables









can be accounted for by the extracted factors. On the other hand, with the principle axis method

the initial communalities are not assumed to be one (it does assume error variance). They are

usually estimated by taking the squared multiple correlations of the variables with other variables.

These estimated communalities are then represented on the diagonal of the correlation matrix,

from which the eigenvalues are determined and factors are extracted.

Theoretically, when the analyst is primarily concerned about determining the minimum

number of factors needed to account for the maximum portion of the variance represented in the

original set of variables, and has prior knowledge suggesting that specific and error variance

represent a relatively small portion of the total variance, the principle component method is

appropriate. In contrast, when the primary objective is to identify the latent dimensions or

constructs represented in the original variables, and the analyst has little knowledge about the

amount of specific and error variance and therefore wishes to eliminate these variances, the

principle axis method is appropriate. Practically, however, both methods are widely used and the

solutions generated by each usually do not differ significantly.

Figure 3-4 shows an example of a path diagram for an exploratory factor analytic model

by the principle component extraction method. As discussed, this method finds latent factors

that maximize the amount of total variance (of the observed variables) that is explained, and it is

assumed that there is no error variance. An EFA model is constructed in the way that series of

regression equations are set up to summarize its configuration. Equation 3.2 describes an EFA

model by the principle component extraction method. Each of the variables is defined as a linear

combination of the factors (i.e., sum of the products of each latent factor variable and the factor

loading of each observed variable on the corresponding factor). Results from the EFA include

derived loadings of each variable on each factor and calculated factor scores for each subj ect on


































Az, = a factor loading of the ih Variable on the fh latent factor


F, = fh latent factor variable (a common, underlying dimension, j= 1 to k, k = number of

observed variables). These correspond to Fl, F2, and F3 in Figure 3-4.

Determination of number of factors to be retained: The maximum number of factors

that can be extracted is equal to the number of observed variables. However, the purpose of an

EFA is to adequately explain a relatively large number of variables with a small number of

factors. Thus, the analyst seeks to identify the smallest number of factors that explain a

considerably large amount of variance in the observed variables.

There are several criteria for the number of factors to be extracted, but these are just

empirical guidelines rather than an exact quantitative solution. In practice, most factor analysts

seldom use a single criterion to decide on the number of factors to extract. Some of the most

commonly used guidelines are latent root, percentage of variance, and scree test criteria. With

the latent root (eigenvalue) criterion, only the factors having an eigenvalue greater than one are


each factor. The factor scores are a composite measure that can be used for subsequent analyses.

When an orthogonal rotation method is used, the scores of the factors can be considered to be

independent of each other, and thus can be used as explanatory variables in a multiple regression

analy si s.


k
V, = [ ( A z, x F, )
]=1


(3 .2)


re,

ith observed variable (i = 1 to k, k = number of observed variables). These correspond to Vl,

V2, ..., V10 in Figure 3-4.


where

V =









retained. It should be noted that the total sum of eigenvalues from the data is equal to the total

number of variables and the variance of a single variable is considered as the eigenvalue of one.

Thus, the rationale for the eigenvalue criteria is that any individual factor should account for at

least the variance of a single variable if it is to be retained for interpretation. The percentage of

variance criterion is a different approach. Using this method, the cumulative percentages of the

variance extracted by successive factors is the criterion. It is common in social science research

to consider a solution that accounts for at least 60% of the total variance as a satisfactory solution.

Another common approach is the scree test criterion. The scree test is derived by plotting the

latent roots (eigenvalues) against the number of factors in their order of extraction. The scree

plot illustrates the rate of change in the magnitude of the eigenvalues for the factors. The rate of

decline tends to be fast for the first few factors, but then levels off. The "elbow", or the point at

which the curve bends, is considered to indicate the maximum number of factors to extract.

Rotation of factors: Once the number of factors to be retained is decided, the next

logical step is to determine the method of rotation. The fundamental theorem of factor analysis

is invariant within rotations. That is, the initial factor matrix is not unique. There are an infinite

number of solutions, which produce the same correlation matrix, by rotating the reference axes

of the factor solution. A primary obj ective of the rotation is to make each variable load highly

on only one factor and have nearly zero loadings on the other factors. This simplifies the factor

structure and helps to achieve a more meaningful and interpretable solution. The simplest case

of rotation is an orthogonal rotation in which the angles between the reference axes of factors are

maintained at 90 degrees. Thus, there is no correlation between the extracted factors. A more

complicated form of rotation allows the angle between the reference axes to be other than a right

angle and is referred to as an oblique rotation. The factors are allowed to be correlated with each










other in this type of rotation. Orthogonal rotation procedures are more commonly used than

oblique rotation procedures because researchers often try to obtain an independent set of factors

to clarify the meaning of each factor. Three maj or orthogonal approaches are varimax,

quartimax, and equamax rotation methods, and two maj or oblique approaches are direct oblimin

and promax rotation methods.

Criteria for the significance of factor loadings: If there are variables that load highly

on two or more factors, or do not load highly on any factor, they are excluded from a factor

solution because it is not clear which factors) has an influence on the variables and this

ambiguous relationship between the factors and the variables blur the interpretation of a factor

solution. Whether a factor loading of a variable is significant or not depends on the sample size,

the total number of observed variables, and the total number of extracted factors. The larger the

sample size, the smaller the loading is considered to be significant. The larger the total number

of variables, the smaller the loading is considered to be significant. The larger the number of

factors, the larger the size of the loading on latent factors is considered to be significant. As a

rule of thumb, factor loadings greater than f 0.5 are considered to be significant when the

sample size is 120 or more, and factor loadings greater than f 0.65 are considered to be

significant when the sample size is 70 or more.

Naming of factors: Once the latent factors to be retained, and the variables associated

with each of those factors are identified, the analyst attempts to assign some meaning to the

factors based on the patterns of the factor loadings. It should be noted that the factor loadings

represent the correlation, or linear association, between a variable and the latent factors. Thus,

the analyst makes a determination as to what an underlying factor may represent, investigating

all the variables' loadings on the factor in terms of their size and sign. The larger the absolute










magnitude of the factor loading for a variable, the more important the variable is in interpreting

the factor. The sign of the loadings also need to be considered in labeling the factors. It may be

important to reverse the scoring of the negatively worded items in Likert-type instruments to

prevent ambiguity. That is, in Likert-type instruments some items are often negatively worded

so that high scores on these items actually reflect low degrees of the attitude or construct being

measured.

As the importance level of each traffic, roadway, and/or control variable was evaluated

on a 7-point interval-rating scale, EFA was applied to search for a set of latent factors that

accounts for the patterns of collinearity among the variables. The extracted factors and their

correlations with observed variables reflect in what respect each variable contributes to truck trip

quality and the degree to which the importance of each variable is explained by the underlying

latent factors. The principle component extraction method was used to find the set of latent

factors that accounts for the maximum amount of variance of the observed variables. The results

of the EFA presented a set of extracted latent factors, the percent of trace, that is, the portion of

the total variance (of the observed variables) that is explained by each latent factor, and the

correlations between each observed variable and latent factor (i.e., factor loadings). It was not

possible to apply Confirmatory Factor Analysis (CFA) or Structural Equation Modeling (SEM)

statistical techniques to the survey data because no previous hypothetical model construct exists

for the truck trip quality issue.

3.2.5.3 Multiple comparison test

When an Analysis of Variance (ANOVA) test verifies that the means of multiple variables

(i.e., more than two) are statistically different, multiple comparison procedures are widely used

to determine which means are different from one another. Fisher's Least Significance Difference

(LSD), Tukey's W, Student-Newman-Keuls (S-N-K), and Duncan's tests are often used (Ott and










Longnecker, 2006), assuming each sample from the groups is selected from a normal population

with an equal variance. However, if it is not reasonable to assume equal variance, pair-wise

multiple comparison procedures such as Tamhane's T2, Dunnett's T3, Games-Howell, or

Dunnett' s C tests can be used (Dunnett, 1980).

As the importance of each hypothetical performance measure was evaluated in a 7-point

interval rating scale, the Games-Howell pair-wise multiple comparison test was performed to

investigate if the differences among the mean importance levels of the performance measures are

statistically significant. Normal Quantile-Quantile (Q-Q) plots showed that each sample was

approximately normally distributed (Figure 3-5), but the equal variance assumption did not hold

according to Levene' s tests (Levene, 1960).

Thus, it was necessary to perform the multiple comparison tests with methods that do not

rely on the equal variance assumption. The Games-Howell test was performed among the

multiple comparison tests with unequal variances to investigate every possible statistical

difference in the pair-wise mean comparisons among the groups. The Games-Howell test is the

most liberal, meaning that differences between group means are identified as being significant

more readily with this test than the other tests. The Games-Howell test is a modification of S-N-

K procedure, using the q test statistic (i.e., studentized range statistic). Equation 3.3 is used to

calculate the test statistic, reflecting heterogeneous variances and sample sizes in the error term

in the denominator.



q = (3.3)






where,










q = studentized range test statistic


pu, = calculated mean of the group i

s, = calculated standard deviation of the group i

nl = sample size of the group i

Equation 3.4 is also used to calculate the degrees of freedom for each pair-wise comparison to

adjust the error term. The calculated test statistics are compared with critical q values with the

corresponding degrees of freedom found in the studentized range tables.





df = n n(3.4)



n -1 n -1


where,

df = degrees of freedom for each pair-wise comparison

3.2.5.4 Non-parametric test

The survey respondents' backgrounds were collected from various question types (i.e.,

ratio-scale, discrete choice, forced ranking, etc.) and their perceptions on the importance of each

hypothetical performance measure were evaluated on a 7-point interval rating scale. Thus,

ordered probit modeling technique (Greene, 2000) was first applied in an attempt to explore

respondents' backgrounds that may explain the variance in their perceptions. However, with the

ordered probit modeling, only a small number of potential explanatory variables were found to

be statistically significant, resulting in generally poor model fits. The Kruskal-Wallis test and

Mann-Whitney test (non-parametric version of Analysis of Variance (ANOVA) and t-test) were










applied to the data. The use of parametric ANOVA and the t-test was not appropriate even

though it is more powerful, because it was not reasonable to assume normality and equal

variances with the data.

The Kruskal-Wallis test (Conover, 2001) is a non-parametric one-way ANOVA by ranks.

It is used with k independent groups, where k is equal to or greater than 3, and measurement is at

least ordinal. The sample sizes across the groups can vary because the samples are independent.

The null hypothesis is that the k samples come from the same population. The alternative

hypothesis states that at least one sample comes from a different population. Following H test

statistic is used to test the hypothesis.


H = 12 '2 3 x (N + 1) (3.5)
N (N + 1) nl


where,

H= Kruskal-Wallis test statistic

N = total sample size

k = number of independent samples

RI = sum of the ranks of group i

n, = sample size of group i

The calculated Htest statistic approximately follows a Chi-Squared distribution (X2) with k-1

degrees of freedom. Thus, for a specified value ofa the null hypothesis is rejected when

calculated H value exceeds the critical value of X2 for k-1 degrees of freedom.

The Mann-Whitney test (Conover, 2001) is a non-parametric t-test by ranks. It is used

specifically with two independent groups, and measurement is also at least ordinal. The sample

sizes between the two groups can vary because the samples are independent. The null hypothesis




Full Text

PAGE 1

1 IDENTIFICATION OF PREFERRED PE RFORMANCE MEASURES FOR THE ASSESSMENT OF TRUCK LEVEL OF SERVICE By BYUNGKON KO A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2007

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2 2007 Byungkon Ko

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3 To the graduate students of the University of Florida

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4 ACKNOWLEDGMENTS It may not have been possible for me to co mplete my Ph.D. study without the advice or assistance of those who have supported me throug hout my life in Gainesville. They are my family members, friends, and University of Flor ida faculties and administrative staffs. Although I cannot adequately describe how blessed I am to have been with them, I would like to show my gratitude to them with this opportunity. I am especially grateful to my parents, wife, and four siblings. Thei r consistent care and support have been a true driving factor for me to succeed in preparing for my future as well as earning the degree. I would like to express my a ppreciation to Dr. Scott S. Wa shburn who has served as my academic advisor for my Ph.D. study. He supported me financially and his exceptional knowledge and wisdom has provoked academic curios ity that encouraged me to enhance my research ability onto a higher level. I also would like to thank my Ph.D. supe rvisory committee members for their time and effort to provide me with invaluable advice fo r my research. Dr. Lily Elefteriadou has guided me into the right direction academically a nd also provided many gr aduate students with convenient study environment as the director of Transportation Research Center. Dr. Yafeng Yin lent me his acute insight on mathematical expressions and recommended better ways to organize the dissertation. Dr. Siva Srinivasan he lped me to improve statistical data analysis and modeling part of my work. Dr. Joseph Geunes in the Industrial Engineer ing department served as an external committee member. His consis tent concerns and advi ce about my work has become a motivation to improve overall performance of my study.

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5 My friends in Korea or Gainesvi lle have always cheered me up for my successful career at the University of Florida. I will never forget the time and memory I have shared with them. I certainly appreciate the assistance of Univer sity of Florida faculties and staffs. They have always tried to answer my academic or admi nistrative questions in sp ite of their tight time schedules.

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6 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ........10 LIST OF FIGURES................................................................................................................ .......13 LIST OF TERMS.................................................................................................................. .........14 ABSTRACT....................................................................................................................... ............17 CHAPTER 1 INTRODUCTION..................................................................................................................20 1.1 Background................................................................................................................. ......20 1.2 Problem Statement.......................................................................................................... ..21 1.3 Study Objectives and Tasks..............................................................................................23 1.4 Organization of the Dissertation.......................................................................................25 2 LITERATURE REVIEW.......................................................................................................26 2.1 Truck Level of Service.....................................................................................................26 2.2 User-Perceived Level of Service......................................................................................30 2.3 Trucking Community Focus Groups and Surveys............................................................37 2.4 Florida Trucking Community Focus Gr oup Participant Recruitment Sources................52 2.5 Trucking Community Survey Methods............................................................................54 3 RESEARCH APPROACH.....................................................................................................59 3.1 Focus Group Sessions.......................................................................................................61 3.1.1 Participant Recruitment..........................................................................................62 3.1.2 Participant Selection...............................................................................................63 3.1.3 Focus Group Questionnaires..................................................................................66 3.1.4 Conducting Interviews............................................................................................68 3.1.5 Summary of Discussions........................................................................................69 3.2 Survey Studies............................................................................................................. .....70 3.2.1 Question Types.......................................................................................................71 3.2.1.1 Interval-rating questions...............................................................................71 3.2.1.2 Ratio-scale questions....................................................................................72 3.2.1.3 Forced-ranking questions.............................................................................73 3.2.1.4 Discrete-choice questions.............................................................................74 3.2.1.5 Fixed-sum questions.....................................................................................76 3.2.2 Survey Development..............................................................................................76 3.2.3 Data Collection.......................................................................................................82

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7 3.2.4 Data Reduction.......................................................................................................88 3.2.5 Data Analysis..........................................................................................................89 3.2.5.1 Descriptive statistics.....................................................................................89 3.2.5.2 Exploratory factor analysis...........................................................................90 3.2.5.3 Multiple comparison test..............................................................................97 3.2.5.4 Non-parametric test......................................................................................99 3.2.5.5 Chi-squared test..........................................................................................101 3.3 Truck LOS Measurement...............................................................................................103 3.3.1 Truck LOS Service Measures...............................................................................103 3.3.1.1 Single performance measure approach......................................................104 3.3.1.2 Multiple variable approach.........................................................................104 3.3.2 Truck LOS Estimation Model..............................................................................105 3.3.2.1 Data collection............................................................................................105 3.3.2.2 Statistical modeling....................................................................................107 4 FOCUS GROUP STUDY RESULTS..................................................................................115 4.1 Perceptions of Truck Drivers..........................................................................................115 4.1.1 Truck Route and Departure Time Selection.........................................................116 4.1.2 Factors Affecting Truck Trip Quality...................................................................118 4.1.2.1 Factors affecting truck trip quality on freeways.........................................118 4.1.2.2 Factors affecting truck trip quality on urban arterials................................122 4.1.2.3 Factors affecting truck trip quality on two-lane highways.........................126 4.1.2.4 Factors affecting truck trip quality on hub facilities..................................128 4.1.3 Improvement Priority of Transportation Services................................................128 4.1.4 Truck Delivery Schedule Reliability....................................................................129 4.2 Perceptions of Truck Company Managers.....................................................................130 4.2.1 Truck Route and Departure Time Selection.........................................................131 4.2.2 Factors Affecting Truck Trip Quality...................................................................133 4.2.2.1 Factors affecting truck trip quality on freeways.........................................133 4.2.2.2 Factors affecting truck trip quality on urban arterials................................136 4.2.2.3 Factors affecting truck trip quality on two-lane highways.........................140 4.2.2.4 Factors affecting truck trip quality on hub facilities..................................141 4.2.3 Truck Delivery Schedule Reliability....................................................................141 4.3 Perceptional Difference between Truck Drivers and Truck Company Managers..........143 5 SURVEY DATA ANALYSIS RESULTS...........................................................................145 5.1 Backgrounds of the Participants.....................................................................................145 5.1.1 Truck Driver Participants.....................................................................................145 5.1.2 Truck Company Manager Participants.................................................................146 5.2 Perceptions on the Relative Importa nce of Each Factor on Freeways...........................147 5.2.1 Relative Importance of Each Factor.....................................................................148 5.2.2 Applicability of Single Hypothetical Performance M easure to Estimate Truck Trip Quality................................................................................................................152 5.3 Perceptions on the Relative Importance of Each Factor on Urban Arterials..................155 5.3.1 Relative Importance of Each Factor.....................................................................155

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8 5.3.2 Applicability of Single Hypothetical Performance M easure to Estimate Truck Trip Quality................................................................................................................159 5.4 Perceptions on the Relative Importance of Each Factor on Two-Lane Highways.........162 5.4.1 Relative Importance of Each Factor.....................................................................162 5.4.2 Applicability of Single Hypothetical Performance M easure to Estimate Truck Trip Quality................................................................................................................166 5.5 Relative Importance of Each Category of Factors to Quality of a Truck Trip...............168 5.5.1 Relative Importance of Each Ca tegory of Factors for Freeways.........................169 5.5.2 Relative Importance of Each Categor y of Factors for Urban Arterials................169 5.5.3 Relative Importance of Each Categor y of Factors for Two-lane Highways........170 5.6 Comparisons of the Importance of Each Factor Category on Various Roadway Facilities..................................................................................................................... .......170 5.7 Perceptions on the Improvement Priority of Various Roadway Types..........................171 5.8 Relationships between Truck Drivers Backgrounds and Their Perceptions on the Applicability of Each Hypotheti cal LOS Performance Measure......................................172 5.8.1 Truck Drivers Backgrounds that Explain Their Perceptions on the Importance of Each Hypothetical LOS Performance Measure on Freeways............173 5.8.2 Truck Drivers Backgrounds that Explain Their Perceptions on the Importance of Each Hypothetical LOS Pe rformance Measure on Urban Arterials...175 5.8.3 Truck Drivers Backgrounds that Explain Their Perceptions on the Importance of Each Hypothetical LO S Performance Measure on Two-Lane Highways...................................................................................................................178 5.9 Perceptions on Truck Driving Environment by Time of Day........................................180 5.9.1 Current and Preferred Truck Driving Time of Day..............................................181 5.9.2 Relationships between Truck Drivers Backgrounds and Their Perceptions on Preferred Truck Driving Time of Day.......................................................................182 5.10 Other Transportation Service Issues for Truck Drivers................................................186 5.10.1 Freeway Truck Operations.................................................................................186 5.10.2 Urban Arterial Truck Operations........................................................................187 5.10.3 Two-Lane Highway Truck Operations...............................................................187 6 CONCLUSIONS AND RECOMMENDATIONS...............................................................239 6.1 Conclusions................................................................................................................ .....239 6.1.1 Quality of a Truck Trip on Freeways...................................................................240 6.1.2 Quality of a Truck Trip on Arterials.....................................................................241 6.1.3 Quality of a Truck Trip on Two-Lane Highways.................................................242 6.1.4 Improvement Priority of Various Transportation Facilities for Trucks...............244 6.1.5 Improvement Priority of the Factors on Each Transportation Facility for Trucks........................................................................................................................244 6.1.6 Preference on Truck Driving Time of Day...........................................................245 6.1.7 Relationships between Truck Drivers Backgrounds and Their Perceptions on Truck Trip Quality.....................................................................................................246 6.1.8 Overall Effectiveness of Research Approach.......................................................247 6.2 Recommendations...........................................................................................................248 6.2.1 Truck LOS Estimation Model Development........................................................248 6.2.1.1 Truck LOS on freeways.............................................................................248

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9 6.2.1.2 Truck LOS on arterials...............................................................................251 6.2.1.3 Truck LOS on two-lane highways.............................................................252 6.2.2 Transportation Service Improveme nt for the Trucking Community....................252 6.2.3 Trucking Community Surveys.............................................................................253 APPENDIX A COOPERATION REQUEST LET TER SENT TO FTA.....................................................257 B FOCUS GROUP INSTRUCTION.......................................................................................259 C GUIDELINES FOR FOCUS GROUP PARTIC IPANT SELECTION SENT TO FTA......261 D FOCUS GROUP MODERATORS GUIDE........................................................................265 E FOCUS GROUP PARTICIPANTS BA CKGROUND SURVEY RESULTS....................268 F TRUCK DRIVER FOCUS GROUP BACKGROUND SURVEY FORM..........................272 G TRUCK COMPANY MANAGER FOCUS GROUP BACKGROUND SURVEY FORM........................................................................................................................... ........275 H TRUCK DRIVER SURVEY FORM....................................................................................278 I TRUCK COMPANY MANAGER SURVEY FORM.........................................................285 J IMPROVEMENT PRIORITY SCORE................................................................................292 K POSTAGE-PAID TRUCK DR IVER SURVEY FORM......................................................294 L POSTAGE-PAID TRUCK COMP ANY MANAGER SURVEY FORM............................298 M POSTAGE-PAID MANAGER SURVEY COVER LETTER.............................................302 N SURVEY DATA FILTERING CRITERIA.........................................................................304 LIST OF REFERENCES.............................................................................................................307 BIOGRAPHICAL SKETCH.......................................................................................................311

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10 LIST OF TABLES Table page 2-1 Comparison of Survey Methods........................................................................................58 3-1 Survey Development........................................................................................................108 3-2 Postage-Paid Truck Driv er Survey Response Rates........................................................109 3-3 Survey Participation of the Selected Carriers by Each Conference and Chapter............110 3-4 Survey Collection by Each Conference and Chapter.......................................................111 3-5 FTDC Truck Driver Survey Data Usability.....................................................................111 3-6 Postage-Paid Truck Driver Survey Data Usability..........................................................111 3-7 Overall Truck Driver Survey Data Usability...................................................................112 3-8 Overall Truck Company Manager Survey Data Usability...............................................112 3-9 Current HCM Service Measures used for LOS Determination.......................................114 5-1 Truck Driver Survey Responde nt Background Summary Statistics................................188 5-2 Additional Truck Driver Survey Re spondent Background Summary Statistics..............189 5-3 Truck Company Manager Survey Re spondent Background Summary Statistics...........190 5-4 Additional Truck Company Manager Survey Respondent Background Summary Statistics..................................................................................................................... ......191 5-5 Truck Drivers Perceptions on Each Fact or on Truck Travel Quality of Service on Freeways....................................................................................................................... ...192 5-6 Managers Perceptions on Relative Im portance of Each Factor on Freeways................193 5-7 Exploratory Factor Analysis Results...............................................................................194 5-8 Importance of Each Factor on Truck Travel Quality of Service on Freeways................195 5-9 Truck Drivers Perceptions on Applicab ility of Single Performance Measure to Determine Truck Travel Quality of Service on Freeways...............................................196 5-10 Truck Company Managers Perceptions of Relative Importance of Each Truck Driving Condition on Freeways for trucking business....................................................196 5-11 Games-Howell Post Hoc Test Results.............................................................................197

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11 5-12 Truck Drivers Perceptions of Each F actor on Truck Travel Quality of Service on Urban Arterials................................................................................................................ .198 5-13 Managers Perceptions on Relative Importa nce of Each Factor on Urban Arterials.......199 5-14 Exploratory Factor Analysis Results...............................................................................200 5-15 Importance of Each Factor on Truck Trav el Quality of Service on Urban Arterials......201 5-16 Truck Drivers Perceptions of Appli cability of Single Performance Measure to Determine Truck Travel Quality of Service on Urban Arterials.....................................202 5-17 Truck Company Managers Perceptions of Relative Importance of Each Truck Driving Condition on Urban Arterials for trucking business..........................................202 5-18 Games-Howell Post Hoc Test Results.............................................................................203 5-19 Truck Drivers Perceptions of Each F actor on Truck Travel Quality of Service on Two-Lane Highways........................................................................................................204 5-20 Managers Perceptions of Relative Im portance of Each Factor on Two-Lane Highways....................................................................................................................... ..205 5-21 Exploratory Factor Analysis Results...............................................................................206 5-22 Importance of Each Factor on Truck Travel Quality of Service on Two-Lane Highways....................................................................................................................... ..207 5-23 Truck Drivers Perceptions of Appli cability of Single Performance Measure to Determine Truck Travel Quality of Service on Two-Lane Highways............................208 5-24 Truck Company Managers Perceptions of Relative Importance of Each Truck Driving Condition on Two-Lane Highways for trucking business..................................208 5-25 Games-Howell Post Hoc Test Results.............................................................................209 5-26 Definitions of Independent Vari ables used in Statistical Tests.......................................217 5-27 Definitions of Independent Vari ables used in Statistical Tests.......................................218 5-28 Kruskal-Wallis and Mann-W hitney Test Statistics..........................................................219 5-29 Kruskal-Wallis and Mann-W hitney Test Statistics..........................................................220 5-30 Kruskal-Wallis and Mann-W hitney Test Statistics..........................................................221 5-31 Kruskal-Wallis and Mann-W hitney Test Statistics..........................................................222 5-32 Kruskal-Wallis and Mann-W hitney Test Statistics..........................................................223

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12 5-33 Kruskal-Wallis and Mann-W hitney Test Statistics..........................................................224 5-34 Mann-Whitney Test Statistics..........................................................................................225 5-35 Kruskal-Wallis and Mann-W hitney Test Statistics..........................................................226 5-36 Kruskal-Wallis and Mann-W hitney Test Statistics..........................................................227 5-37 Mann-Whitney Test Statistics..........................................................................................228 5-38 Chi-Squared Test Statistics 1........................................................................................232 5-39 Chi-Squared Test Statistics 2........................................................................................233 5-40 Chi-Squared Test Statistics 3........................................................................................233 5-41 Chi-Squared Test Statistics 4........................................................................................234 5-42 Other Factors Affecting Tr uck Trip Quality on Freeways...............................................235 5-43 Other Drivers Behavior Affec ting Truck Trip Quality on Freeways.............................236 5-44 Other Factors Affecting Truck Trip Quality on Urban Arterials.....................................237 5-45 Other Factors Affecting Truck Tr ip Quality on Two-Lane Highways............................238 6-1 Truck Trip Quality of Service Determinants on Freeways..............................................254 6-2 Truck Trip Quality of Service Determinants on Arterials...............................................255 6-3 Truck Trip Quality of Service Determinants on Two-Lane Highways...........................255 6-4 Top Six Factors in the Need for the Improvement on Each Roadway Type for Trucks.256 E-1 1st Truck Driver Focus Group Partic ipants Background Survey Results.......................268 E-2 1st Truck Company Manager Focus Group Pa rticipants Background Survey Results...269 E-3 2nd Truck Driver Focus Group Partic ipants Background Survey Results......................270 J-1 Improvement Priority Scores...........................................................................................292

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13 LIST OF FIGURES Figure page 3-1 Research Approach..........................................................................................................108 3-2 Truck Driving Competition Course.................................................................................109 3-3 Survey Table Setup at Truck Driving Competition.........................................................110 3-4 Example of a Path Diagram for an EF A Model by Principle Component Extraction Method......................................................................................................................... ....113 3-5 Normal Q-Q Plot of the Importance of Ease of Obtaining Useful Traveler Information on Freeways................................................................................................113 5-1 Relative Importance of Each Factor Category for Freeways...........................................210 5-2 Relative Importance of Each Fact or Category for Ur ban Arterials.................................211 5-3 Relative Importance of Each Fact or Category for Two-lane Highways..........................212 5-4 Relative Importance of Roadway Cond itions on Different Roadway Types..................213 5-5 Relative Importance of Traffic Cond itions on Different Roadway Types......................214 5-6 Relative Importance of Other Drivers Behavior on Different Roadway Types.............215 5-7 Improvement Priority of Various Road way Facilities for Truck Trip Quality................216 5-8 Truck Drivers Current and Pref erred Truck Driving Times of Day...............................229 5-9 Truck Driving Time of Day Preference of Current Users...............................................230 5-10 Truck Company Managers Preferen ce on Truck Driving Times of Day.......................231

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14 LIST OF TERMS AASHO American Association of State Highway Officials (now AASHTO) AASHTO American Association of State Hi ghway and Transportation Officials ANOVA Analysis of Variance ATA American Trucking Association BTS Bureau of Transportation Statistics CATI Computer-Aided Telephone Interview CBD Central Business District CBR Citizen Band Radio CDL Commercial Drivers License CEDR Center for Economic Development and Research CMS Changeable Message Sign CMV Commercial Motor Vehicle EFA Exploratory Factor Analysis ES202 Quarterly Census of Employment and Wages FDHSMV Florida Department of Highway Safety & Motor Vehicles FDOT Florida Department of Transportation FHWA Federal Highway Administration FIHS Florida Intrastate Highway System 511 Americas traveler information phone number FTA Florida Trucking Association FTDC Florida Truck Driving Championship GPS Global Positioning System HAR Highway Advisory Radio HCM Highway Capacity Manual

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15 HIS Highway Information System IRI International Roughness Index KYTC Kentucky Transportation Cabinet LOS Level of Service LSD Fishers Least Significance Difference multiple comparison test LTL Less-than-TruckLoad NATSO National Association of Truck Stop Operators NHS National Highway System NTA National Truckers Association NYC New York City ODOT Oregon Department of Transportation OTA Ontario Trucking Association P&D Pick-up and Delivery PCE Passenger Car Equivalent PSI Present Serviceability Index PSR Present Serviceability Rating PTBF Percent-Time-Being-Followed PTSF Percent-Time-Spent-Following QOS Quality of Service Q-Q plot Quantile-Quantile plot RCI Roadway Characteristics Inventory RMI Relative Maneuverability Index RV Recreational Vehicle SIS Strategic Intermodal System SMC Safety Management Council

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16 S-N-K Student-Newman-Keuls multiple comparison test SPRPC Southwestern Pennsylvania Regional Planning Commission SUV Sport-Utility Vehicle TIS Traveler Information System TL TruckLoad 3PL Third Party Logistics TTS Transportation Technical Services, Inc TU Text Unit TUB Text Unit Block UDC Urban Distribution Centre UHM University of Hawaii at Manoa VMS Variable Message Sign XM radio a satellite radio service

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17 Abstract of Dissertation Presented to the Graduate School of the Un iversity of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy IDENTIFICATION OF PREFERRED PE RFORMANCE MEASURES FOR THE ASSESSMENT OF TRUCK LEVEL OF SERVICE By Byungkon Ko May 2007 Chair: Scott S. Washburn Major: Civil Engineering Commercial trucks, the leading transportation mode for freigh t movement, are vital to our economy and peoples lives. The importance of th is mode has become greater as the demand for just-in-time delivery, lower inventory, electroni c commerce, and Less-than-TruckLoad (LTL) shipping has increased. This study was conducted under the Fl oridas Strategic Intermodal System (SIS) plan for the Florida Department of Transportation (FDOT) in an effort to better understand the needs of the Florida trucking comm unity by investigating their perceptions of truck trip quality on various roadway facilities. The current Highway Capacity Manual (HCM) provides analytical methods to evaluate performance levels of transportation facilities and estimate Level Of Service (LOS) perceived by the users. The HCM methodologies yield a single LOS value for all th e users in a traffic stream. However, due to the unique size and operating charac teristics of trucks, it is possible that truck mode users perceive LOS on various roadway faci lities based on different criteria from those of the other mode users. This study focused on id entifying the determinants of LOS perceived by truck mode users and measuring their relative importance based on which truck LOS estimation methodologies should be developed.

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18 Three focus group sessions (two with truck drivers and the other one with truck company managers) were held to elicit the factors affec ting truck LOS on various transportation facilities and the relative importance of those factors were evaluated through a follow-on survey study with a larger audience. A to tal of 459 truck drivers and 38 truck company managers responded to the written surveys collected at Florida Tr uck Driving Championship (FTDC) event or the postage-paid mail-back surveys dist ributed at four agri cultural inspection stations. The survey responses were analyzed with various statisti cal methods such as de scriptive statistics, Exploratory Factor Analysis (EFA), multiple co mparisons of the means, non-parametric tests, and chi-squared tests of independence. The quality of a truck trip generally was f ound to depend on three issues: travel safety; travel time; and physical and psychological driv ing comfort. Truck drivers showed more concerns about the driving comfort, while truc k company managers were more concerned with travel time. The travel safety aspect of a truck trip was a majo r concern for both groups. Speed Variance and Pavement Quality were the two most important dete rminants of truck trip quality on freeways. Truck trip quality on arterials was a function of multiple factors including Pavement Quality, Turning Maneuvers, Speed Variance, and Traffic Density. PercentTime-Being-Followed (PTBF), Percent-Time-S pent-Following (PTSF), Travel Lane and Shoulder Widths and their Pavement Quality were identified as truck LOS determinants on twolane highways. Among many factor s contributing to the quality of a truck trip, Passenger Car Drivers Road Etiquette and Knowledge about Truck Driving Characteristics, Traffic Congestion, and Frequency and Timing of Constr uction Activities were perceived to be in significant need of improvement regardless of the type of roadway facility. Truck travel restrictions (e.g., truck lane re strictions) on freeways, inadequa te curb radii and poor traffic

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19 signal coordination on arterials, and inappropriate shoulder widt h and condition and poor nighttime lighting condition on two-lane highways were also considered to be greatly in need of improvement. The results of this study provide the FDOT with guidelines and recommendations to develop truck LOS estimation met hodologies to effectively assess how well it is addressing the needs of freight transportation on the state roadway system a nd offer transportation service providers and other stakeholders valuable insights for the prio ritization of transportation improvement projects for co mmercial truck traffic.

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20 CHAPTER 1 INTRODUCTION 1.1 Background Transportation services for freight are vita l to our economy and peoples daily lives. Various kinds of freight are relocated daily in and out of Florida thr ough several transportation modes such as truck, train, ship, and plane. Am ong all the modes, truc ks moved 76.3 percent of freight value, 79.4 percent of tonnage, and 66.9 percent of ton-miles of the total freight originating in Florida in 2002, according to th e Commodity Flow Survey (CFS) (Bureau of Transportation Statistics, 2004). This makes th e truck mode the leading mode for freight movement in the U.S. Truck traffic is also e xpected to grow significan tly throughout the State of Florida over the next co uple of decades, along with increasing population, as noted in the Freight Analysis Framework (FAF) (U.S. Department of Transportation and Federal Highway Administration, 2002). In addition, the increas ed demands for just-i n-time delivery, low inventory, electronic commerce (e-commerce), Le ss-than-TruckLoad (LTL) shipping, and more distributed manufacturing, make it necessary for transportation service providers and other stakeholders to look for ways to better accommod ate truck traffic on existing roadway systems. Floridas Strategic Intermodal System (S IS) plan was established by the Florida Department of Transportation (FDOT) in 2003 to support trans portation facilities that are necessary for Floridas rapidly growing and ev er changing economy (FDOT, 2003). The main goal of the SIS program is to provide safe, effici ent, and convenient tran sportation services for all types of transportation users on the most critic al transportation facilities in Florida. The SIS facilities were selected based on national or industry standards fo r measures of transportation and economic activity. The SIS includes three different types of facilities; hubs, corridors, and intermodal connectors. Hubs are ports and terminals, corridors are highways, rail lines, or

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21 waterways that connect major markets, and conn ectors are highways, rail lines, or waterways that connect hubs and corridors. As one of its first research projects unde r the SIS program, this study has been conducted for FDOT to better understand the needs of the Florida trucking community by investigating its pe rceptions and opinions about tran sportation services for trucks on various state roadway facilities. 1.2 Problem Statement The Highway Capacity Manual (HCM) (Trans portation Research Board, 2000) provides analytical methods to estimate capacity and ke y performance measures for a wide variety of roadway facilities. The HCM also provides met hods for translating perf ormance measure values into a Level Of Service (LOS) value. Level Of Service (LOS) is a qualitative measure used to describe general operating conditi ons within a traffic stream. Th e HCM uses a scale of A to F for LOS, with A correspond ing to excellent operating conditions and F corresponding to extremely poor operating conditions. These LOS values are typically based on performance measures such as speed and travel time, freedom to maneuver, traffic in terruptions, and comfort and convenience, and are intended to reflect drivers perception of those conditions. The selected performance measures, upon which LOS is based, referred to in the HCM as service measures, can vary from one roadway facility to another. The methods of the HCM have been widely adopted by the transportation community as the primary means of measuring system performance. They are often used as valuable to ols for most transportation agencies to monitor or improve the performance levels of existing transportation facilities or plan for future transportation facilities. The current HCM utilizes engineering-based measures such as density, delay, and average travel speed in evaluating the performance level of transportation facilities. These measures are conceptually reasonable and easy to apply, but are not necessarily consistent with the measures

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22 that the actual roadway users base their per ception of the facility performance level on. Furthermore, for the roadway facilities in the HC M (such as arterial, hi ghways, and freeways), the HCM methods result in a single LOS value for the entire traffic stream ; that is, they do not distinguish between different mode s within the same traffic str eam. The methods are generally designed to establish LOS for the passenger vehicle mode. However, commercial trucks (hereafter referred to as just trucks) are uniqu e in their size and operating characteristics among various vehicle types in traffic st reams. Trucks require more space and time to maneuver due to their difference in size, weight, off-tracking, acceleration, and braki ng. So they are often used as design vehicles for roadway facilities, and sometimes subject to various restrictions such as lane, route, speed, or time-of-day. In addition, most truck drivers op erate their trucks primarily for business purposes and spend a si gnificant amount of time drivi ng, while the trip purposes of other vehicle users vary and they usually drive less frequently. Due to these special characteristics of truck operations on transportation facilities, it is possible that truck mode users perceive LOS on various roadway facilities based on different criteria from those of the other mode user s. However, the current HCM (Transportation Research Board, 2000) only accounts for the mode of trucks in the traffic stream through the heavy vehicle adjustment factor (HVf ). This adjustment factor is based upon the percentage of heavy vehicles (e.g., trucks, buses, or recreationa l vehicles) in the traffic stream and their passenger car equivalents (PCEs). A PCE is th e number of passenger cars expected to have same effect on a traffic stream as a single heavy vehicle under specific roadway and traffic conditions. The PCE is used to convert a traffic stream flow ra te with some portion of heavy vehicles into an equivalent one with passenger cars only. So, although the current HCM procedures account for the effects of trucks in the traffic stream, the overall methodology does

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23 not provide any exclusive LOS evaluation criteria or procedures to estimate how well a transportation facility accommodates truck traffic in a traffic stream. 1.3 Study Objectives and Tasks The primary objective of this study was to dete rmine the roadway, traffic, and/or control factors important for Floridas trucking community and estimate the relative significance of each factor on overall quality of truck operation on the SIS facili ties. Based on information obtained about the perceptions of truck mode users, th is study provides the FDOT with guidelines and recommendations to develop actual LOS estim ation methodologies to effectively assess how well it is addressing the needs of freight transportation on the state roadway system. These efforts provide transportation se rvice providers, researchers, a nd transportation agencies with valuable insights as to what should be prio ritized to improve truck operations on various transportation facilities. To achieve this objective, it was essential to obtain input from both truck drivers and truck company managers, as these two groups represent the major stakeholders with regard to truck operations. For this study, data collection wa s confined to focus group and written survey methods. The major tasks performed to complete this study are described below and presented in their chronological order: Task 1: Literature review. A literature review was performed to facilitate and enhance this study with regard to the following five ar eas: 1) truck LOS studies; 2) user-perception based LOS studies; 3) trucking community focus group and/or survey studies 4) Florida trucking community focus group participan t recruitment sources, and 5) trucking community survey methods. Task 2: Preparation for focus group sessions. In advance of the focus group meetings, the methodology for conducting the focus group st udies was determined. This includes

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24 clarification of the purpose and scope of the interviews, part icipant selection guidelines, participant recruitment plans, moderator selection, development of participant background surveys, moderators guide regarding focus gr oup questionnaires and instructions, focus group discussion recording, and methods of analysis. Task 3: Focus group sessions of truck drivers and/or truck company managers. Several focus group meetings were held with select ed participants to explore the perceptions and opinions of the trucking community on the fact ors important to truck operations on various transportation facilities. Task 4: Summary of focus group findings. The focus group discussions were summarized to identify the factors affecting qua lity of a truck trip on each facility and by different user groups (truck drivers and truck company managers). The summary results were then used as inputs to a follow-on survey. Task 5: Survey development. Survey studies were intended to reconfirm the focus group findings with a broader audience and to meas ure the relative importance of each factor quantitatively. Two different survey forms were prepared; one for truck drivers and the other for truck company managers. Both forms contain questions about partic ipants personal and working backgrounds and their perceptions abou t the relative importance of each factor identified in the previous focus group studies. Task 6: Survey data collection. Truck driver surveys and truck company manager surveys were distributed at multiple locations wi th the assistance of the FDOT and the Florida Trucking Association (FTA). Task 7: Analysis of survey data. Collected surveys were analyzed statistically to determine the relative importance of each factor on truck trip quality and/or overall trucking

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25 business. Relationships between the participan ts backgrounds and their perceptions were also explored. Task 8: Study results and recommendations. The factors influenc ing truck trip quality and their relative importance were determined based on the focus group and survey results. Potential performance measures to estim ate truck LOS were recommended by each transportation facility type. 1.4 Organization of the Dissertation The remainder of this dissertation is as follows Chapter 2 provides a summary of relevant literature. Chapter 3 describes the study methods and detailed steps taken to complete this study. The study results are presented in Chapters 4 and 5. Chapter 4 discusses focus group study findings and Chapter 5 discusses the results of sta tistical analyses on the survey data. In chapter 6, conclusions are presented and recommendations are made with regard to roadway facility improvement priorities for truck LOS considerati ons. Potential truck LOS service measures are also presented.

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26 CHAPTER 2 LITERATURE REVIEW This chapter describes previous research effort s by other researchers related to this study, conceptually or methodologically. It provides descriptions of the research methodologies and results of those studies, and a summary of the fi ndings. First, the studies regarding truck LOS development are presented. They contain some implications on potenti al truck LOS service measures. Other research efforts to identify and test potential LOS service measures on a specific roadway facility are described in the next section of this chapter. They provide some insights about how to elicit the perception of tr avelers and convert the information to develop user-perceived LOS. The final section reviews previous focus group and/or survey studies of the trucking community. Potential sources of focus group recruitment and characteristics of various survey methods were separately summarized to help develop the methodol ogies suitable for the purpose and scope of this study. 2.1 Truck Level of Service A study by Washburn (2002) explored and dem onstrated the development of a potential method to assess level of service specific to the truck mode. The current LOS performance measure for a freeway segment (density) applies to the traffic stream as a whole, regardless of vehicle types, although they typically have very different physical and op erating characteristics from passenger cars in the traffic stream. To assess the level of service of heavy trucks on a freeway segment, a relative density concept was proposed. Relative density for trucks could be obtained by dividing density for th e traffic stream by a Relative Maneuverability Index (RMI), which is a function of the ratio of percentage of free-flow speed of trucks to percentage of freeflow speed of passenger cars. Under low density, ideal conditions, percenta ge of free-flow speed for both trucks and cars should be at or near 100%, yielding th e same LOS for both modes (e.g.,

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27 A). Likewise, under high density, the percentage of free-flow speed for both trucks and cars would both be very low, producing the same LO S for both modes (e.g., F). At densities in between, the percentage of free-flow speed for truc ks will probably be less that that for cars, resulting in higher relative densit y values for trucks, which can be referenced to the current HCM density thresholds for determining LOS. Ot her possible measures for determining LOS for trucks have also been introduced in the document; acceleration noi se, passing opportunities, percent-time spent-followi ng, and heavy vehicle factor. A study by Hostovsky and Hall (2004) focused on th e perceptions of tract or-trailer drivers on the factors affecting quality of service on freeways. A focus group meeting was conducted for this study with 7 Road Knight Team memb ers at the annual convention of the Ontario Trucking Association (OTA) in November, 2001. Th e participants were asked what makes for a good or bad trip for themselves to drive truc ks on a freeway. The discussion was then transcribed to be analyzed through NUD.IST (N on-numerical Unstruct ured Data Indexing Searching and Theorizing), an indu stry standard qualitative data analysis software program using five criteria (intensity, releva nce, frequency, universality, and emphasis). The OTA, Canadian Trucking Association (CTA), and American Truc king Association (ATA) showed an agreement with the studys conclusions. The study results we re organized in this paper by the number of Text Unit Blocks (TUB), which represent the num ber of times a certain theme was discussed by the participants. With respect to freeway conditi ons (TUB = 34), the factor s identified were road surface (pavement condition, snow removal, road de bris), lane restrictions, signage, and lane width and markings. As far as traffic conditions (TUB = 22) are concerned, congestion, steady flow, and maneuverability were discussed. Attitudes toward other vehicles (TUB = 27) were another big

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28 issue mentioned in the meeting. Courteous in terplay by automobile dr ivers was found to be important for them to have a good trip. In terms of safety (TUB = 3), road etiquette, weather, and rubbernecking were considered to influence their trip quality. The participants also mentioned the hazard of aggressive driving and road rage (TUB = 23) and regionally different perceptions of trip quality (TUB = 23) with respect to such various factors as level of congestion, courteous interplay by other driv ers, driving skills of other drivers around them in snow condition, and low speed limits. With all the fi ndings mentioned above, the authors indicated that what really matters to truck drivers in term s of traffic condition is traffic flow, not traffic density, which is a service measure used in the curre nt HCM. In this resp ect, steadiness of traffic flow and an ability to drive at a comfortable operating range of highway speeds without much braking or accelerating were considered to be important for truck drivers perception of the quality of service. A study by Hall, et al. (2004) developed a met hod to evaluate the access routes for large trucks between intermodal or other truck-tr affic-generating sites and the National Highway System (NHS) and used it to prioritize and progr am the truck access routes for improvement. The study began with identifying clusters of truc k-traffic generating facilities based on total trucks per day, distance to NHS, and recommen dations on the sites with truck access problems by transportation planners from highway district and area development district offices. Then telephone surveys were conducted wi th the operators/managers of th e selected sites to identify problem routes to be evaluated in this study. Each route was evaluated with respect to three types of features: subjective, point, and conti nuous. Using the Kentuc ky Transportation Cabinet (KYTC) statewide Highway Information System (H IS) database and some field observations, the following characteristics of each route were coll ected: 1) point features: curve off-tracking,

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29 maximum safe speed on horizontal curves, stoppi ng sight distance, turnin g radii; 2) continuous features: grade, lane width, shoulder, route LO S. The point and continuous elements on each route were ranked as preferred, adequat e, and less than adequate based on the recommendations in AASHTO (American Associ ation of State Highway and Transportation Officials)s A Policy of Ge ometric Design of Highways and Streets and Roadway Design Guide. The features subjectively evaluated by the researchers include clear zone, pavement condition, accident history, parki ng, pedestrian traffic, land use conflicts, dust/noise issues, and so on. The rankings of point and continuous el ements were converted to a relative urgency rating by assigning a relative weight with respec t to truck volume and section length. Problem truck-points and problem truck-miles were calcu lated based on these ra nkings to prioritize the problem routes. They were adjusted by predom inant subjective features, where appropriate. Finally, with all the evaluation da ta of each problem route, the re searchers inspected the routes and graded them on a subjective scale of 1 to 10 (10 represents good access for trucks). All the three studies presented in this section focused on developing methods to evaluate operating conditions on a specific roadway type for truck traffic. Washburn (2002) devised the RMI concept to evaluate truck LOS on freew ays, based on a hypothetical reasoning that maneuverability of trucks is inferior to that of passenger cars because of unique physical and operating characteristics of trucks. A study by Hostovsky and Hall (2004) f ound that traffic flow is more important than traffic density to truck drivers perception of trip quality on freeways. The two above mentioned studies imply that tr affic density, the current HCM service measure for freeway facility, may not reflect the perceptio n of truck drivers adequately. Steady traffic flow and maneuverability may be more importa nt to truck drivers than traffic density, considering the large size, heavy weight, low accel eration and deceleration capability of trucks.

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30 Hall, et al. (2004) developed a methodology for evaluating large truck access routes between NHS and truck-traffic generating facilities to prio ritize and program the routes for improvement. Overall quality of each route for truck access was determined by many various factors. They included geometric adequacy measures, pavement condition, clear zone, accident history, traffic LOS, and other subjective measures such as pa rking, pedestrian traffi c, land use conflicts, dust/noise issues, etc. As this study investigated so many different character istics of each route, it may not be appropriate to identify one or tw o performance measures to effectively estimate quality of an access route for trucks. 2.2 User-Perceived Level of Service A study by Hall, et al. (2001) conducted two focu s groups (one with five members, and the other with seven members) to explore what free way users perceive as important to level of service on freeways. Focus group participants we re chosen by a snowball sample selection process to identify commuters going from the c ity of Toronto, Ontario, Canada to McMaster University (the research cente r) in Hamilton, Ontario Canada. The participants were all university faculty members in various departments, and were selected to ensure that all the participants traveled the same st retch of freeway so that all knew about the situations that were being discussed and all had had relatively simila r experiences. Both groups were moderated by the same member of the research team to ensu re consistency in tech nique across groups. The following general questions were asked in the discussions; Tell me about the usual freeway r oute you take when you are commuting. Have your perceptions of the drive changed over time? I want you to talk about both the ideal or best trip youve ever made and also the worst trip ever.

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31 The factors important to percep tions of trip quality were listed with respect to the number of Text Units (TUs) in which each theme was me ntioned in the two discussions, as follows; Travel time (TUs = 103 (6%)) Density/maneuverability (TUs = 86 (5%)) Road safety (TUs = 81 (5%)) Commuter information and communication (TUs = 65 (4%)) Civility (TUs = 38 (2%)) Photo radar (TUs = 31 (2%)) Weather (TUs = 23 (1%)) The participants indicated travel time as the fi rst thing to describe th e quality of particular trips. Having time constraints on their arrival from a trip was thought to greatly increase the stress involved in the trip. Time spent commu ting was also considered to be lost time. Maneuverability also came up in the discussions in light of travel time, accident avoidance, adequate gaps between cars, w eaving in and out of traffic, and changing lanes. Another important issue for participants was safety. They were concerned about accidents not only in terms of congestion but also because of the risk to their personal safety. Materials from trucks and other debris on the road were concerns for many participants, and the effects of sport-utility vehicles (SUVs) and minivans on other drivers vi sibility were also mentioned in the safety perspectives. Having adequate information about what was happening to traffic while they were on the road was also important to the particip ants. This issue was discussed along with the possibility for them to avoid traffic congestion by finding alternative routes. Some other factors were mentioned occasionally in the focus groups as well; driver civility, the use of photo radar, and weather and its effect on dr iving conditions. Passengers perc eptions also were different from drivers with respect to travel time, mainly due to their ability to undertake activities other than driving. The participants indicated th at travelers on the bus would have different perspectives; they usually do not notice the traffic c onditions when they are in the bus. The

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32 authors also indicated that the pa rticipants do not think of freeways in terms of distinct segments classified by the HCM (basic segments, ramp -freeway junctions, and weaving segments), proposing further studies about LOS breakpoints. The factors that affect traveler-perceived quality of service on rural freeways were explored using in-field surveys of motorists tr aveling on rural freeways (Washburn, et al., 2004). A total of 233 responses from a good mix of re spondents were collected at several different locations along I-75 and the Turnpike in Florida. The researchers deci ded to perform an infield survey data collection effort, as opposed to a mail-back survey or something similar, as this provided the advantage of obtaining input while the specific characteris tics of the travelers trip were still fresh in their mind. The surveys at the rest stops, whic h offered respondents a $2 food discount voucher, also showed a good overall productivity (about six respondents per hour). The importance of 16 traffic and roadway factors was ranked by respondents on a sevenpoint ordinal scale (1 being not at all important, and 7 being extr emely important). The six top ranked factors based on mean scores and/or percent time in top three are as follows: Ability to consistently maintain your desi red travel speed (mean = 6.09, % time in top three = 64.3%) Ability to change lanes and pass other vehicl es easily (mean = 5.79, % time in top three = 33.3%) Smooth and quiet road surface condition (mean = 5.68, % time in top three = 20.3%) Ability to travel at a speed no less than th e posted speed limit (mean = 5.58, % time in top three = 33.0%) Other drivers etiquette/courtesy (m ean = 5.38, % time in top three = 22.1%) Infrequent construction zones (mean = 5.37, % time in top three = 23.4%) A probit modeling technique was used to di scover any possible relationships between personal characteristics and current trip informa tion of the respondents, and their opinions about

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33 the top four ranked factors (from 1 to 4 above). Fo r the first factor, the mo re highly educated an individual, the more likely they were to rank th is factor highly. Th e respondents that were driving a tractor-trailer, or with higher estimated average speeds, were also more likely to rank this factor highly. For the second factor, the respondents that had highe r education or with higher estimated average speeds, were more likely to rank this factor highly. Travelers that indicated they were experiencing mostly very dense or dense traffic flow conditions on their trip were less likely to rank this factor highly. For the third factor, tr avelers with higher income were more likely to rank this factor highly. Travel ers who indicated their trip purpose was other, that is, neither business nor leisure, were less lik ely to rank this factor highly. Travelers that were on a business trip and very familiar with the route they were traveling were also less likely to rank this factor highly. For the fourth factor older people were more lik ely to rank this factor highly. On the other hand, the resp ondents that were stri ctly drivers on the trip or driving a large auto (pickup truck, SUV, miniva n, or full-size van) were less lik ely to rank this factor highly. The authors indicated that in addition to dens ity, there are some factors that are just as important to travelers, such as speed variance and percent of fre e-flow speed. Some non-traffic performance measures were also found to be im portant through the study, such as pavement quality, and driver etiquette. A web-based survey was conducted at the Univ ersity of Hawaii at Manoa (UHM) in 2005 to see how road users evaluate signalized in tersection LOS (Zhang and Prevedouros, 2005). Email messages with group-specific survey hyperlinks were sent to selected samples asking them to fill out the motorist survey on-line. Six groups comprise the sample; random UHM students, faculty, and staff, Hawaii engineering and transp ortation professionals, drivers from other groups in Hawaii, and drivers from the mainland U.S. The response rate was 12.2%, 16.0%, 18.9% for

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34 UHM students, faculty, and staff respectively, yielding 2,017 usable surveys. The survey questions focused on factors important to drivers at signalized intersec tions, driver opinions on protected left-turn signals for va rious sizes of intersections, an d trade-offs between perceived safety risk and delay. Analysis of variance (ANOVA) was applied to dependent variables with ratioor interval-level data to assess if there were significan t differences among the independent groups. The influence of several independent variables such as gende r, age, and driving experience on the dependent variables were investigated simu ltaneously through ANOVA (Analysis of Variance). The ten factors important to drivers at signaliz ed intersections were evaluated with a fivepoint ordinal scale (1 being not important, and 5 being extremely importa nt). The factors are listed as follows in order of decreasing importance. 1. Traffic signal responsiveness 2 Ability to go through the intersection within one cycle of light changes 3 Availability of left turn only lanes and protected left turn si gnal for vehicles turning left 4 Pavement markings for se parating and guiding traffic 5 Availability of a protected left tu rn signal for vehicles turning left 6 Availability of left turn only lanes for vehicles turning left 7 Pavement quality 8 Waiting time 9 Heavy vehicles such as trucks and buses that are waiting ahead 10 Availability of right-turn only la nes for vehicles turning right The respondents were also asked to rate th e difficulty in making a left turn without a protected left turn signal and th eir preference for a protected left turn signal, on a scale form 1 (not difficult or not preferred) to 5 (extremely difficult or extr emely preferred). The difficulty increases with intersection size, and female driver s perceived more difficulty than male drivers. The preference also increases with intersection size, and female drivers prefer protected left turn signals more than male drivers. As high as 91% of the respondents stat ed that they much or

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35 extremely prefer a protected left turn signal at an intersection wher e left turn vehicles have to cross three lanes of opposing through traffic. Safety was stated to be three to six times more important than delay, depending on the type of conflict; drivers turn ing left are more concerned than drivers going through, and pedestrians are more concerned than drivers turning left. Female drivers, non-risk-prone drivers, and pedestrians valued safety more than delay to a significantly greater degree. Drivers and pedestrians would be willing to wait a longer time at signalized intersections in exchange for protected left turn signals. The average additi onal delay to exchange for protected left turn signal is 20.6 sec, 25.4 sec, and 27.2 sec for dr ivers going through, driver s turning left, and pedestrians respectively. Overall findings suggest that the current measure, delay, should be supplemented by a number of quantifiable attributes of signali zed intersections for determining a LOS that represents road user perceptions. Some potential performance measures for LO S on rural freeways were evaluated with microscopic traffic simulation (Kim, et al., 2003). Although density-based LOS is thought to be well suited to the assessment of urban freeways, some have questioned whether density is also a proper indicator of the quality of service on ru ral freeways because drivers may think more in terms of psychological or emotional comfort fo r rural freeways, which generally serve long, high-speed trips and rarely experience more th an moderate congestion levels. Acceleration noise, number and duration of cruise control applications, and percent time spent following (PTSF) were examined as some possible means of determining Level of Service (LOS) of rural freeway sections as they have at least an intuitiv e relationship to the concept of driver comfort. The three measures were estimated for a hypothe tical section of rural freeway. Acceleration

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36 noise is a measure of the physical turbulence in the traffic stream. The instantaneous acceleration of each vehicle at each second is computed from the speed differential with respect to the previous second, and the standard deviatio n of acceleration for each vehicle is computed over the 6,000 feet segment. A post-processing routine was written to analyze the simulation results and infer the application and release of cr uise control. Cruise control was applied to a vehicle when it had been traveling at its desired speed for three or more seconds. Cruise control was released when the vehicle began to decel erate for any reason. For PTSF, a vehicle is considered to be following its leader if the relationship between its position and speed with respect to the leader places it within the car-f ollowing influence zone. A nonlinear relationship between acceleration noise and traffic volume s hows that acceleration noise increases more rapidly in the lower volume range and levels off as volume increases. It is also observed that the nonlinear effect is most pronounced on single-lane freeways and diminishes as the number of lanes increases. The nonlineari ty for proportion of time with cr uise control applied was only discernible to any degree in the case of the si ngle-lane freeway. The nonl inearity for number of cruise control applications was too pronounced to be useful. The nonlinearity for PTSF was more pronounced than that for acceleration noi se, and there was no discernible difference between two and three lane freeways. Based on the investigation of th e nonlinear relationship shapes between level of volumes and three candi date measures by number of lanes, the authors concluded that a nonlinear relationship between acceleration noise and traffic volume on rural freeways could be used directly as the basis fo r a new set of LOS criteria for rural freeways, given further studies focusing on driver opinions behavior, and field measurement to support this finding. The other two measur es also have conceptual appeal but used individually would not be suitable as the basis for determining LOS on a rural freeway.

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37 The above described studies concerning the expl oration of user-perceiv ed level of service performance measures identifie d some interesting findings. The study by Hall, et al. (2001) found from two focus group meetings that tota l travel time is the most important LOS determinant for travelers on a freeway. It was note d that passengers did not consider the travel time as important as drivers did because they can do something else other than driving. It would be worth investigating how freeway drivers pe rceptions on importance of total travel time can be influenced by their trip purposes. The st udy by Washburn, et al. (2004) found from surveys of rural freeway travelers that consistently main taining desired travel speed is more important than ability to change lanes and pass other vehicles easily or t raveling at or not less than the posted speed limit. This implied that the cruise control factor is more important than the density factor or percent of free-flow speed fact or. Other important factors included pavement condition, other drivers etiquette /courtesy, and infrequent cons truction zones. A web-based survey study by Zhang and Prevedouros (2005) f ound that the current serv ice measure of delay for signalized intersections is less important than a number of other factors such as traffic signal responsiveness, abilit y to go through the intersecti on within one cycle of li ght changes, etc. It may be possible that drivers are more sensit ive to stop and go conditi ons than actual delay experienced at signalized intersections. Kim, et al. (2003) identified acceleration noise as a potential service measure for rural freeways due to its desirable properties of relating to drivers sense of safety and comfort and its non-linear relationship with traffic volume. Many previous studies of this type verified th at some current service measures do not reflect the perception of the users adequately. 2.3 Trucking Community Focus Groups and Surveys With increasing importance on urban freight mo bility, Morris, et al. (1998) conducted a study on cost, time, and barriers related to moving freight into New York Citys Central Business

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38 District (CBD). The study cons isted of 13 focus groups with di fferent industry-sector senior logistics executives and freight mobility interviews with logistics, transportation, or distribution managers. The research team cooperated with the Center for Logistics and Transportation Executive Committee in the development of a fo cus group moderators guide, recruitment of focus group participants, pretest of the guide, and the guidance of the freight mobility interview. Each focus group was scheduled at partic ipants convenience, allowing the use of speakerphones, and included 2 participants to e xplore the issues in de pth within a 2-hour time frame. Participants also received the focus group guide and informational material before the meetings so that they would have time to think about the issues in advance. At the meetings, a flip chart listing seminal points for focus group questions and probes, a nd a large CBD area map were displayed to reinforce atte ndees attention to topics. Tran sportation barriers listed in the order of greatest frequency of mention in all the meetings were; congestion, inadequate docking space, curb space for commercial vehicles, secur ity, and excessive ticketing of high-profile companies. A freight mobility interview followe d with logistics, distri bution, or transportation managers, who were recruited with the help of th e previous focus group participants or from the membership lists of Council for Logistics Ma nagement and the Center for Logistics and Transportation. An on-site interview was pla nned initially, but the response was generally negative. So phone interviews were requested to be scheduled after send ing letters containing a freight mobility interview, the study goals and purpose, and focus group findings. Follow-up calls were made 2 weeks after the letters were sent. But due to the lower than expected response rate, another attempt was made with foll ow-up calls made 1 week after sending letters, resulting in 51 completed interviews. Data on co sts, time, distance, product types, and major barriers in the movement of freight into Manhattans CBD by shippers and carriers were

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39 collected. Major barriers to freight mobility id entified through the interviews were; widespread congestion, security, physical constr aints, and institutional barriers. In a study by Veras, et al. (2005), various type s of efforts such as focus group studies, indepth interviews, and internet su rveys were made to obtain the perceptions on challenges and the potential of off-peak deliveries to congested areas. The target ed groups for this study included private stake holders; shippers, receivers, third party logistics (3PLs), trucking companies, and warehouses. Off-peak deliveries to the Ne w York City (NYC) metropolitan region were proposed to avoid traffic congest ion and lack of parking spot s hampering daytime commercial vehicle deliveries. The strategy may reduce the price accompanied by congestion and illegal parking, pollution, and frustration to the public, but it may pose additional costs to shippers, receivers, and carriers mainly due to workers necessary for night shifting. It may also involve regulatory and legal impediment. On January 20, 2004, two focus groups with truck dispatchers were conducted in NYC as part of the Evaluation Study of the Port Authority of New York and New Jerseys Value Pricing Initiative. The focu s group findings indicated that both shipper costs and receiver costs would increase due to the need of night time workers, regardless of feasible toll discounts, or compensation due to traveling hour s. In-depth interviews with 17 stakeholders of various types (trucking companies, shippe rs, receivers, lobbyist s, trucking-warehouse combination companies, and shipping-truck ing-warehouse combination companies) were performed to explore the issues further. Comp anies with trucking opera tions prefer to make deliveries during off-peak hours due to congestio n and parking problems, but they are often discouraged to do it becaus e of the recruitment of night-shift workers, and security of drivers, receivers, and products. The shi ppers were natural on the subject of off-peak deliveries. They do not care when their products are delivered only if the products get to th e destination on time.

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40 The two receivers operating restaurants stated th at off-peak deliveries would help reduce severe parking problems, but fresh food is typically not delivered during off-peak hours. Thirty-three internet surveys were gathered from private stakeholders (shippers, receivers, 3PLs, trucking companies, warehouses) in several states. Se venty-five percent of warehouses and 70% of shippers, 3PLs, and trucking companies were pe rforming off-peak deliveries. Many others at least have considered using this alternative. None of the shippers was currently performing offpeak deliveries, but majority of them indicated th at they could do them if they are provided with some incentives. Reasons given by stakeholde rs for performing off-peak deliveries included faster deliveries, faster turn-a round times, and lower costs. Reasons given for not performing off-peak deliveries included businesses not be ing open at those times, customers not accepting off-peak deliveries, and employee costs. With respect to incentives to implement off-peak deliveries, most stakeholders were interested in all of the tax incenti ves and subsidies. A significant number of trucking companies and 3PLs indicated that they would be responsive to a request from many receivers to do off-peak de liveries. The author also brought up carriercentered policies, receivers as key stumbling blocks, financial incentives, and targeted major traffic generators. Two studies focused on deve loping an effective methodol ogy to survey the freight community (i.e., shippers and carriers) in Or egon state by comparing various survey data collection methods (Lawson and Riis, 2001 and La wson, et al., 2002). The researchers did an extensive literature review on the previous truc king community survey studies and experimented with several data collection methods to find out th e most effective method to survey shippers and carriers. Traditional structured interviews c onducted in person or by telephone have a high response rate from purposeful sampling procedure. These surveys can focus on broad issues and

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41 are useful in obtaining detailed information about the specific topic, but may not accurately reflect the large population. Computer-Aided Telephone Interviews use a script of predetermined questions with random sampling t echniques. This method allows for a large sample size, and is less costly, but, reduces the op portunity to fully explore a certain aspect of any given topic. The response rate from the method is lower than tr aditional structured interviews, but higher than mail out surveys. Response rates for previous written surveys ranged from 8 to 24 percent, but can be improved w ith telephone follow up. Written surveys are the least costly, good for broad samp ling, but it typically produces lo w response rates and does not ensure that the right person will complete the survey. Nine types of survey methods were tested as a pilot study to search fo r the best one to use for surveying the freight community. Type 1 mail out/mail back questionnaire with follow up reminders (ES202 database, response rate of 15%) Type 2 mail out/mail back questionnaire and a map, with follow up reminders (ES202 database, response rate of 11%) Type 3 postcard invitation to participate, for positive respondents, mail out/mail back questionnaire with follow up reminders (ES202 database, response rate of 6%) Type 4 postcard invitation to participate, for positive respondents, mail out/mail back questionnaire and a map, with follow up reminde rs (ES202 database, response rate of 4%) Type 5 telephone invitation to participate, for positive respondents, mail out/mail back questionnaire with follow up reminders (ES202 database, response rate of 19%) Type 6 telephone survey (ES202 database, response rate of 60%) Type 7 telephone survey (Oregon DOT Mo tor Carrier Transpor tation Division truck registration database, response rate of 64%) Type 8 mail out/mail back questionnaire with telephone fo llow up reminders (Oregon DOT Motor Carrier Transportati on Division truck registration database, response rate of 33%)

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42 Type 9 mail out/mail back questionnaire with follow up re minders (Oregon DOT Driver and Motor Vehicle Services Division Commer cial Drivers License (CDL) Database, response rate of 12%) In every case, the respondents were given info rmation on the availabil ity of the survey via e-mail or the website, or both. However, little evidence was found that th e option to use the web site or to communicate via e-mail was of interest to the survey participants. In addition to the infrastructure problems, various other issues su ch as regulations, taxati on, and enforcement were covered in the survey. The survey response rate s from telephone surveys were highest among all the methods. The use of postcard invitation befo re a mail out survey resulted in very low response rates. Telephone invita tion with a mail out survey yiel ded a 19% response rate, while a mail out survey with follow up phone calls produced a 33% response rate. Freight firms said that a written survey is the most preferred method, but proved to yield a lower response rate than phone surveys. Prior to carrying out the Or egon State-wide Freight Shi pper and Carrier Survey, 34 previous freight surveys already implemented were reviewed by Loudon (2000). Considerations for conducting surveys of the freight movement community, and lessons le arned from previous freight research in Oregon are summarized. The options to be determined according to the purpose of the surveys are: One company questionnaire versus separate company questionnaires for shippers and carriers. Survey transportation managers only ve rsus survey managers and drivers. Direct driver contact versus di stribution to drivers by managers. Focus on truck movements only versus focus on all modes of freight. Explore problems through open-ended questi ons versus structured list of possible problems. Ask the respondent to rank probl ems versus list problems only.

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43 Ask about problem and practices for inbound and outbound freight movements or outbound only. Use a written (self-completing) questionnaire versus interviewing. Use cold mailing of written questionnaire versus pre-arranged participation. Lessons learned from previous fr eight research in Oregon are: Freight movement logistics are complex. So, sp ecial considerations for flexibility should be made to design each issue (shipm ent size, timing of shipments, etc). Methods of shipping freight ar e changing rapidly. For exam ple, more inventories are maintained in trucks on the highway rather than in manufacturing plants, warehouses, or stores. The survey questionnaires must allow sufficient flexibility to pick up on the current changes. It is not easy to get particip ation from private businesses. Limit the number of issues covered in the surv ey so that sufficient depth of understanding on those issues can be achieved. Survey the right person. A transportation ma nager will generally know the most about decisions about how goods are shipped and received, while an accountant of the company will be able to say something about impact of congestion on costs and profitability. Explore the reasons why transp ortation bottlenecks are a prob lem and how they affect the business Nonrecurring congestion is a significant prob lem, and access to the major highways was as important as level of serv ice on the major highways. The feasibility and an estimation of the pot ential for using Urba n Distribution Centres (UDCs) in the city of Dublin were studied using two surveys (Finnegan, et al., 2004 and Finnegan, et al., 2005). UDCs can fulfill a num ber of functions including warehousing, transshipment, consolidation of loads, effici ent dispatch and collection of goods. Several associations or groups including Dublin City Ce ntre Business Associati on have participated consultation meeting to indicate freight transportati on issues in Dublin City Centre prior to the surveys. The fist survey was deployed on a we eklong survey of deliveries to Trinity College Dublin. The intercept survey method was used at the gate of the campus, resulting in an 82%

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44 response rate (299 participated out of 365 requested). The sec ond survey was distributed to several trade association members, capturing 906 i ndividual deliveries with a 10% response rate. Many aspects of freight movement in the Dublin City Centre (time of delivery arrival and departure, types of goods, how packaged, quantity of packages, types of delivery vehicles, who supplied the goods, location of s upplier, loading and unloading places) were yielded by the surveys to discover the use and location of a UDC. Some conclusions were made at this point; Food related deliveries are expected to be im proved by the use of a UDC in Dublin, a UDC could assists in the process of backhauling, the oper ation of a delivery platform in the city centre was suggested. But, no definite recommendations for the use, location, an d impact of UDC were presented. The Southwestern Pennsylvania Regional Planning Commission (SPRPC) conducted a broad mail survey of freight transportation users and service providers about various issues in freight transportation (SPRPC, 1996). The su rveys were drafted under the guidance of the Freight Forum and distributed to the 700 freight service provi ders from SPRPCs Freight Transportation Database, and 800 area manufacturer s listed in the Southwestern Pennsylvania Regional Development Councils Computer Assist ed Product Search database (CAPS). The postage-paid surveys were sent to them by mail. The original response rate was 4%, but the final response rate was 9% after follow up phone call effo rts. With various ty pes of freight service providers, and manufacturers answer ing the survey, 22% of manuf acturers indicated that they utilize intermodal trans portation, and 42% of service provid ers said that they do. Specific impediments to the respondents with locations and comments were also identified with their percent frequencies; traffic congestion (46%), rush hour deliveries (32%), roadway turning radius (25.5%), turning at traffi c lights (24%), poor bridge or tunnel clearance (18%), curfews on

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45 high or wide roads (17%), merge lanes (16.5%), at-grade railroad crossings (14%), lack of receiving areas at malls (13%), lack of trailer drop-off and pi ck-up (12%), poor truck access to airport air cargo area (11%), la ck of adequate warehousing (8%), delays or other problems at customs (5%), poor truck access to river terminals (4.5%), poor truck access to intermodal facilities (4.5%), lack of rail to highway access (3.5%), poor signage (3%), highway interference with railroad (2%). Womens compensation and ot her labor costs, regula tions, and taxation were also introduced to be pressing issues a ffecting the freight tr ansportation industry. In 1998, Regan and Golob (1999) studied the pe rceptions of motor carriers about traffic congestion, congestion-relief policies, usefulness of information technologies, and efficiency of intermodal facilities in California through comput er-aided telephone interviews (CATI). A total of 5258 freight operators, includi ng California-based for-h ire trucking companies, private fleets, and for-hire large national carriers were chosen from the databases maintained by Transportation Technical Services, Inc, producing an overall response rate of 22.4% (1177 responses). Most responding operators believe that tr affic congestion will get worse over the next five years. Over half the respondents indicated significant or majo r problems of increased fuel and maintenance costs due to stop and go traffi c, high numbers of accidents an d insurance costs, driver frustrations and morale, and scheduling problems due to unreliable travel times. Similarly, more than half indicated that stop and go driving, speede rs and other traffic violators, and poor road surface quality are important cause s of loss of equipment, damaged goods, or even injury to drivers. Almost 90% of the frei ght operators also replied that at least sometimes schedules are missed, drivers are re-routed due to congestion, or customer timewindows force drivers to work in congestion. Preferred congest ion relief policies in cluded adding more freeway lanes, truck only lanes on freeways or arterials, better traffi c signal coordination along the arterials, and so

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46 forth. The surveyed operators perceive that de dicated highway advisory radio, traffic reports on commercial radio stations, and face-to-face report s among drivers at truck stops and terminals are somewhat useful by drivers on the road, wh ile traffic reports on te levision and computer traffic maps on the Internet are ve ry useful to dispatchers. With respect to intermodal operations, about 45% of the operators pointed out that ope rations of carriers are often or very often impacted by congestion or other problems at ma ritime ports, while only 25% of them indicated this for airports or rail terminals. The American Trucking Association (ATA) su rveyed 470 stratified, randomly selected private and for-hire motor carriers based in th e Baltimore region (ATA, 1997). It yielded a response rate of 13.1% (62 returned surveys) after two times mailing and follow-up calls. The ATA worked with the Baltimore Metropolitan C ouncil and the Maryland Motor Transportation Association for the survey questions to check out various needs of tr ansportation users in Baltimore region. The survey questions included company characteristics, major routes of travel, impediments in freight fl ows, infrastructure improvement s needed, downtown freight pick up and delivery, time of day travel freight origins and destinations, intermoda l freight activity, company future plans. The survey results desc ribe facts and motor carr iers perceptions about freight transportation in the Baltimore region. Especially, traffic congestion was thought to be one major structural impediment to freight movement. Intersection design/function, ramp design, tools, constructions projects were listed by some motor carriers as well. In 2001, two surveys were conducted to obtain the opinions of the public and large truck drivers on road safety issues (Center for Public Po licy, 2001). A computer-assisted telephone surveys produced 2415 samples of randomly select ed adult residents from Virginia (n=602), Maryland (n=600), North Carolina (n=610), and West Virginia (n=603) w ith a cooperation rate

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47 of 52%. For truck drivers, an intercept survey was used at three truck stops (Lee Hi Truck Stop (n=206), Truck Stops of America (n=318), and Wh ites Truck Stop (n=102) on interstate 81 and 95, producing 618 samples. The surveys discovered some overall aspects of truck drivers and difference in perspectives on safety issues fr om truckers and public respondents. The main findings from the surveys are as follows: More than 70% of truckers in the surv ey were company drivers opposed to owneroperators. Most truck drivers (about 70%) ge t paid based on miles driven. More than 90% of truckers in the survey are driving tractor-semi trailers. More than 80% of truckers spend 3 7 days away from home every week. Most truckers take time for a sleep break at pr ivate truck stops or publ ic rest areas rather than motels or roadside. Both truckers and public re spondents perceived the highway s driven on most often by them to be somewhat safe. Truck drivers and public respondent s tend to attribute conflicts or crashe s between cars and trucks to each other. Both truckers and public responde nts agreed that the driving ha bits of large bus drivers are considered to be the most safe and least aggressive around cars. Both truckers and public responde nts agreed that drivers of large trucks drive somewhat aggressively around cars. Truck drivers get information of crashes betw een cars and trucks mostly from other truck drivers or citizen band (CB) radio, while public respondents get it from television or newspaper mostly. Truck drivers and motorists opinions of such restrictions at three sites (I-5 Southcenter Hill, SR-520, and I-5 Southbound to Tacoma Ma ll) in the Puget Sound region of Washington State were obtained through surveys in 1992 (K oehne, et al., 1997). The trucker survey was performed at two truck stop locations (one day at each location), slightly more than four months after the last restrictions we re put into place on I-5 Southcen ter Hill, yielding 129 completed

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48 surveys. A mail-back survey was distributed to motorists who traveled each of the three restricted sections of highway more than thre e months after the last restrictions. About 400 license plate numbers were collected from each si te for this purpose. The survey produced 153 completed responses (response rate of about 16%). The major findings are as follows: A relatively high 31.4 percent of truckers in dicated that they had disobeyed the lane restrictions, while about 78 per cent of motorists indicated that they have seen truckers disobey them. Only about 32 percent of the truckers are in favor of keeping Puget Sound lane restrictions, while 91 percent of the motorists are in favor of them. The ne gative view of most truckers toward restrictions could also be simply becau se they believe they are not necessary since trucks rarely use the leftmo st lanes on ascending grades. About 65 percent of truckers and motorists indica ted that it is not clear which vehicles or which lanes are subject to the lane restrictions. Only about 30 percent of the truckers believ e that lane restrictions improve freeway operations, while 86 percen t of the motorists do. Only about 31 percent of the truckers believe that lane restrictions improve safety, while 82 percent of the motorists do. About 66 percent of the truckers believe that lane restrictions should include buses, and 74 percent of the motorists do. Only about 21 percent of the truc kers believe that lane restri ctions should be expanded to more freeway sections, while 83 percent of the motorists do. Three logit models were also developed for exploring truckers and motorists favorability and awareness of truck restrictions There is a profile of a truc ker who is least likely to favor truck restrictions, one who admits to violating restrictions, frequently changes lanes to avoid rough pavement, typically carrie s nonperishable cargo, is between 20 and 40 years old, and has been licensed for many years. Mo torists most likely to favor re strictions also fit a definite profile; one who frequently changes lanes to av oid being followed by trucks, typically drives a passenger car, is between 30 a nd 45 years old, and has been a long-time licensed driver. The motorists most likely to be awar e of Puget Sound truck lane restri ctions are male passenger car

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49 operators who have been licensed relatively few years. Efforts to improve motorist awareness should focus on those motorists who do not fit this profile. A study by Golob and Regan (2002) investigat ed the perceptions of truck company managers on usefulness of different source of traffic information to trucking operations. Managers from 1177 trucking companies includi ng 34% private carriers and 66% for-hire carriers were asked how useful th ey consider different sources of traffic information are to the dispatchers and to their drivers. The survey s were distributed to 5258 companies containing 804 California-based for-hire companies, 2129 Californi a-based private carriers, and for-hire large national carriers based outside of California, ove rall response rate of 22.4 %. The relationships between 6 characteristics of the companies (load type, carrier type, primar y service, location of logistics manager, intermodal operations, and average length of load moves) and managerperceived usefulness of different source of traffi c information to dispatchers or drivers, are discovered through canonical correl ation analysis. The respondent s were asked to evaluate the sources in one of the three categories (i.e., ve ry useful, somewhat useful, and not useful). With respect to the overall useful ness to dispatchers, reports from the drivers on the road are judged to be most useful followed by traffic reports on the radio. Least useful was traffic reports on television, followed by internet traffic maps. Reports from their own drivers are valued most highly by carriers with either rail or multiple intermodal operations, and traffic reports on commercial radio statio ns are useful to general LTL carriers and operations based in the Greater Los Angeles Area. Traffic reports on television are thought to be useful to movers, and intermodal operations. Internet traffic informa tion is judged to be useful to operators with long moves, and phone calls to Ca ltrans are considered to be us eful to operations based in California, but outside of th e two largest metropolitan areas.

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50 Concerning overall usefulness to drivers, ch angeable message signs (CMS) was thought to be most useful, followed by CBR (Citizen Ba nd Radio) or other radi o reports from other drivers. Traffic reports on commercial radio sta tion were considered as useful as face-toface reports among drivers, while Dedicated highw ay advisory radio (HAR) was rated to be least useful for drivers. Face-to-face reports among drivers at truck stops and terminals are useful with rail intermodal operations, and CMS was useful to common carriers and operations from outside of CA carriers with long load move s. CBR reports from other drivers are deemed to be useful exclusively to tr uckload carriers, and HAR is judged to be useful to common carriers or carriers with long load moves. As far as future traffic information sources are concerned, HAR and CMS are considered to be most useful followed by in-vehicle na vigation system. In-vehicle navigation, computer traffic map, CMS, and traffic inform ation kiosk are expected to be useful to operations from outside of CA, and carriers with long moves, while HAR is thought to be useful for carriers with both truckload and LTL in the future. Crum, et al. (2001) developed a conceptual level of commercial motor vehicle (CMV) driver fatigue model by reviews of the 55 litera ture and focus group meetings with industry professionals. It was later used for developing a survey for truck drivers to explore driving environment effects on driver fatigue and crash rates. The model categorized three factors influencing driver fatigue and crashes; CMV driving environm ents, economic pressures, and carrier support for driving safe ty. The CMV driving environments were subcategorized into three issues as regularity of tim e, quality of rest, and trip control, under which a total of 25 individual measures fall, while fatigue and crash outcome measures included 15 items. The data for analysis were collected at five truck stops each in diffe rent states, yielding 502 usable

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51 surveys. A $10 cash inducement was offered to pa rticipants in 1999 with the assistance of the National Association of Truck Stop Operator s (NATSO). Twelve driving environment indicators were found to be meaningfully relate d to 15 fatigue and crash outcome measures; two regularity of time items, six measures of trip contro l, and four items for qua lity of rest. Factor analysis identified three constr ucts underlying the 15 fatigue a nd crash measures; close call due to fatigue, the perception of fatigue as a problem for self and other drivers, and crashes. From the regression analysis, long load time and start workweek tired is found to be significant in increasing frequency of close calls due to fatigue, while the frequency of the use of -hour time zone was negatively related to close calls unexpectedly. Long load time and start workweek tired also were associated with mo re fatigue, while more uninterrupted hours of sleep, more use of regular routes, and more times driving the same hours were associated with less fatigue. More average stops per da y and start workweek tired were found to significantly increase the number of crashes. At the front part of this st udy, it was decided to use focus group and survey methods in obtaining the perceptions and opinions of the truc king community. Thus, some previous studies relevant to this issue were revi ewed in this section to search for an effective way to conduct focus group and survey studies for the objective of this study. The authors of the studies provide their experiences and recomme ndations about focus group preparation, survey data collection and perceptions of the trucking community on various trucking-related issues as results. For the sake of this study, potential Fl orida trucking community focus group and survey participant recruitment sources, and advantages and disadvant ages of various trucking community survey methods are summarized in the following three sec tions, based on the findings from this section.

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52 2.4 Florida Trucking Community Focus Gr oup Participant Recruitment Sources It is generally difficult to invite truck driv ers to a meeting at one place and time. They usually spend a significant portion of their time driv ing on the road, and their schedules are apt to change for time-variant demand for deliveries. On e survey on truck safety issues at three truck stops (Center for Public Policy, 2001) showed that more than 80 percent of the total of 618 surveyed truck drivers spends 3 to 7 days away from home every week for deliveries. Given these constraints, following pot ential recruitment sources were found for truck driver focus groups: Florida Trucking Association (FTA) Road Team members Florida Truck Driving Championship (FTDC) participants Florida-based National Truckers Association (NTA) members Truck drivers from one or two trucking companies Many previous trucking-related studies have benefited from the cooperation of national or state trucking associations. The American Trucking Association (ATA) is a major national association for United States trucking indus try, which often provide s assistance to the researchers, even conducting resear ch studies by itself (ATA, 1997). Most states have either the state trucking association or motor vehicle carri er association, which is the state division of ATA. The Florida Trucking Associ ation (FTA) is in that category. A focus group study was conducted in Toronto at the annual convention of the Ontario Trucking Association (OTA) with its Road Kn ight Team members, which consist of 10 professional truck drivers (Hostovs ky and Hall, 2004). FTA Road T eam is the equivalent of the OTA Road Knight Team. The FTA Road Team in cludes 8 professional driv ers from 6 different companies. They are highly informed professi onals (each with more than 15 years of truck driving experience) and care abou t their industry and profession enough to take time from their daily jobs to speak at any public gatherings and give safety de monstration to the general public.

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53 The Florida Truck Driving Championship (FTD C) is an annual competition of truck drivers driving skills and know ledge on how to operate trucks safely and efficiently. Each participant competes for the championship in one of eight classes of trucks (i.e., single truck, three-axle, four-axle, five-axle, tank truck, flat bed, sleeper bert h, and twins). The champions of this event represent Florida truck drivers in the annual National Truck Driving Championship (NTDC). Florida-licensed truck drivers, who perfo rmed regular duties of a full-time professional truck driver with no accident history for at least a year and no crimin al record in the past 5 years, are eligible to particip ate in the competition. The National Truckers Associ ation (NTA) is an organiza tion designed to protect and support the trucking business of independent (own er-operator) truck drivers. For the reason, most NTA members are inde pendent truck drivers. One or two big-sized trucking companies may he lp get their drivers together for a focus group meeting. The following potential sources of trucking company contacts were found: FTA membership directory Center for Economic Development Res earch (CEDR) Data Center (ES202) Florida Department of Highway Sa fety & Motor Vehicles (FDHSMV) Transportation Technical Services, Inc (TTS) A total of 180 Florida-based trucking companies are listed in the FTA membership directory. They are categorized by 5 chapters (geographical locations), or 6 conferences (carrier types). One study (Lawson, et al., 2002) utilized the Or egon Employment Department database (ES202), Oregon Department of Transportation (ODOT) Motor Carrier Transportation Division truck registration database, and ODOT Driver and Motor Vehicle Services Division Commercial Drivers License (CDL) database to survey fr eight shippers and carriers. Likewise, ES202 database from CEDR Data Cent er or FDHSMV database can be used to contact the trucking companies in Florida. The information by TTS was used in the Golob and Regan (2002) study.

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54 It includes national motor carrier directory, private fleet direct ory, and owner-operator directory database. FDOT personnel also recommended the following companies as potential sources of truck driver participant recruitment: Watkins Motor Lines, Inc (one of the nations largest LTL carriers) Landstar Systems, Inc (a big logist ics and transportation provider) Roundtree Transport & Rigging, Inc It seems to be also difficult to recruit manager-level particip ants for focus group meetings. A study by Morris, et al. (1998) performed 13 i ndustry sector focus groups, but with only 2 participants per group. Some of them also used a conference call. They seem to be tight in their time schedules. The following potential recrui tment sources were found for truck company manager focus groups. FTA Leadership Conference participants FTA Safety Management Council (SMC) members Truck company managers from two or more trucking companies In 2004, Veras, et al. (2005) conducted 2 focus groups with truck dispatchers as a part of the Evaluation Study of the Port Authority of New York and New Jerseys Value Pricing Initiative. FTA Leadership Conference is held annually to suppor t and enhance trucking business for Florida trucking community. Florid a-based Truck company owners and managers are the main attendees in this event. SMC memb ers consist of professional safety managers from FTA member companies. Their primary goal is to make trucking in Florida as safe as possible. It is also possible to contact some of the trucking companies by using the same sources presented earlier to recruit truck company managers. 2.5 Trucking Community Survey Methods In-field survey methods have often been used by other resear chers to collect truck driver surveys. The perceptions of tr uck drivers on roadway safety i ssues (Center for Public Policy,

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55 2001) were surveyed at three truck stops (Lee Hi Truck Stop, Truck St ops of America, and Whites Truck Stop on interstate 81 and 95), produc ing a total of 618 surveys. A truck driver survey by Koehne, et al. (1997) was performed at two truck stop locations (one day at each location), yielding 129 completed surveys. Anot her study about truck driv er fatigue and crash rates (Crum, et al., 2001) conducted surveys at 5 truck stop locations: Maryland, Georgia, California, Iowa, and Colorado. The National Association of Truck Stop Operators (NATSO) assisted with the study, producing a total of 502 us able surveys (overall e ffective response rate was 97.3% with $10 cash incentives). A week-long survey of deliveries to Trinity College Dublin (TCD) was also conducted with an interc ept survey method at the gate of the campus, resulting in an 82% response rate (Finnegan et al., 2004). Surveying truck drivers at rest stops or truck stops have following benefits: It usually shows good overall productivity with some types of incentives. The completed surveys are colleted promptly on site. It asks for the percepti ons of truck drivers on truck operation issues while they are on a trip, so their experiences are fresh. It enables face-to-face interactions between the participants and surveying staff. This helps yield more completed surveys and reduce the ri sk of the participants misunderstanding of the questions. However, it is required for the surveying staff to spend a fair amount of time and effort in the field. Considering the irregular work ing hours and job characterist ics of the truck drivers, it would be effective to distribute the surveys directly to them. It may be possible that written surveys are filled out by truck drivers where they can take time to fill them out. At places where truck drivers stay for a short time, written postage-paid surveys could be distributed to them so that they can return them later by mail. Otherwis e, written postage-paid surveys may be sent to a

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56 number of trucking companies that can distribute the su rveys to their drivers. The sources of contacting trucking companies are listed in the previous section. Different survey methods can be considered for surveying truck company managers. Two studies by Lawson and Riis, and Lawson, et al. (2001 and 2002) compared several methods to survey the freight community (i.e., shippers and carriers) by a literatu re review and a pilot study. It was found that the most effective survey method in terms of response rates is a phonebased survey although a written survey is preferred by the freight firms. In the pilot study, the highest response rate (60%) was achieved by a telephone interview survey (with five callbacks). The other study (Regan and Golob, 1999) obtained a response rate of 35% with a phone survey. However, the response rates fo r previous written surveys of the trucking community only ranged from 8% (Lawson and Riis, 2001). General characteristics of the survey methods are described in Table 2-1. Mail-based surveys are less costly and time-consuming while producing relatively lo w response rate. It would be helpful to contact th e potential respondents in person or by phone to improve the response rate. In one study (SPRPC, 1996), followup phone calls resulted in an 5% increase of the response rate (from 4% to 9 %). The other study (ATA, 1997) showed a response rate of 13.1% by mailing twice with follow-up phone calls. The surveys were personally distributed to the potential respondents to improve the response rate in anothe r study (Finnegan, et al., 2005). This approach also enabled to obtain helpful feedback and co mments about the survey issues directly from them. The survey questions should be clear and simple to reduce the possibility that the respondents misunderstand them. A postage -paid survey format is typically used to reduce the respondents effort s to return the surveys.

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57 Phone-based surveys usually require more money and effort while yielding relatively high response rate. The two studies (Lawson and Riis, 2001 and Lawson, et al., 2002) indicated that a traditional structured phone survey is us eful in obtaining detailed information about specific issues, but is not suitable for a sample si ze large enough to repres ent the views of a big population. The Computer-Aided Telephone Interview (CATI) allows for a large sample size while reducing the opportunity to fu lly explore a certain aspect of any given topic. The CATI is typically conducted by survey companies because it requires trained intervie wers and interactive CATI computer systems. The participants resp onses are keyed directly into a computer and administration of the interview is managed by a specifically designed software program. The program does not accept invalid surveys. In a survey study of freight operators (Regan and Golob, 1999 and Golob and Regan, 2002), the CATI was conducted by Strategic Consulting and Research, an Irvine, Californ ia-based survey company, yiel ding a response rate of 35%. The Web-based survey method is less costly an d easy to administer, but the response rate from this method may greatly depend on publicity a nd advertisement of the surveys. Some types of contacts (e.g., phone, email, mail) with poten tial respondents are highly encouraged to increase the response rate. L ittle is known about the use of this method for surveying the trucking community. The Idaho Technology Transfer Center, in conjunction with the ATA, is conducting a web-based study about the perceptions of truck drivers or fleet managers on the benefits of anti-icing chemicals and reductions of potential vehicle co rrosion (Alexander and Moore, 2006). Regardless of the survey method(s), a clear in dication of the contribution(s) of a survey study to the trucking community or industry may encourage the potential respondents to participate in the survey. The sponsorships of trucking-related associations or institutes (e.g.,

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58 FTA, ATA, or FDOT, etc.) may help reinforce the importance of the survey study, resulting in an improvement of the response rate as well. Table 2-1. Comparison of Survey Methods Survey Methods Characteristics Mail-based Survey Phone-based Survey Web-based Survey Advantages Less costly and timeconsuming Interviewer bias is not introduced Uniform survey method Provide respondents with enough time to give thoughtful answers Suitable for obtaining larger and more representative sample Relatively high response rate Chance to correct misunderstandings Chance to get more detailed information No respondents efforts required to return surveys Less costly, easy to administer Fast results Provide respondents with enough time to give thoughtful answers No respondents efforts required to return surveys Disadvantages Relatively low response rate Potential long time delay Hard to ensure that the right person will complete the survey Potential misunderstanding of the questionnaires by the respondents Respondents efforts required to return surveys o More costly and timeconsuming o Dependent on respondent availability o Not suitable for large sample size o Can not be used for non-audio information o May present lack of uniformity Response rates may greatly depend on publicity/advertisement of the surveys Hard to ensure that the right person will complete the survey Typical Range of Response Rates 824% 3564% Highly variable Studies in which used Lawson and Riis (2001) Lawson, et al. (2002) Finnegan, et al. (2005) SPRPC (1996) ATA (1997) Lawson and Riis (2001) Lawson, et al. (2002) Regan and Golob (1999) Golob and Regan (2002) Alexander and Moore (2003)

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59 CHAPTER 3 RESEARCH APPROACH This exploratory study was aimed at discoveri ng the factors important to estimate LOS on existing roadway systems, as perceived by truck m ode users. This was intended to provide the FDOT with the information about the determinants of truck LOS and the levels of their significance with which it can de velop methods to effectively ev aluate LOS provided for trucks on various roadway facilities. To accomplish this it was necessary to obtain the perceptions and opinions of the trucking community on what factors are important for the quality of a truck trip on various transportation facilities and the relative significance of each of those factors. Focus group and survey studies were conduc ted to satisfy this requirement. Truck drivers and truck compa ny managers are the two major stakeholder groups for the LOS provided for trucks. Truck drivers delive r goods by driving trucks while truck company managers operate the trucks and drivers to make a profit. Truck drivers are the most important group concerning truck LOS, in that they are the ones who actually drive the trucks on the road, so their performance and satisfaction is dir ectly affected by the LOS provided by various transportation facilities. Their performance leve ls also have major effects on trucking business in such aspects as on-time pe rformance, operating cost, etc. Truck company managers are primarily concerned with those trucking business concerns, but in most cases they need to manage their fleet on existing ro adway facilities based on the pe rceptions and opinions of their drivers due to the lack of regular truck dr iving experience. Thus the focus was on the perceptions of truck drivers, wh ile the perceptions of truck company managers were also sought to be compared with those of truck drivers. Initially, very limited knowledge was present on the factors determining LOS perceived by the trucking community. This required a sma ll number of observations to be obtained through

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60 some type of qualitative study to get enough information to develop a more formal survey, which would then be used to obtain the perceptions of a larger sample to represent the trucking community. Typically, there are two types of qua litative study methods; personal interviews and focus group studies. Individual interviews can requi re a considerable amount of time and effort. A respondent is also apt to be unwittingly influe nced by an interviewer, or, often not able to come up with his/her opinions about various aspe cts of the subjects dur ing the interview. Homogeneous and information-rich participan ts in a focus group study can boost the diverse conversation about the subjects with little guidance from a modera tor. Thus, focus group studies were performed for this study. Focus group studies do not provide enough observa tions for quantitative analysis. Thus, a follow-on survey study was conducted to confir m the focus group findings with a broader audience and measure the relative importance of each factor quantitatively. The surveys focused only on potentially relevant and important items. Based on the focus group and survey studies, the guidelines for developing truck LOS estimation methodologies were developed to provide the FDOT with potential service measures for truck LOS and/or a list of f actors that should be considered to develop truck LOS estimation models on various roadway facilities. The overall description of the research approach of this study is presented in Figure 3-1. To develop actual truck LOS estimation models in future studies, experimental data should be collected to quantitatively m easure the correlations between the service me asures and/or the list of factors identified in this study and the pe rceptions of a representa tive sample of truck drivers on trip quality. This ofte n requires some experimental effo rts with in-field driving, video simulation, or driving simulator methods.

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61 3.1 Focus Group Sessions The primary objective of this focus group study was to identify the roadway, traffic, and control factors that are important to the truc king community for truck trip quality on various transportation facilities and to explore the per ceptional differences between truck drivers and truck company managers on the relative importance of each factor. Ultimately, this study sought to inform transpor tation service providers of what should be focused on to better accommodate truck traffic on existing roadway facilities. Thus, the factors that cannot be controlled by the transportation service providers were of less interest in the discussions, although the discussions were open to any factor im portant to the quality of a complete truck trip from origin to destina tion. The focus was on the operational and design policies and issues relative to truck operations on various road way facilities (e.g., lane widths, traveler information systems, etc.) as opposed to truck industry regulation issues such as number of continuous hours of driving, maximum non-permit we ight loads, etc. Weather-related factors were also less of a concern. Three focus group interviews were conducted to elicit the factors affecting LOS on various transportation facilities perceived by truck m ode users (truck drivers and truck company managers). The participants for each focus group session were recruited by the cooperation of the Florida Trucking Association (F TA). Several discussion topics were selected carefully for the overall objective of this study, which is to find out what should be focused on to better accommodate truck traffic on the existing roadway system. During each focus group session, the topics were introduced to the participants with several open-ended questions so that they discuss each topic amongst themselves with only a little guidance from the moderator. The focus group discussions were transcribed and summarized to be used as inputs to a follow-on survey.

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62 3.1.1 Participant Recruitment Based on the review and considerations of fo cus group participant recruitment presented in Chapter 2, it was decided that the most effective and efficient approach to recruiting candidates for the focus group sessions would be with the c ooperation of the FTA. With the FTAs contacts and presence in the industry, they would be much more capable of identifying willing participants for this study. Thus, assistan ce from the FTA was solicited (Appendix A for a cooperation request letter), and they were happy to assist. FTA staff were pleased to hear that the FDOT was conducting a research project ta rgeted at the LOS needs of the trucking community. For the focus group participant recruitment, several documents were provided to the FTA to inform them of preferred participant ch aracteristics, what ques tions would be asked throughout the sessions, and how the sessions wo uld be conducted. The documents included focus group instructions (Appendi x B), guidelines for participan t selection (Appendix C), and a focus group moderators guide (Appendix D). For the truck drivers focus group sessions, the FTA recruited members from its Road Team. Two other truck driver candidates were also recruited. Once they provided a list of thes e candidates, a follow-up recruitment letter was emailed to them to ask for confirmation. Focu s group instructions and a map to the meeting place were attached to the email as well. Two focus group sessions were held with the truck drivers as follows: 5 people including 4 FTA Road Team memb ers, 2.5 hours of discussion, on November 15th, 2005 4 people including 3 FTA Road Team memb ers, 2.5 hours of discussion, on December 8th, 2005

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63 It was initially planned to al so hold two focus group sessions with truck managers, but the FTA found the recruitment of these individuals to be much more difficult. Ultimately, just one 2-hour focus group session was held w ith three managers on November 17th, 2005. 3.1.2 Participant Selection When the FTA agreed to help recruit the pa rticipants for the focus group meetings, the guidelines for selecting the participants were de veloped and provided to the FTA (Appendix C). This was intended to ask the FTA to consider the guidelines to obtain a repr esentative sample of the Florida trucking community. They describe ch aracteristics of eligible participants and the desirable participant composition for each focu s group. This section explains the reasoning behind the development of the guidelines. Initially, a total of four focus group intervie ws were proposed (two with drivers and two with managers), balancing the sc ope and resources available for this study. The two distinct groups of participants were separately invited to different meetings because they were expected to have different perspectives wi th regard to the discussion topics Truck drivers may show more concerns about the traffic, road way, and/or control factors aff ecting truck driving comfort and amount of earning while truck company managers may be more concerned with the factors contributing to their trucking businesses. The separation of the two groups not only made the participants comfortable to shar e their opinions, but also helped the research team to clearly identify the perceptions of each group. The de sired number of participants in each focus group was originally 8. However, it proved challenging for the FTA to obtain this number of participants. Thus, about half this number of participants for each focus group was obtained. Nonetheless, these group sizes worked out well give n the depth and breadth of the participants in each session, and that each of the participants had much to contribute to the discussions.

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64 There are several major socio-economic or working characteristics of the trucking community that may be highly correlated with th eir perceptions on the fa ctors affecting LOS on transportation facilities. It is not realistic to ta ke into account all the characteristics of the focus group participants with the several focus group m eetings, but consideratio n would help recruit a more representative sample of the Florida truc king community. The following characteristics of the trucking community were c onsidered in the guidelines: Hauling distance : Long-haul trucking with frequent travel on the Floridas SIS facilities was of main interest in this exploratory study as opposed to local or short-haul trucking, which only use small portion of th e Floridas SIS facilities. Carrier type : It was desired to include the part icipants from both for-hire and private truck companies. Private trucks moved 32.7 percen t, and for-hire trucks transported 43.3 percent of the total freight valu e originating in Florida in 2002 (BTS, 2004). The rest of the percent (24 percent) was delivered by other modes such as train, ship, plane, or multiple modes. One survey study (Golob and Regan, 2002) verified the percep tional difference between private and for-hire truck companies about various sources of traffic information. Load type : It was desired to include truck dr ivers from both Truckload (TL) and Lessthan-Truckload (LTL) carriers. It is likely that the perceptions of TL drivers on truck trip quality issues are different from those of LTL driver due to the weight of the equipment and goods. The TL and LTL operations are defined as follows: TruckLoad (TL): The quantity of freight required to fill a truck. Usually in excess of 10,000 pounds. When used in connection with fr eight rates, the qua ntities of freight necessary to qualify a shipme nt for a truckload rate. Less-than-TruckLoad (LTL): A quantity of freight less than that required for the application of a truckload ra te. Usually less than 10,000 pounds and generally involves the use of terminal facilities to break and consolidate shipments.

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65 Truck type : It is likely that perceptional differe nce among the drivers operating different types of trucks exists due to their discrete size and operational characteristics. The majority of the trucks on the roads were categorized into the following three types of trucks in terms of truck configurations: Straight Truck (single unit truck) Truck-Trailer (truck-trail er, truck-double trailer) Tractor-Trailer (tracto r-semitrailer, twin-trailer, rocky mountain double, turnpike double) It was desired that each truck driver focus gr oup include at least three truck drivers, each operating different three types of the trucks. Fleet size : It was desired to include truck driv ers that represented companies with a variety of fleet sizes. About 87 percent of the ca rriers in the U.S. operated 6 or fewer trucks, 9 percent operated the fleet size between 7 and 20, a nd 4 percent operated more than 20 trucks in 2004 (ATA, 2005). Others : ATA (2005) showed that 29.4% of the to tal drivers in the U.S. were minorities in 2003 and the percentage has been going up gradua lly from the year 1996. Only 4.6 % of the total truck drivers in the U.S. were women in 2003, but the percentage has been going down since 2001. So, inclusion of the minority or wo man truck drivers in focus group sessions was desired, but this was obviously difficult to attain, given their limited proportions. Overall, a good composition of participants wa s recruited for the two truck driver focus groups in terms of carrier type and primary load type. The participants were all from major trucking companies with at least 16 years of truc k driving job experience. The truck types they operate include various tractor-tr ailers. However, no straight tr uck driver, truck trailer driver, female driver, or minority driver was present on the meetings. Only one truck company manager focus group was conducted with three managers. They were all from the TL carriers with at least

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66 5 years of truck company manage r experiences. One was from private company while the other two were from for-hire companies. A detailed description of the focus group participants backgrounds is provided in Appendix E. 3.1.3 Focus Group Questionnaires For each focus group interview, several discussion topics were introduced to the participants by the moderator th rough several open-ended questi ons. Then, the participants talked about the topics amongst each other with a lit tle guidance of the moderator. An identical set of questions was presented for each meeting to compare and contrast the perceptions of truck drivers with those of truck company managers. Th e issues to be covered were selected according to the overall objectives of this focus group study, which is to inve stigate which factors transportation service providers should focus on to better accommodate truck traffic. Following four topics were considered as most relevant to this study, th erefore were covered during each focus group meeting. Truck travel route and departure time selection : Who is responsible for selec ting a truck travel route and departure time for a delivery? When selecting a travel route and departure time for a delivery, what factor are considered and what is their relative significance? Truck trip quality on various transportation facilities : When you drive a truck on Floridas roadway f acilities (i.e., freeways, arterials, and twolane highways) for a delivery, what factors a ffect the quality of a truck trip and how significant is each of those factors for it? (for truck driver groups) When you consider truck operations on Florid as roadway facilities (i.e., freeways, arterials, and two-lane highways), what factors affect the quality of a truck trip and how significant is each of those factors for it? (for truck company manager groups) Transportation service improvement priority for trucking industry : What types of transportati on facilities (e.g., freeways, urban arterials) would you emphasize most for improving truck operations in Florida?

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67 What are your top priorities for improving tr ip quality/travel c ondition for commercial trucks? Truck delivery schedule reliability : What factors affect the truck drivers ability to reach a destination on time? How often does a late delivery take place? What are the typical consequences fo r you/your company of a late delivery? The most important contributors to quality of a truck trip perceived by the trucking community were thought to be the factors that they consider fo r truck route and departure time decision, so they were asked in the first place. The participants provided their valuable insight about in what respect certain r outes and times of day are prefe rred or avoided by them and which specific factors contributes to each of those aspe cts. The performance levels of those factors have most significant impacts on their trucking bus iness and truck trip quality perceived by them. Secondly, the participants were di rectly asked to list the factor s affecting quality of a truck trip. The topic was introduced by each roadway type in that it was generally admitted in the first focus group meeting that the importance of a factor on truck trip quality vary by which type of a roadway it is on. For example, it was discusse d that the importance of shoulder width and condition on truck trip quality for two-lane highways was considered to be much bigger than that on freeways. The participants also indicated how and how much each factor influences truck trip quality based on their direct or indirect experiences. Improvement priorities among vari ous transportation issues and facilities were asked next. It was intended to search for the facilities or specific factors which are getting relatively less attention by transportation service providers compared to their re lative importance on truck trip quality. The participants comments on this issue will be a good reference for prioritizing transportation improvement plans.

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68 As the truck volume and the demand for just-in-time deliveries have increased in Florida, the importance of truck delivery schedule reliability ha s become critical. Thus, at the last part of the discussion, the participants were asked to list the factors contributing to the issue and indicate their level of impact on it. Truck company manage r participants explicitly mentioned that their customers assess the performance level of thei r truck companies based primarily upon on-time delivery performance. The factors affecting ontime performance, frequency and consequences of a late delivery were e xplored within this topic. The factors identified as important to any of the topics covered in the focus group discussion were used as a primary input for a fo llow-on survey so that the importance of each of those factors could be verifi ed by a larger audience and the relative significance among the factors on truck trip quality coul d be quantitatively investigated. 3.1.4 Conducting Interviews The focus group meetings were held at th e Civil Engineering Conference Room or Transportation Research Center Conference Room at the University of Florida. All the meetings were moderated by a researcher who is knowledgeab le to direct the discussions for the purpose of this study. Upon the arrival of the participants, they were asked to fill out an informed consent form and a two-page participant b ackground survey (Appendix F and G). During the main session, the moderator introduced the selected topics to the partic ipants with several general open-ended questions (Appendix D). Each question was displayed on a big screen to help keep the participants focu sed on the issue being discussed. The participants discussed each topic amongst each other with just a little guidanc e from the moderator. Although all the issues were planned to be covered within the two hour s, it turned out to not be enough time because they had so much to say about each topic. The truck driver focus groups were extended by about

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69 30 minutes with the permission of the participants. Participa tion in the focus group meetings was voluntary. The audio from the focus group meetings wa s recorded on a laptop computer through external microphones and a specialized software product that enabled every comment to be associated with the corresponding speaker. Th e recorded audio file s (.wav files) were transcribed into an electronic text format. Th e transcriptions were reviewed in detail by the research team to ensure their accu racy. After the transcriptions were complete and accurate, the focus group discussions were su mmarized and used as guidan ce in the development of the follow-on surveys. 3.1.5 Summary of Discussions The results of the focus group studies were summarized by each issue covered in the discussions. The participants did not always focus on the issue being discussed. They sometimes jumped onto the previously discussed issues or the issues to be discussed later. As long as the discussions remained on relevant topics, the moderator did not redirect the conversation. Thus, it was required to collect participants comments a bout each issue spread over the transcriptions. Some of the comments were paraphrased for clarity and brevity, but care was taken not to place any personal perceptions on the summary. The factors perceived to affect truck trip quality were categorized by each transp ortation facility type (e.g., freeways). Each of the factors was listed and describe d with the participants comment s about its contribution to the truck trip quality and their dire ct or indirect experiences with it. The perceptions of truck company managers were separately summarized to be compared with th ose of truck drivers. The focus group meetings covered many issues in a limited amount of time. The discussions were directed by the moderator to draw out a list of the factors important to truck trip quality, not spending too much time on one or two specific factors. Thus it was not appropriate

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70 to make quantitative implications of the discussi ons on the relative importan ce of each factor. It was investigated quantitatively in a follow-on survey, which was developed based on the focus group discussion summary. 3.2 Survey Studies The primary objective of this survey study wa s to verify the importance of each factor identified in the previous focus group study with a larger audience and to quantitatively measure the relative significance of each of those fact ors on truck trip quality on Floridas roadway system. Preference of the respondents on truck dr iving time of day was also investigated to explore how quality of truck driving environm ent varies by time of day. The background characteristics of the respondents that were corre lated with their perceptio ns on truck trip quality or preference on truck driving time of day were identified. The survey included following three parts: working and socio-economic backgrounds of the respondents; their perceptions on the relative importance of each factor on truck trip quality; and their opinions about the importance of each hypothetical truck LOS performance measure. Two sets of survey forms were distributed; one for truck drivers and the other for truck company managers. Some questions about the backgrounds of the respondents differed in the two survey forms, but identical sets of factors and hypothetical performance measures were evaluated in both surveys. They were carefully selected based on the previous focus group study results. Only truck trip quality determinants on the fo llowing three roadway types were investigated considering the lengths and comple xity of the surveys: freeways; urban arterials; and two-lane highways. A total of 459 truck drivers and 38 truck company managers responded to the written surveys collected at Florida Truck Driving Ch ampionship (FTDC) event or the postage-paid mail-back surveys distributed at four agricultural inspection stati ons. The survey responses were

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71 analyzed with various statistical methods such as descriptive statisti cs, Exploratory Factor Analysis (EFA), multiple comparisons of the means, non-parametric tests, and chi-squared tests of independence. 3.2.1 Question Types When the issues to be questioned in the survey were selected, a questions type for each of those issues was carefully determined to effectiv ely carry out the study. It involved such various considerations as applicability of statistical analyses, res pondent burden, respondent error (or complexity level), measurement accuracy, etc. Following five question types were utilized in this survey study: interval-ra ting questions; ratio-s cale questions; forced-ranking questions; discrete-choice questions; and fi xed-sum questions. This sections describes characteristics (advantages and disadvantages) of those question types based on which the question types for each survey issue of this study were determined. 3.2.1.1 Interval-rating questions This question type is often used to ask for th e perceptions of the respondents. Respondents are asked to present their percep tions on the level of importance, satisfaction, or agreement for each item on an interval-rating scale (e.g., 1 = No t at all Important, 7 = Extremely Important). The accuracy of the participants opinions may be improved as the number of selectable points in a rating scale increases. However, more point s require the respondent to think about the differentiation along the extended scale, increasi ng response burden and time to complete the survey. It is typical to have at least five points on the scale, since fewer than five points generally do not generate normally distributed data which are usually obtained when the data are truly interval in nature. Se ven-and ten-point scales are most common. Even-numbered scale may be used when there is a need to force the re spondents to commit to one side or the other, but

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72 it typically creates a downward bias as they tend to choose five as a neutra l point in a ten-point scale. A critical differentiator between interval-rati ng and ordinal scale ques tions is that equal intervals exist between each ad joining pair of res ponse options. If all response options are literally presented (e.g., 1 = Not At All Important, 2 = Little Important, 3 = Important, 4 = Very Important, 5 = Extremely Important), the differe nce between each adjoining pair of response options are not the same, resulti ng in ordinal scale da ta. Thus, the response values from an ordinal scale question do not have numerical meaning, and categorical or nonparametric statistical analyses can only be ap plicable to the data. It is also true for a forced-ranking question, which is one type of the ordinal scale questions The responses from an interval-rating scale question are appropriate for the numerical in terpretation (although not perfectly), enabling further statistical analyses (e.g., parametric stat istical analyses). However, an interval-rating scale question allows respondents to indica te that everything is almost equally important/satisfied, making the distinction among the factors difficult, while a forced-ranking question require them to make clear distinctions among the factors. 3.2.1.2 Ratio-scale questions Respondents are asked to provide their answer as a ratio to a basic unit of measurement (e.g., year, dollar, hour, etc.) assigned to each ques tion. The ratio scale of measurement is the most informative scale and widely used to obtain participants background data (e.g., age, number of children, how long their phone call was on hold, etc.). It has zero position indicating the absence of the quantity being measured and is an open-ended interval-rating scale, in that there is no designate d upper-end point. The use of a ratio-scale question for responde nts perceptions or opinions (e.g., perceived importance or satisfaction levels) may not be a good idea. It may decrease a response rate,

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73 increase respondent error, and bring about unreasonably large va riances among the responses or participants, rather than increase level of precision in their res ponses with numerous numbers of points in the scale. 3.2.1.3 Forced-ranking questions Respondents are given a number of factors and asked to plac e them in order based on a certain criterion (e.g., importance level). For ex ample, six factors might be presented and the respondent is asked to place a next to the mo st important factor, a next to the second most important factor, and so on. Again, the re sponse values from this scale do not have a meaning numerically, so the data should be analy zed categorically or nonparametrically. These data are often analyzed with a cumulative fre quency distribution for each factor. For example, percent of the respondents pe rceived that factor A is at le ast secondly important among the listed factors. This approach forces them to cl early distinguish one factor from others, usually producing a larger variance among the responses than an interval-rating scale question. A forced ranking question is heavily prone to respondent error. Respondents might interpret it as an interval-rati ng question, use the top and bottom rankings more than once, or rank the top and bottom items skipping the middle ones. In a telephone survey, you could train the interviewers to prompt the respondent for full and correct answ ers. For a web-based survey, you could insert an error feedb ack system, not letting the resp ondents move through the survey without completing the question correctly. These practices are as likely to annoy the respondent as to get real answers. It could also require the respondents to make a considerable effort to complete the survey as the number of factors to be ranked increases. Rank ordering of 10 factors is, in fact, extremely difficult.

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74 3.2.1.4 Discrete-choice questions Respondents are given a list of items and asked to choose one answer that best applies, or specific number of answers that best applies, or all the choices that apply. A survey developer decides which type of discrete-c hoice question is appropriate for a survey issue, considering the characteristics of the issue, study purpose, a nd respondents burden accompanied by the question. This question type is widely used in a survey to ask for the respondents background characteristics as well as thei r perceptions on certain issues. The responses are nominal (or categorical), so it is easier for the respondents to reply to the question than other question types. The discrete-choice data are often presented by (cumulative) frequency distributions of each option category. If the respondent s perceptions are asked with a discrete-choice question, logit modeling technique can be applied where the responses data are us ed as a response variable and other background characteristic data as explan atory variables. Relationships between the perceptions and background char acteristics of the respondent s are identified by the logit modeling technique, which provides an equation to predict the response once a set of background characteristics are given. This question type is often used to ask about the topics that may be potentially sensitive to the res pondents (e.g., income level) because it is less personally obtrusive than a ratio-scale question to the re spondents. In that case, the res ponse data can be used in logit modeling process as a potential explanatory variable. Multiple choices, multiple responses : Respondents are given multiple response categories and asked to select ei ther all the response choices th at apply, or specific number of response options that apply. The former question type is usually used for some background characteristics (e.g., choose all th e types of foods you can cook) a nd the latter is often used to investigate the perceptions of th e respondents (e.g., select 2 type s of foods you like the most). The response data are often analyzed by (cumulativ e) frequency distributions. When the former

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75 question type is used, the data are often convert ed to binary data for each response category and used as potential explanatory variables for variou s statistical modeling (e .g., logit, probit, or regression modeling). The latt er question type requires less respondent burden than other question types for respondents perceptions. The respondents only have to choose a certain number of factors (e.g., 2, 3, or 5 factors) from a list of factors according to their perceptions. Two or three factors are most co mmonly asked to be selected, but the number of factors to be selected basically should be determined based on the purposes of a study. One study by Washburn, et al. (2004) used this question type to identify most important user-perceived trip quality determinants on rural freeways. The resp ondents were asked to select 3 factors most important to their perception of trip quality among 16 listed factors. The importance of each factor was presented by the percent of the responde nts who selected the factor in their top 3 most important factors. Multiple choices, single response : Respondents are asked to select only one most appropriate response out of multiple response opti ons. A survey designer should be careful to direct the respondents to select only one most appropriate response, not just any responses that may apply. If the perceptions of the respondents are asked with this question type, the relationships between their perceptions and b ackground characteristics can be modeled by multicategory logit or probit modeling technique, wh ich provides a means of predicting response probability, given a set of background characteristics. This question type can also be used to ask for some background characteristics of the respondents (e.g., race). In that case, the responses are often converted to a set of bi nary data to be used as explanatory variables for other models. Binary choice : The respondent is asked to select one out of two choices. Typically, these choices are true or false, or yes or no (e.g., existen ce of any dependent). If the perceptions of the

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76 respondents are asked with this question type, th e relationships between their perceptions and background characteristics can be modeled by bina ry logit or probit modeling technique, which provides a means of predicting re sponse probability, given a set of background characteristics. This question type can also be used to as k for some background characteristics of the respondents (e.g., gender) and defined as an explanat ory variable for other models. In all cases, the data can be easily represen ted by frequency distributions. 3.2.1.5 Fixed-sum questions Fixed-sum or fixed-allocation questions are co mbination of the inte rval-rating scale and the forced-ranking questions. Respondents are given a set of factors and asked to allocate a total of 100 points to the factors for a certain aspect (e.g., importance level). They are encouraged not only to consider rankings of all th e listed factors before the allocat ions, but also to present level of each factor for the aspect by a number. This question type is also used to obtain some background characteristics of the respondents (e. g., percent of your trip purposes: business; leisure; social). The block of points to allot is typically 100 since most people are comfortable thinking in percentages, but allo cated scores often tend not to a dd up to 100 as the number of factors in question increa ses. Time-consuming data cleansing is required for the responses not summing to 100. For this reason, it is common to present not more than 10 factors and state what an equal weighting would be. Typically, 4, 5, or 10 factors are listed to be evaluated because the equal weighting (i.e., 25, 20, and 10) is easily recognized by the respondents. The response data are often presented with descriptiv e statistics and can be co nsidered as a response variable or an explanatory variable for various statistical analyses. 3.2.2 Survey Development The survey issues covered in this study were selected for the ultimate objective of this study which is to find out what should be focused on by transportation servi ce providers to better

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77 accommodate truck traffic on current roadway system s. They are presented in Table 3-1 with their corresponding question types an d analysis methods utilized in the survey to address those issues. Two different survey forms were prep ared (Appendix H for truck driver survey and Appendix I for truck company manager survey). Id entical sets of factors were evaluated in the two surveys to compare the pe rceptions of the two groups, bu t most questions regarding respondents background characteristics nece ssarily differed between the two surveys. Participants background : Background characteristics of e ach participant were asked in the first part of the survey. It was intende d to discover the relationships between their backgrounds and perceptions on truck trip quality. Thus, the characteristics suspected to explain the potential variances in their percepti ons were included in the first place. The background survey section includes the questions about socio-economic status and working characteristics of the respondents. Va rious question types were used for this section according to the characteristics of the selected issues. Discrete-choice questions were used to ask for socio-economic backgrounds (e.g., age, annua l income) to be less personally intrusive to the respondents than ratio-scale questions. All the response options for working characteristics questions (e.g., types of goods ha uled, truck types used, duties of truck company managers, etc.) were determined through extensive literature search and di scussions with members of Florida trucking industry to reflect the curren t state of Florida trucking industry. Preference on truck driving time of day : Truck driver respondents were asked to present current and preferred truck driving times of day, while only preferred truck driving times of day could be asked in the manager survey. It was intended to investig ate how quality of truck driving condition varies by time of day. This offered valuable in formation about the preference of the trucking community on night-time deliver y and how the preference of truck drivers on

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78 truck driving time of day is different by thei r background characteristics. Time of day was divided into 6 time periods and preference on each of the periods was asked with a binary choice question to reduce respondent burden. Relative importance/satisfaction of each factor on truck trip quality : Relative importance of each factor on truck trip quality was asked simultaneously with relative satisfaction of each factor on overall Florida ro adway facilities. Importance-satisfaction (or importance-performance) analysis approach is often used in the field of marketing research to prioritize attributes of a product for improveme nt (Martilla and James, 1977). That is, manufacturers should firstly focus on improving the attributes of a product that are perceived to be important, or are unsatisfied by the customers. In this study, this approach provided transportation service providers wi th valuable insights about what should be firstly focused on to improve LOS for trucks (i.e., th e factors that are perceived to be important, but are not well satisfied by truck drivers). Basically, the factors identified in the previous focus group st udies were presented in the survey to be evaluated, but a c ouple of other factors that the re search team considered to be important were also included (e.g., frequency of faster vehicles following your truck). Weather factors (e.g., thunder storms, heavy rain falls) we re excluded from this survey study even though they were perceived to be fairly important in the focus group studies, b ecause they cannot be controlled by transportation service providers. The relative significance/satisfaction of each factor on following three roadway facilities was evaluated: freeways; urba n arterials; two-lane highways. Truck trip quality issues on multilane highways were not covered in this study to keep the proper length of the surveys. It was indicated in the focu s group studies that the trucking community was least concerned about truck trip quality on multilane highways among

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79 those on various roadway facilitie s (i.e., freeways, urban arteri als, two-lane highways, and multilane highways). The importance/satisfaction of each factor wa s asked on a 7-point re lative interval rating scale ( 3 = Least Important (or Satisfied), 0 = As Im portant (or Satisfied) As Others, +3 = Most Important (or satisfied)). It wa s not appropriate to use typical in terval-rating scale, ordinal scale, or ranking scale types of questions As mentioned in a previous section, a typical interval-rating scale question allows respondents to indicate that everything is almost equally important, making the distinction among the factors difficult. An ordinal scale or forced -ranking questions do not allow mathematical interpretation of the survey re sponses, restricting the a pplicability of various statistical analyses. The number of factors to be evaluated range d from 18 to 19 by roadway type, so the use of a forced-ranking scale questi on was strongly discourag ed because it would significantly increase respondents burden and er ror, or decrease the response rate. Bubbleshaped option boxes were displayed for participants to reply to this survey question easily. A 7point scale was used for this question, balanc ing the precision in measuring respondents perceptions and the respondent bur den. A small number of points in scale decrease level of measurement precision, while a large number of scale increase the respondent burden. Improvement Priority Score ( IPS ) : Although not asked directly in the survey, improvement priority of each of the listed fact ors could be assessed through a combination of Relative Importance Score ( RIS ) and Relative Satisfaction Score ( RSS ). Improvement priority is proportional to RIS and inversely proportional to RSS That is, the more important or the less satisfied a factor was, the more the factor is in need of impr ovement. Based on this reasoning, Equation 3.1 was devised to calculate the Improvement Priority Score ( IPS ) for each factor.

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80 ) ( RSS RIS RSS RIS IPSa (3.1) where, IPS = Improvement Priority Score ( 42 +42) RIS = Relative Importance Score (1 7) RSS = Relative Satisfaction Score (1 7) a = +1 if RIS >= RSS otherwise 1 RIS and RSS of each factor collected on an interval-rating scale of 3 to +3 were converted to a scale of 1 to 7 for calculating IPS for each factor. The greater the IPS of a factor is, the more improvement on the factor is antic ipated. When the importance leve l of a certain factor is equal to the satisfaction level, IPS will be zero. A total of 49 (7 RIS 7 RSS ) possible responses and their corresponding IPS are tabulated in Appendix J. On e survey response presents one IPS for each factor. An average IPS of each factor from all the responses is used to estimate its relative need of improvement. Relative importance of each fact or on overall trucking business : Relative importance of each factor on Operating Cost ( OC ), On-time Performance ( OP ), and truck drivers Trip Satisfaction ( TS ) was asked individually on a 7-point relative interval rating scale ( 3 = Least Important, 0 = As Important As Others, +3 = Most Important) in the manager survey. The percent allocation of the managers concerns on each of these issues was also asked with a fixedsum question. OC OP and TS values were converted to a scal e of 1 to 7 and relative importance of each factor on Overall Trucking Business ( OTB ) was calculated by summing the OC OP and TS values weighted by their correspondi ng portion of the managers concerns.

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81 Relative importance of each category of factors on truck trip quality : All the listed factors for each roadway type were divided into four categories for each roadway type and the relative importance of each of the categories on truck trip quality was evaluated with a forcedranking question (1, 1 = Most Important, 4 = L east Important). This was intended to investigate which types of tran sportation service are relatively important to LOS perceived by truck mode users for each roadway type and how this importance priority varies for various roadway facilities. The relative importance of four identical categories were evaluated for freeway and two-lane highway facilities (i.e., physical roadway condition, traffic condition, traveler information systems, and other drivers behavior), but signal condition replaced traveler information systems category to be eval uated for urban arterial facilities. A forcedranking question was used for this issue to focus on the distinctions amon g the importance of the categories of factors. The use of an interval -rating scale question was not appropriate because there is much chance that most respondents simply state that every categor y is equally extremely important. Applicability of single hypothetical perf ormance measure to estimate truck trip quality : Based on the focus group studies, several hypothetical truck LOS performance measure were developed by the research team for each road way facility (e.g., Ease of Driving at or above the Speed Limit for freeways). This was intended to investigate whether it is possible to use only one or two performance measures to adequately ev aluate truck trip quality on each roadway type. The hypothetical performance measures for each road way type were selected in a way they are independent one another. That is, each performa nce measure presents different aspect of truck driving condition. Each performance measure ca n be considered as a function of multiple specific factors that were evaluated in a former part of the survey. The applicability of each

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82 hypothetical performance measure solely to eval uate truck LOS was asked on a typical 7-point interval-rating scale (1 = Not at all Applicable, 7 = Perfectly Applicable). It was expected to obtain large enough variances for th e distinction amongst the factors since it is not reasonable for the respondents to indicate that most of th e different performance measures are perfectly applicable solely to assess truck LOS. Agai n, a 7-point scale was used for this question, balancing the precision in measuring responde nts perceptions and the respondent burden. Relative improvement priority for each roadway type : The relative improvement priority among various roadway types was asked at th e last part of the surv ey. It was intended to find out which roadway facility types are more in need of improvement for the trucking community among the four roadway facility type s (i.e., freeways, urban arterials, two-lane highways, and multilane highways). A forced-ranking question was used for this issue to focus on the distinctions among the ro adway facility types while keep ing the respondent burden at a reasonable level (1, 1 = Most in Need of Impr ovement, 4 = Least in Need of Improvement). The survey data is beneficial for prioritizing the improvement n eeds of various roadway facility types for truck traffic. 3.2.3 Data Collection Truck drivers and truck compa ny managers, as the major truc k mode users on the Floridas SIS facilities, are the target population in this study. A truly random sample is impossible to obtain considering expected difficulties with recru itment, time, and budget. However, an effort has been made to get a reasonably re presentative sample for this study. Based on the review and considerations of truck driver survey methods discussed in Chapter 2, two different approaches were used for survey data collection of the truck drivers. The first method involved the distribution of the written surveys at the Florida Truck Driving

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83 Championship (FTDC) event. The second method consisted of distributing the postage-paid surveys at several agricult ural inspection stations. The first truck driver survey was conducte d at the FTDC event (on June 1, 2006 in Tampa). This event is co-sponsored by the FTA a nd they assisted the research team with the distribution of the surveys. They were given to the drivers while they were in a session where they were required to fill out other paper work as well. There were some concerns expressed by the rese arch team about the original length (six pages) of the survey, and the research team discussed this with the FDOT and the FTA. However, the FTA representatives felt that th e length would not be a problem because they would require the drivers to fill them out as part of their participation in the event, so it was decided to leave it at that length. The first two pages of the survey included th e questions regarding the socio-economic and working characteristics of the respondents. Th e relative importance/satisfaction of each factor on truck trip quality on freeways, urban arterial s, and two-lane highway s were asked on pages 3, 4, and 5 of the survey, respectively. The last pa ge was used to ask abou t the applicability of several different performance measures being su itable as a single performance measure to estimate truck trip quality on each roadway type (Appendix H for the truck driver survey form). As FTA recommended, a total of 220 truck driver survey forms were provided for distribution at the driving competition. This number was based on the number of registered participants in the even t. Of this total, 148 surveys were returned to the researchers. Unfortunately, only 38 respondents out of the to tal of 148 respondents ( 25.7%) completed all sections of the survey as directed. Most participants chose to not fi ll out the survey in its entirety, even though they were instructed to do so. Most respondents may have not taken the surveys

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84 seriously, or they might have thought that the surv ey was a bit long or hard for them to complete, especially the survey sections for relative importance/satisfacti on of each factor on each roadway type. Given these results, it was deci ded to also conduct an in-field survey data collection effort. With the assistance of FDOT personnel, postage -paid mail-back surveys were distributed at several agricultural inspection stations. For this survey effort, a reduced version of the surveys was prepared in hopes of obtaining a good response rate and a higher level of completion of the surveys. The response rate statistic from the FTDC survey data indicated that the survey sections for relative importance/satisfaction of each factor required the most amount of respondent burden among all the sections in the survey. Most su rvey participants did not have problems or difficulties filling out the last page on applicabil ity of a single performance measure. Thus, the survey was reduced to three pages in length; the first page for background information, the second page for relative importan ce/satisfaction of each factor on one of the 3 roadway types (freeways, arterials, or two-lane highways), and the last for app licability of a single performance measure. This survey (one with freeway rela ted factors on the second page) is included in Appendix K. A total of 4000 postage-paid surveys were supplied to the F DOT for distribution at the inspection stations. A total of 1000 survey s were distributed at each of four inspection stations. The four stations were located at northern border locations, and included: Pensacola station on I-10 (West) Suwannee station on I-10 (near Live Oak) Hamilton station on I-75 Yulee station on I-95 The previous focus groups indicated that most truck drivers concer ns are on freeways or two-lane highways as alternatives to freeways. A significant portion of their trips are also on

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85 freeways, distance-wise as well as time-wise. Thus, of the 1000 su rveys distributed at each site, 500 surveys were freeway related (i.e., the s econd page asked ques tions about relative importance and satisfaction of factors affecting freeway quality of service), 250 surveys were two-lane highway related, and 250 were arterial related. The research team was informed by FDOT that only about 3 percent of all the truck drivers trave ling on the Interstates could bypass the agriculture stations. These vehicles generally include pre-pass users, car haulers, and empty flat beds. The surveys were distributed dur ing the week of August 14, 2006. A return date of September 1st was put on the surveys. Most surveys were returned by that date, but some were still received up to a month later. Surveys received after October 1st were not included in the data set because data reduction was complete at that point and analysis had started. A total of 311 surveys were returned by October 1st, yielding a response rate of 7.8%. As shown in Table 3-2, the response rate for the freeway-related survey s was much higher than those for arterials or two-lane highways. It implies that this pa rticular population of tr uck drivers was more concerned or interested about tr ansportation services on freeways. The completion rate of these surveys was much higher than those from the FTDC It is likely that the reduced length and greater flexibility of when they could fill them out contributed to this. According to the advantages and disadvantages of the various survey methods reviewed and discussed in Chapter 2, the phone-based survey method was considered as the most efficient way to survey truck company managers. However, this method was not well suited for this study because it does not allow the respondents to comp lete the second section of the survey without bias. This section requires the respondents to present their perceptions on the relative importance/satisfaction of each fact or among a total of ~18 factors. It would be possible only when the respondents can skim thr ough all the factors simultaneously.

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86 With these considerations, truck company managers were surveyed with two different approaches. One method was the in-field survey duri ng the FTDC event. The other method was the postage-paid mail-back survey with a numbe r of trucking companies listed in the FTA membership directory. A survey data collection effort for truck company managers was made on June 1, 2006 at the Fairgrounds in Tampa, where the truck driv ing championship was taking place (Figure 3-2). There was a modest crowd present on this day, with a mix of company managers, other truck company employees, as well as family and friends. The research team set up a table with several chairs, covered by a large umbrella. A survey poster describing the pur pose and background of this research project was placed on the table to solicit participation of truck company managers (Figure 3-3). The announcers for the driving competition also made several announcements throughout the day about our effort. Eleven truck company managers filled out the surveys, with seven of them answering most of the survey questions as directed (Appendix I for the truck company manager survey form). For the second survey data collection effo rt of truck company managers, potential recipients were identified from the FTA membership directory, which contains a list of all carrier and allied members with their affiliation and cont act information. All the Florida-based carriers in the directory (a total of 180 trucking companies) were cons idered as potential survey participating trucking companies. All the al lied members were removed from consideration because they do not operate trucks. They support trucking companies by offering various services such as accident investigat ion, insurance, truck driver traini ng, truck sales or rentals, etc. In the FTA directory, the carriers can be divided into 5 chapters (geographical locations), or 6 conferences (carrier types). A total of 50 Florida-based carriers were selected for the survey

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87 using the stratified random sampling procedure th at preserves the proporti onal composition of all the Florid-based carriers in terms of conference and chapter (a total of 30 strata). A reduced version of the manager survey (one with freeway related factors on the second page) was used (Appendix L). The background sec tion of the manager survey is different from that of the driver survey. For th e rest of the sections, the same sets of roadway, traffic, and/or control factors are presented, but questioned differently. For instan ce, the relative importance of each factor on operating cost, on-time performance, and truck drivers trip quality were asked respectively in the manager survey, while the impor tance and satisfaction of each factor on truck trip quality were asked in the driver survey. Five postage-paid surveys were mailed to each of the 50 FTA carrier members (5 50 = 250 surveys) with a cover letter asking them to distribute the surveys to the managers (transportation, safety, dispatch, logistics, etc.) at their comp anies (Appendix M for the cover letter). As can be seen in the cover letter, it was mentioned that this study was being conducted with the cooperation of the FTA. It was hoped th at this would encourage managers to respond to the survey. Follow-up phone contacts were made to each of the companies to whom surveys were mailed (about one week later) to confirm that they received them and ask for their support in filling them out. Initially, a total of 300 surveys we re prepared in case some the 50 carriers do not receive the surveys. Of 50 additional surveys, 5 surveys were resent to one carrier that did not receive the survey and 45 surveys were mailed to 9 different newly-selected carriers. As a result, 27 surveys were obtained from all the Flor ida regions except for West Florida. Nineteen surveys were from common (for-hire) carriers, 6 fr om private carriers, and 2 from tank carriers. Table 3-3 shows the number of carriers that partic ipated in the survey from each conference by

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88 each chapter, out of the total of 59 carriers. Ta ble 3-4 indicates the number of surveys received from each conference by each chapter, out of the total of 300 surveys. 3.2.4 Data Reduction It turned out that most respondents completed only parts of the surv eys and there was also some evidence that some of the respondents did not pay enough attention to fill out the surveys as directed. The length, or the perceived co mplexity, of the survey may have kept the participants from completing them correctly. Considering that the purpose of this survey is to potentially identify ways of improving the working environments of the participants, there is no other source of non-response bias expected in this study. Thus given an overall low response rate, it was decided that in addi tion to complete surveys, partially completed surveys would be utilized for data analyses once surveys with unre liable responses were scr eened out according to certain criteria. The usability of survey respons es for data analysis was determined for each survey question. Survey data filt ering criteria were de veloped to assess the validity of the survey responses (Appendix N). However, for a few surveys, it was still difficult to determine their validity with the filtering criteria. The resear ch team had to make d ecisions in those cases through discussions. In that process, the resear chers were stricter on the validity of survey responses collected during FTDC than of the postage-paid surveys due to the larger respondent burden to complete the surveys distributed at the FTDC event. Tables 3-5, 3-6, and 3-7 illustrate the number of valid surveys out of total number of returned truck driver surveys from the FTDC event, the postage-paid surveys, and both comb ined. Table 3-8 describes the number of valid surveys out of the total number of returned mana ger surveys from all the survey data collection sources.

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89 3.2.5 Data Analysis A variety of statistical methods were used to analyze the survey data to satisfy the objectives of this study. All th e variables observed in the survey were first summarized by descriptive statistics and/or (cumulative) frequency distribution to investigate overall distributions of the responses. The following five statistical methods were applied to the survey data for further analyses: Exploratory Fact or Analysis (EFA); Games-Howell multiple comparison test; Kruskal-Wallis test; Mann-Whitney test; and chi-squared test. Exploratory Factor Analysis (EFA) was performe d with Relative Importance Score ( RIS ) of all the factors to look for common latent factors that are important to truck trip qua lity. Each pair of the mean importance of hypothetical truck LOS performa nce measures for each roadway type was compared with Games-Howell tests to find out which performance measure is more important than others statistically. Potential relations hip between each background characteristics of the respondents and their perceptions on each poten tial truck LOS performance measure was investigated through nonparametric tests such as Kruskal-Wallis test and Mann-Whitney test. Chi-squared test, in particular was performed to discover pote ntial relationship between each background characteristics of the respondents and their preference on each truck driving time of day. 3.2.5.1 Descriptive statistics Two most important descriptive statistics were calculated fo r all the interval-rating and ratio-scale questions; mean and standard deviation. A central tendency of each variable was presented by its mean, while its standard devia tion was presented to measure a typical degree of spread of the variable. For nomin al (categorical) data, (cumulative) frequency distributions were presented to describe overall di stribution of the survey response s. Histograms and scatter plots were used to display the results.

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90 3.2.5.2 Exploratory factor analysis (EFA) Exploratory Factor Analysis (EFA) is a sta tistical method to expl ain a large number of metric variables in terms of their common, underlying dimensions, that is, latent factors. Latent factors are unobserved entities that influence a set of measures (v ariables) and are extracted from the correlations among the variables. EFA result s provide how the variab les are grouped into a small number of latent factors from the res pondents perspectives and how much each latent factor is correlated with each of the variables. Factor analysis is a multivariate interdependence technique with which all the variables are si multaneously considered, rather than multiple regression, discriminate or canonical analyses, in which one or more variables are explicitly considered as dependent variables (Hair, et al., 2005). This tec hnique is often used in social science research to summarize the data by identifying a set of latent factors that influence each set of variables, which correlate highly amongs t each other. The following seven steps are typically taken to perform EFA (Field, 2005). Calculation of correlation matrix : The starting point of factor analysis is to create a correlation matrix, in which the inter-correlations between each pair of observed variables are presented. A basic assumption of a factor analytic procedure is that a group of variables that significantly correlate with each other do so because they are measuring the same common, underlying dimension. Thus, if a group of variable s seem to correlate highly with each other within the group, but correlate very badly with variables outside of that group, they are considered to well measure a common, underlying di mension, which is called a latent factor. The ultimate objective of the EFA procedure is to reduce the correlation matrix to a factor matrix, which provides the correlations be tween the latent factors and each of the observed variables (i.e., factor loadings). This can be done by various fa ctor extraction methods th at are introduced later in this section.

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91 Factorability investigation : To identify common underlying dimensions that explain the patterns of collinearity am ong the variables, the observed variables have to be intercorrelated enough to be factorable, but they should not correlate t oo highly. It is important to avoid multicollinearity (i.e., variables that are very highly correlated ) and singularity (i.e., variables that are perfectly correlated) as thes e would cause difficulty in determining the unique contribution of the variables to a factor. The communality of a variable is the sum of the loadings of the variable on all extracted factors. This represents the proportion of the vari ance in that variable th at can be accounted for by all extracted factors. Thus, if the communality of a variable is high, the extracted factors account for a large proportion of the variables variance. This means that this particular variable is reflected well via the extracted factors, and he nce the factor analysis is reliable. When the communalities are not high, the sample size has to be large enough to compensate for this. To examine whether the sample is large enough to elicit a meaningful factor solution, the KaiserMeyer-Olkin (KMO) measure of sa mpling adequacy is used. The KMO statistic varies between 0 and 1. A value of 0 represents that the sum of partial correlations is large relative to the sum of correlations, indicating that factor analysis is likely to be in appropriate. A value close to 1 represents that patterns of correlations are rela tively compact, so factor analysis should yield distinct and reliable factors. Typically, a KMO statistic value of greater than 0.5 is acceptable to perform factor analysis. Bartletts test of spherity test s the null hypothesis that the orig inal correlation matrix is an identity matrix. When the correlation matrix is an identity matrix, ther e would be no correlations between the variables, eliminating the need for a fact or analytic procedure. Thus, this test has to

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92 be significant. A significance value ( p value) less than 0.05 is usua lly necessary to justify the factor analytic procedure. Multicollinearity and singularity problems can be detected by investigating the determinant of the correlation matrix. Usually, it is considered to not be a problem if the determinant is greater than 0.00001. Extraction of factors : A variety of statistical methods have been developed to extract latent factors from an inter-correlation matrix of the observed variable s. They include the principal component extraction method, the pr inciple axis extraction method, the maximum likelihood extraction method, the unweighted leas t-squares extraction method, the generalized least squares extraction method, the alpha extrac tion method, and the image factoring extraction method. The two most commonly used extraction methods are the principle component and principle axis methods. There are three types of variance in the variable s: common, specific, and error. Common variance is the variance in a variab le which is shared with all other variables in the analysis. Specific (unique) variance is the varian ce associated with only a specific variable. Error variance is the inherently unreliable random variation. The principle component method finds latent factors that maximize the amount of total variance (i.e., sum of common, specific, and error variances) that is expl ained, while the principle axis method finds latent factors that maximize the amount of common variance that is explained. The main difference between the two types of methods lies in the way the comm unalities are used. Co mmunality of a given variable is the proportion of its variance that can be accounted for by extracted factors. In the principle component method, it is assumed that a ll the communalities are in itially one (unities are inserted in the diagonal of the correlation matr ix). That is, the total variance of the variables

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93 can be accounted for by the extracted factors. On the other hand, with the principle axis method the initial communalities are not assumed to be on e (it does assume error variance). They are usually estimated by taking the squared multiple correl ations of the variables with other variables. These estimated communalities are then represen ted on the diagonal of th e correlation matrix, from which the eigenvalues are determ ined and factors are extracted. Theoretically, when the analyst is primarily concerned about determining the minimum number of factors needed to account for the maxi mum portion of the variance represented in the original set of variab les, and has prior knowledge suggestin g that specific and error variance represent a relatively small po rtion of the total variance, the principle component method is appropriate. In contrast, when the primary objective is to iden tify the latent dimensions or constructs represented in the or iginal variables, and the analyst has little knowledge about the amount of specific and error variance and theref ore wishes to eliminate these variances, the principle axis method is appropria te. Practically, however, both me thods are widely used and the solutions generated by each us ually do not differ significantly. Figure 3-4 shows an example of a path diagra m for an exploratory factor analytic model by the principle component extrac tion method. As discussed, this method finds latent factors that maximize the amount of total variance (of the observed variables) that is explained, and it is assumed that there is no error variance. An EFA model is constructed in the way that series of regression equations are set up to summarize its configuration. Equation 3.2 describes an EFA model by the principle component ex traction method. Each of the vari ables is defined as a linear combination of the factors (i.e., sum of the products of each latent factor variable and the factor loading of each observed variable on the corr esponding factor). Results from the EFA include derived loadings of each variable on each factor and calculated factor scores for each subject on

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94 each factor. The factor scores are a composite meas ure that can be used for subsequent analyses. When an orthogonal rotation method is used, the scor es of the factors can be considered to be independent of each other, and thus can be used as explanatory variables in a multiple regression analysis. k j j ij iF A V1) ( (3.2) where, iV = ith observed variable ( i = 1 to k k = number of observed variable s). These correspond to V1, V2, V10 in Figure 3-4. ijA = a factor loading of the ith variable on the jth latent factor jF = jth latent factor variable (a common, underlying dimension, j = 1 to k, k = number of observed variables). These correspo nd to F1, F2, and F3 in Figure 3-4. Determination of number of factors to be retained : The maximum number of factors that can be extracted is equal to the number of observed variables. However, the purpose of an EFA is to adequately explain a relatively larg e number of variables wi th a small number of factors. Thus, the analyst seeks to identify the smallest number of factors that explain a considerably large amount of vari ance in the observed variables. There are several criteria for the number of factors to be extracted, but these are just empirical guidelines rather than an exact quantita tive solution. In practice, most factor analysts seldom use a single criterion to d ecide on the number of factors to extract. Some of the most commonly used guidelines are latent root, percentage of variance, and scree test criteria. With the latent root (eigenvalue) cr iterion, only the factors having an eigenvalue greater than one are

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95 retained. It should be noted that the total sum of eigenvalues from the data is equal to the total number of variables and the varian ce of a single variable is consid ered as the eigenvalue of one. Thus, the rationale for the eigenvalue criteria is that any individual fact or should account for at least the variance of a single variable if it is to be retained for interpreta tion. The percentage of variance criterion is a different approach. Using this method, the cumulative percentages of the variance extracted by successive fact ors is the criterion. It is co mmon in social science research to consider a solution that accounts for at least 60% of the total variance as a satisfactory solution. Another common approach is the sc ree test criterion. The scree test is derived by plotting the latent roots (eigenvalues) agains t the number of factors in thei r order of extraction. The scree plot illustrates the rate of change in the magnitude of the eigenvalues for the factors. The rate of decline tends to be fast for the first few factors, but then levels off. Th e elbow, or the point at which the curve bends, is considered to indicate the maximum number of factors to extract. Rotation of factors : Once the number of factors to be retained is decided, the next logical step is to determine the method of rotatio n. The fundamental theorem of factor analysis is invariant within rotations. That is, the initial f actor matrix is not unique. There are an infinite number of solutions, which produce the same co rrelation matrix, by rotating the reference axes of the factor solution. A primar y objective of the rotation is to make each variable load highly on only one factor and have nearly zero loadings on the other factors. This simplifies the factor structure and helps to achieve a more meaningful and interpreta ble solution. The simplest case of rotation is an orthogon al rotation in which the angles between the refere nce axes of factors are maintained at 90 degrees. Thus, there is no corr elation between the extracted factors. A more complicated form of rotation allows the angle betw een the reference axes to be other than a right angle and is referred to as an oblique rotation. Th e factors are allowed to be correlated with each

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96 other in this type of rotation. Orthogonal rotation procedures are more commonly used than oblique rotation procedures because researchers ofte n try to obtain an independent set of factors to clarify the meaning of each factor. Th ree major orthogonal approaches are varimax, quartimax, and equamax rotation methods, and two major oblique approaches are direct oblimin and promax rotation methods. Criteria for the significance of factor loadings : If there are variables that load highly on two or more factors, or do not load highly on any factor, they are excluded from a factor solution because it is not clear which factor(s ) has an influence on th e variables and this ambiguous relationship between the factors and the variables blur the inte rpretation of a factor solution. Whether a factor loading of a variable is signi ficant or not depends on the sample size, the total number of observed variab les, and the total number of extr acted factors. The larger the sample size, the smaller the loading is considered to be significant. The larger the total number of variables, the smaller the loading is considered to be significant. The larger the number of factors, the larger the size of th e loading on latent fact ors is considered to be significant. As a rule of thumb, factor loadings greater than 0.5 are considered to be significant when the sample size is 120 or more, and factor loadings greater than 0.65 are considered to be significant when the samp le size is 70 or more. Naming of factors : Once the latent factors to be re tained, and the variables associated with each of those factors are identified, the an alyst attempts to assign some meaning to the factors based on the patterns of the factor loadings. It should be noted that the factor loadings represent the correlation, or linear association, be tween a variable and the latent factors. Thus, the analyst makes a determination as to what an underlying factor may re present, investigating all the variables loadings on the factor in terms of their size a nd sign. The larger the absolute

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97 magnitude of the factor loading for a variable, th e more important the variable is in interpreting the factor. The sign of the loadings also need to be considered in labeling the factors. It may be important to reverse the scoring of the negatively worded items in Likert-type instruments to prevent ambiguity. That is, in Likert-type inst ruments some items are often negatively worded so that high scores on these items actually reflect low degrees of the attitude or construct being measured. As the importance level of each traffic, ro adway, and/or control variable was evaluated on a 7-point interval-rating scale, EFA was applied to search for a set of latent factors that accounts for the patterns of collinearity among the variables. The extrac ted factors and their correlations with observed variables reflect in what respect each vari able contributes to truck trip quality and the degree to which the importance of each variable is explained by the underlying latent factors. The principle component extrac tion method was used to find the set of latent factors that accounts for the maximum amount of va riance of the observed variables. The results of the EFA presented a set of extr acted latent factors, the percent of trace, that is, the portion of the total variance (of the observe d variables) that is explaine d by each latent factor, and the correlations between each observed va riable and latent factor (i.e., factor loadings). It was not possible to apply Confirmatory Factor Analysis (CFA) or Structural Equation Modeling (SEM) statistical techniques to the surv ey data because no previous hypothetical model construct exists for the truck trip quality issue. 3.2.5.3 Multiple comparison test When an Analysis of Variance (ANOVA) test ve rifies that the means of multiple variables (i.e., more than two) are statistically different, multiple comparison procedures are widely used to determine which means are different from one another. Fishers Least Significance Difference (LSD), Tukeys W, Student-Newman-Keuls (S-N-K ), and Duncans tests are often used (Ott and

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98 Longnecker, 2006), assuming each sample from the groups is selected from a normal population with an equal variance. However, if it is no t reasonable to assume e qual variance, pair-wise multiple comparison procedures such as Tamh anes T2, Dunnetts T3, Games-Howell, or Dunnetts C tests can be used (Dunnett, 1980). As the importance of each hypothetical perfor mance measure was evaluated in a 7-point interval rating scale, the Games-Howell pair-w ise multiple comparison test was performed to investigate if the differences among the mean impor tance levels of the pe rformance measures are statistically significant. Normal Quantile-Quan tile (Q-Q) plots showed that each sample was approximately normally distributed (Figure 3-5) but the equal variance assumption did not hold according to Levenes tests (Levene, 1960). Thus, it was necessary to perform the multiple comparison tests with methods that do not rely on the equal variance assumption. The Games-Howell test was performed among the multiple comparison tests with unequal variance s to investigate every possible statistical difference in the pair-wise mean comparisons among the groups. The Games-Howell test is the most liberal, meaning that diffe rences between group means are id entified as being significant more readily with this test than the other tests. The Games-Howell test is a modification of S-NK procedure, using the q test statistic (i.e., stude ntized range statistic). Equation 3.3 is used to calculate the test statistic, reflecting heterogeneous variances and sample sizes in the error term in the denominator. 22 2 j j i i j in s n s q (3.3) where,

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99 q = studentized range test statistic i = calculated mean of the group i is = calculated standard deviation of the group i in = sample size of the group i Equation 3.4 is also used to calculate the degr ees of freedom for each pair-wise comparison to adjust the error term. The calculated test statistics are compared with critical q values with the corresponding degrees of freedom found in the studentized range tables. 1 12 2 2 2 2 2 2 j j j i i i j j i in n s n n s n s n s df (3.4) where, df = degrees of freedom for each pair-wise comparison 3.2.5.4 Non-parametric test The survey respondents backgrounds were co llected from various question types (i.e., ratio-scale, discrete choice, forced ranking, etc. ) and their perceptions on the importance of each hypothetical performance measure were evaluated on a 7-point interval rating scale. Thus, ordered probit modeling technique (Greene, 2000) was first applied in an attempt to explore respondents backgrounds that may explain the vari ance in their pe rceptions. However, with the ordered probit modeling, only a small number of pot ential explanatory va riables were found to be statistically significant, resulting in genera lly poor model fits. The Kruskal-Wallis test and Mann-Whitney test (non-parametric versi on of Analysis of Variance (ANOVA) and t-test) were

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100 applied to the data. The use of parametric ANOVA and the t-test was not appropriate even though it is more powerful, because it was not reasonable to assume normality and equal variances with the data. The Kruskal-Wallis test (Conover, 2001) is a non-parametric one-way ANOVA by ranks. It is used with k independent groups, where k is equal to or greater than 3, and measurement is at least ordinal. The sample sizes across the groups can vary because the samples are independent. The null hypothesis is that the k samples come from the same population. The alternative hypothesis states that at leas t one sample comes from a different population. Following H test statistic is used to test the hypothesis. k i i iN n R N N H1 2) 1 ( 3 ) 1 ( 12 (3.5) where, H = Kruskal-Wallis test statistic N = total sample size k = number of independent samples iR = sum of the ranks of group i in = sample size of group i The calculated H test statistic approximately fo llows a Chi-Squared distribution ( 2) with k 1 degrees of freedom. Thus, for a specified value of the null hypothesis is rejected when calculated H value exceeds the critical value of 2 for k 1 degrees of freedom. The Mann-Whitney test (Conover, 2001) is a non-parametric t-test by ranks. It is used specifically with two independent groups, and measur ement is also at least ordinal. The sample sizes between the two groups can vary because the samples are independent. The null hypothesis

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101 is that the two samples come from the same population. Following z test statistic is used to test the hypothesis. 12 ) 1 ( 22 1 2 1 min N n n n n U z (3.6) where, z = Mann-Whitney test statistic iU = minUnumber of independent samples minU = minimum of 1U and 2U (iU = sum of the ranks of group i) 1n = sample size of group 1 2n = sample size of group 2 N = total sample size (= 2 1n n) The calculated z test statistic follows a nor mal distribution. The null hyp othesis is rejected when the calculated z value exceeds the critical value of z for a specified value of 3.2.5.5 Chi-squared test The perceptions of the respondents on the pr eferred truck driving times of day were investigated with a binary choi ce question type. Binary logit m odeling technique was first tried to explore respondents backgrounds that are correlated with th e perceptions on their preferred truck driving times of day. However, only a small number of potential explanatory variables were statistically significant, again yielding poor model fits. A chi-squared test (Ott and Longnecker, 2006) was performed for each indivi dual background characteris tic to investigate whether their perceptions are dependent on it. The null hypothesis is that the respondents background and their perceptions are independent. The alternative hypothesis is that they are

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102 dependent; that is, the perceptions of the res pondents on truck driving time of day preference vary by their specific background. The two variab les are categorized in a two-way frequency table, and then the 2 test statistic is calcul ated to test the hypothesis as shown in Equations 3.7, 3.8, and 3.9. j i ij ij ijE E n, 2 2 (3.7) where, ijn = observed number of meas urement in the cell for the ith row and the jth column ijE = expected number of meas urement in the cell for the ith row and the jth column 1 1 c r df (3.8) where, df = degrees of freedom r = number of rows in the two-way table c = number of columns in the two-way table N C R Ej i ij (3.9) where, iR = total sum of the number of measurements in the cells of the ith row jC = total sum of the number of measurements in the cells of the jth column N = total sum of the num ber of measurements in the two-way table The null hypothesis is rejected when the calculated 2 value exceeds the critical value of 2 for a specified value of

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103 3.3 Truck LOS Measurement The ultimate objective of this study is to r ecommend effective methodologies to develop a mathematical model to estimate LOS for trucks on Florida roadways. This section describes what steps are required to satisfy the object ive and how each step could be performed to complete the study. The first step is to determine one or two performance measures, or multiple factors, upon which truck LOS on a specific roadwa y type can be adequately assessed. It is preferable to specify just one or two perfor mance measures to simplify truck LOS estimation methodologies, but if it is not possible, divers e important factors should be simultaneously factored into the development of the methodologies. The focus group and survey studies of this study were conducted for this purpose. Once the truck LOS determinants are identified for each roadway facility type, some experimental data are required to develop mathematical truck LOS estimation models through appropriate statistical analysis methods. Several different approaches can be considered to for the data collection. Gi ven the data, the correlations between the truck LOS determinants and the truck drivers percep tions of LOS could be measured by appropriate statistical modeling techniques to develop truck LOS estimation models, which will then be used to predict truck LOS on a specific route. The final truck LOS estimation models should reflect perceptions of most truck drivers working in Florida and be easily ap plicable to most Florida roadways. It is preferable to select explanatory variables, which are simply measurab le with the data obtaine d from the performance monitoring system of FDOT. 3.3.1 Truck LOS Service Measures It was indicated in the focus group studies that what is important for LOS perceived by the trucking community varies by different roadway types. This means that truck LOS service measures (or truck LOS determinants) should diffe r according to the type of roadway that truck

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104 drivers travel on as current HCM represents (Tab le 3-9). This study id entified the truck LOS service measures (or truck LOS determinants) for each roadway type from the focus group and survey study results. The two different approa ches were considered to perform this task. 3.3.1.1 Single performance measure approach It may be possible to represen t overall perceptions of truck drivers on truck trip quality only by one or two important truck driving or traffic c ondition effectively. As shown in Table 39, the current HCM uses one or two performance measures to determine LOS on a specific roadway type for all the vehicles in a traffic st ream. Several important truck driving conditions for each roadway type were postulated from the previous focus group studies and evaluated in the survey study as to how each of these conditions is solely applicable to assess truck trip quality. It was intended to investigate if there are any one or two perfor mance measure(s) that may be used as truck LOS service measure(s) on a specific roadway type. Such performance measures may be measured from the field di rectly, or derived from data obtained by the performance monitoring system of FDOT. 3.3.1.2 Multiple variable approach If the perceptions of truck mode users on truck trip quality have multidimensional characteristics; that is, it is not appropriate to represent them with just one or two performance measures, the truck LOS estimation model should reflect various natures of truck operations. The model may be expressed as a function that yields an LOS index value based upon a number of independent variables (e.g., flow, speed, number of lanes, pavement condition, etc.) and their corresponding coefficients. The experimental data to calibrate truck LOS estimation model should include the factors identified as important in the focus group and survey studies as well as LOS perceptions of a sample of truck drivers. The final model may use only the variables showing significant effects on truc k LOS in the experimental data.

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105 3.3.2 Truck LOS Estimation Model Once the truck LOS performance measure(s) (o r truck LOS determinants) are identified, experimental data are required to calibrate truck LOS estimati on model for each roadway type. The following four different approaches can be considered to collect the experimental data: video simulation; vehicle simulator; in-field dr iving experiment; and truc k operational data from a trucking company. In the experiments, various scenarios with different levels of the factors should be tested with a representative sample of truck drivers in Florid a. The selected truck drivers experience maneuvering a truck directly or indirectly in each of the scenarios, and then rate it in terms of their trip quality. The results of the experi ments should be analyzed with appropriate statistical modeling techniques to develop truck LOS estimation models, which will be used to predict LOS on a specific route for trucks. 3.3.2.1 Data collection Experimental data to calibrate truck LOS estimation models can be collected by video simulation, vehicle simulator, in -field driving experiment, or truck operational data from a trucking company. In a video simulation experime nt, a truck driver view s are video-recorded while he/she is actually experien cing various driving conditions in th e field. It is required to prepare enough number of video clips reflecting various traffic and roadway conditions for the experimental purposes. The collect ed videos are displayed to a sa mple of truck drivers so that they rate each video clip in terms of their trip quality. It may be impossible for the drivers to experience the pavement quality of a road with this method. A driving simulator may also be used for the same purpose, but it is not known wh ether any truck driving si mulator exists. Most driving simulators are designed to mimic passenger ca r driving environment. If available, it may reflect more realistic driving environments in th at the participants are able to manipulate their vehicle speed and directions, but programming e nough number of scenarios to be displayed may

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106 pose a challenge and a pavement quality is difficult to be simulated. The in-field driving approach probably provides a best set of data because participants are exposed to the actual truck driving environment before evaluating LOS on the transportation facility. However, replicating field conditions is very difficult, and it is dangerous to be in the field. Huge recruiting efforts are required as well. It was indi cated from the focus group discussi on that some truck companies collect truck operational data (e.g., travel time) from a GPS system equipped in their trucks. The data are regularly updated and used to reflect current truck driving condition for future truck route and departure time selec tion. If possible, the data s upplemented by other traffic and roadway field data may be used to calibrate tr uck LOS estimation model. In this case, the perceptions of the truck drivers on LOS of their trip should be asked at their arrival. Advantages and disadvantages of the four experimental data collection methods are summarized as follows. Video simulation: This approach has shown promise in other studies. However, it may be more difficult to implement from a truck dr ivers perspective. This approach also has limitations with regard to accura tely reflecting pavement condition, which is a major concern for truck drivers. Vehicle simulator, using truck cab: Pavement quality could be simulated. Motion sickness a significant issue for participant recruiting. Programming the required number of scenar ios to be displayed may be very costly. In-field driving experiment: May provide most accurate feedback from participants. Difficult to replicate desired traffic cond itions for each participant at each location It can be difficult to find all the variou s experimental conditions in the field. Huge recruiting e ffort is required.

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107 Having a truck company allow researchers to tr avel along on previously planned trips, and ask questions during the trip, ma y be a potential data source. Truck operational data from a truck company: It was indicated from focus group discussions that some truck companies use an onboard recording system for speed da ta to regularly update expect ed travel time on the routes. Truck driver focus group particip ants stated that most truc ks are equipped with GPS and controlled by computers. It may be possible that some truck operational data required to develop LOS models can be obtained while truck driver s make real deliveries. However, some supplemental data will probab ly still need to be collected along the specific truck routes, such as general tr affic stream variables (e.g., speed, volume). 3.3.2.2 Statistical modeling The contemporary transportation community has widely adopted LOS estimation procedures provided by HCM as the primary mean s of measuring system performance of each type of facility. The six LOS values defined th rough the procedures range from A to F, with A representing very good operating conditi ons and F representing very poor operating conditions. To be consistent with this re presentation, truck driver s participating in the experiment should be asked to rate their trip qua lity with one letter desi gnation from A to F (or an equivalent numerical scale, such as 16). Statistical modeling involves correlating the response variables (i.e., LOS values) with multiple factors contributing to truck trip quality. Given the fact that the values of the response va riables are ordered, and di screte, ordered discrete choice modeling techniques (i.e., ordered logit or ordered probit modeling) may be appropriate to develop the truck LOS estimation models. Was hburn and Kirschner (2006) used ordered probit modeling technique to develop rural freeway LO S estimation models. The final models should provide truck LOS predictions on va rious roadways as single lett er designations, given all the necessary explanatory variables.

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108 : : :Development of Guidelines for LOS Analysis of Truck Mode Development of Guidelines for LOS Analysis of Truck Mode Survey Questionnaire Development Survey Distribution Survey Distribution Analysis of Survey Responses Focus Group Questionnaire Design Focus Group Meetings Focus Group Meetings Analysis of Focus Group Discussions Figure 3-1. Research Approach Table 3-1. Survey Development Survey Issues Question Types Analysis Methods Background Characteristics Various Question Types Descriptive Statistics, Frequency Distributions Preference on Truck Driving Times of Da yDiscrete Choice Frequency Distributions, Chi-squared Tests Relative Importance/Sa tisfaction of Each Factor on Truck Trip Quality Relative Interval Rating Descriptive Statistics, Exploratory Factor Analysis (EFA) Relative Importance of Each Category of Factors on Truck Trip Quality Forced Ranking Descriptive Statistics, (Cumulative) Frequency Distributions Applicability of Single Hypothetical Performance Measure to Estimate Truck Trip Quality Interval Rating Descriptive Statistics, Multiple Comparisons of the Means, Non-parametric Tests Relative Improvement Priority for Each Type of Roadway Facility Forced Ranking Descriptive Statistics, Frequency Distributions

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109 Table 3-2. Postage-Paid Tr uck Driver Survey Response Rates (number of surveys returned/number of surveys distributed) Roadway Types Issues on the Second Page of the Survey Freeways Urban Arterials Two-lane Highways Response Rate for Each Type of Survey 187/2000 (9.35%) 66/1000 (6.6%) 58/1000 (5.8%) Overall Response Rate 311/4000 (7.78%) Figure 3-2. Truck Drivi ng Competition Course

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110 Figure 3-3. Survey Table Set up at Truck Driving Competition Table 3-3. Survey Participation of the Sele cted Carriers by Each Conference and Chapter (number of carriers from whom the survey s were received/number of carriers to whom the surveys were distributed) Chapter Conference Central West North Florida South Florida Central East West Florida Total Dump Truck Carriers 0/0 0/1 0/1 0/1 0/0 0/3 Common Carriers 5/8 3/7 1/5 2/3 0/2 11/25 Household Goods Carriers 0/0 0/1 0/0 0/0 0/0 0/1 Private Carriers 2/9 1/5 2/2 1/3 0/3 6/22 Special Riggers 0/0 0/1 0/1 0/1 0/0 0/3 Tank Carriers 1/2 0/1 1/1 0/1 0/0 2/5 Total 8/19 4/16 4/10 3/9 0/5 19/59

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111 Table 3-4. Survey Collection by Each C onference and Chapter (number of surveys received/number of surveys distributed) Chapter Conference Central West North Florida South Florida Central East West Florida Total Dump Truck Carriers 0/0 0/5 0/5 0/5 0/0 0/15 Common Carriers 9/40 5/35 2/25 3/15 0/10 19/125 Household Goods Carriers 0/0 0/5 0/0 0/0 0/0 0/5 Private Carriers 2/50 1/25 2/10 1/15 0/15 6/115 Special Riggers 0/0 0/5 0/5 0/5 0/0 0/15 Tank Carriers 1/10 0/5 1/5 0/5 0/0 2/25 Total 12/100 6/80 5/50 4/45 0/25 27/300 Table 3-5. FTDC Truck Driver Survey Data Us ability (number of valid surveys / total number of surveys received) Roadway Types Issues Freeways Urban Arterials Two-lane Highways Relative Importance of E ach Factor 58/148 43/148 36/148 Relative Satisfaction of E ach Factor 66/148 56/148 43/148 Relative Importance & Sa tisfaction of Each Factor 54/148 39/148 33/148 Relative Importance of Each Factor Category 40/148 34/148 37/148 Applicability of Hypothetic al Single Performance Measure 116/148 115/148 116/148 Relative Transportation Servi ce Improvement Priority 25/148 Table 3-6. Postage-Paid Truck Dr iver Survey Data Usability ( number of valid surveys / total number of surveys received) Roadway Types Issues Freeways Urban Arterials Two-lane Highways Relative Importance of E ach Factor 108/187 33/66 33/58 Relative Satisfaction of E ach Factor 121/187 41/66 35/58 Relative Importance & Sa tisfaction of Each Factor 105/187 29/66 31/58 Applicability of Hypothetic al Single Performance Measure 273/311 272/311 269/311

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112 Table 3-7. Overall Truck Driver Survey Data Us ability (number of vali d surveys / total number of surveys received) Roadway Types Issues Freeways Urban Arterials Two-lane Highways Relative Importance of E ach Factor 167/335 76/214 69/206 Relative Satisfaction of Each Factor 187335 97/214 78/206 Relative Importance & Sa tisfaction of Each Factor 159/335 68/214 64/206 Relative Importance of Each Factor Category 40/148 34/148 37/148 Applicability of Hypothetic al Single Performance Measure 389/459 387/459 385/459 Relative Transportation Servi ce Improvement Priority 25/148 Table 3-8. Overall Truck Company Manager Survey Data Usability (number of valid surveys / total number of surveys received) Roadway Types Issues Freeways Urban Arterials Two-lane Highways Relative Importance of Each Factor on Operating Cost 33/38 7/11 7/11 Relative Importance of E ach Factor on On-Time Performance 34/38 8/11 8/11 Relative Importance of Each Factor on Truck Drivers Trip Satisfaction 36/38 9/11 8/11 Relative Importance of Each Factor Category 7/11 6/11 5/11 Relative Importance of Each Aspect of Truck Driving Condition 35/38 33/38 34/38 Relative Transportation Servi ce Improvement Priority 5/11

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113 Travel Time (F1) Travel Safety (F2) Driving Comfort (F3) Shoulder Width and Condition (V7) Availability of Clear Signage (V8) Pavement Condition (V9) Lane Width and Condition (V10) Accident ot Incident Frequency (V1) Availability of Alternative Route (V2) Construction Frequency (V3) Level of Congestion (V4) Lighting Conditions at Night (V5) Other Drivers' Road Etiquette (V6) Figure 3-4. Example of a Path Diagram for an EFA Model by Principl e Component Extraction Method Figure 3-5. Normal Q-Q Plot of the Importance of Ease of Obtaining Useful Traveler Information on Freeways

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114 Table 3-9. Current HCM Service Meas ures used for LOS Determination Roadway Types Service Measure for all the vehicles in a traffic stream Freeway (Basic Segment) Density Multilane highway Density Two-lane highway Average Speed, Percent-Time-Spent-Following Urban arterial Average Speed

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115 CHAPTER 4 FOCUS GROUP STUDY RESULTS This chapter summarizes the findings from the three focus group meetings held for this study. The perceptions of drivers and managers are presented separa tely to be compared to each other. The factors important to the following four issues are listed and di scussed in order: truck route and departure time selec tion, truck trip quality, improveme nt priority of transportation services and truck delivery schedule reliability. The factors contribu ting to quality of a truck trip are clustered by each type of transportation facility. 4.1 Perceptions of Truck Drivers The following section describes the study results from the two focus group meetings with truck drivers. A total of nine drivers participated in the meeti ngs; one with five drivers and the other with four. Seven drivers were from th e for-hire Less-than-Truck Load (LTL) carriers delivering various types of goods while the other two were fr om private Truck Load (TL) operators carrying mainly foods. They all have truck driving experience of more than 15 years, with their ages ranging from 40 to 59 years. Se ven of the participants were FTA Road Team members. The Road Team members, in particular were very concerned with safety issues, as one of their important duties is to give safety demonstrations to the public. The socio-economic and working characteristics of the participan ts were collected through a background survey (Appendix E for the survey results). The text pr esented in this section only includes summaries and paraphrasing of the comments made by th e focus group participants. Thus, all the statements are a reflection of the focus group part icipants perceptions, not the personal opinions of the author.

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116 4.1.1 Truck Route and Departure Time Selection For most truck drivers, truck route and depart ure time decisions are made by the managers at their trucking companies, based primarily on trav el safety, time, and distance. However, when truck drivers run into adverse weather or abnormal traffic or roadway conditions (e.g., severe congestion, low overpasses) on desi gnated routes, they may call the central dispatch office to obtain permission to change routes Some drivers select their ow n routes to meet the delivery times set up by their trucking companies. They choos e what they believe to be the best route for the times. City drivers, especially P&D (Pic k-up and Delivery) drivers pick the routes by themselves for the most efficient delivery. Th ey try to minimize back tracking situations by coordinating the order in which th eir trailers are loaded with the delivery times appointed for each customer. Some owner-operators (independe nt truck drivers) have designated routes by which they get paid. They need permission to de viate from the designated routes in order to still get paid for those different routes. Truck company managers typically use a rout ing software program to find the shortest route with respect to either time or distance. They also consider overall truck operating cost, travel time variability, driver comfort, etc. to ultimately choose the most efficient route for their trucking business. That is, the shortest routes may not be selected if signif icant chances of safety problems (e.g., narrow lane or shoulder widths, pe destrians or wildlife crossing) or excessive cost (e.g., fuel, toll) are involved in traveling the routes. Truc k drivers usually are not allowed to change designated routes to travel on toll roads unless they have a really important shipment, or the designated routes are complete ly blocked at the time of delivery (e.g., I-4 shut down due to crash investigation activities). It may be more important for LTL drivers than for TL drivers to stay on the designated routes. They have other potential destinati ons along the routes that their trucking companies may want them to reach dur ing the deliveries. Th e new reduced hours of

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117 service (amounts of time truck drivers can travel per day) also discour age the LTL drivers from changing routes. Drivers that d ecide on their own to change rout es can be held responsible if they were not able to make deliveries and come back to the base within the hours of service. Truck company managers also like to keep their drivers on routes that keep them within cell phone communication range. Most truck drivers prefer to tr avel on freeways as much as possible because of their shorter travel times and better perceived safety level. They try to stay off back roads (e.g., two-lane highways, arterials), unless they happen to be mu ch shorter in traveling distance. From hub to hub, most trucking facilities are locat ed on or near the interstates, reducing the need of truck drivers to travel on the back roads. A lot of back roads are not well suited for accommodating large, heavy trucks, especially double trailers. Truck drivers can get stuck on those back roads when they have to back up or turn around. They sometimes get a little nervous with low bridges, small towns (e.g., short turning radii), animals, fre quent stop lights, or unexpected pedestrians. Nevertheless, they will definite ly utilize the back roads when there is an accident or other unexpected event on the freeway that brings about considerable delay. Back roads serve a vital role today in the transportation system due to the increasing delays caus ed by the ever growing traffic volume and construction activities on Florida s interstates. Of the back roads, most truck drivers prefer traveling on multilane highways rath er than two-lane highways due to a perceived higher level of safety. Trucking companies set up the lo ads to be delivered at a specific time or within a time window. Truck company managers typically use an average speed of 47 mph in their calculations for travel time. This speed takes into account the various stops (e.g., fuel) that a driver may make during their trip, as well as occa sional slow downs due to traffic congestion. If

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118 traffic conditions are not severe, use of this sp eed value usually result s in ample time for the drivers to reach their destination on time. 4.1.2 Factors Affecting Truck Trip Quality The three most important concerns of truck driv ers for evaluating the quality of a truck trip are the perceived safety level, dr iving comfort, and total travel tim e. The factors contributing to those concerns vary by which type of roadways truck drivers travel on (e.g., freeways, 2-lane highways, etc.). Truck lane restrictions, speed differential between trucks and cars, motoring publics attitudes, level of congestion, and constr uction activities affect th eir perception of truck trip quality on freeways the most, while physical roadway conditions such as shoulder widths and condition, curb radii, lane widths, pavement condition are the most important determinants on their perceived truck trip quality on arterial s or two-lane highways. The motoring publics knowledge and attitude about trucks and truck drivers are important to truck drivers regardless of the roadway type, but their effects on the perceive d quality of a truck trip is greater on freeways than on the other facilities. Truck drivers are also sensitiv e to the potential presence of pedestrians, wildlife, or farming equipmen t on two-lane highways for safety reasons. 4.1.2.1 Factors affecting truck trip quality on freeways Consistently good traffic flow and safety are th e two major factors considered to determine truck trip quality on freeways. Truck lane restrictions, speed differential between trucks and automobiles, the motoring publics attitude and knowledge of truck driving, etc. influence the traffic flow and safety condition of a freeway. Most truck drivers are very satisfied with the lighting conditions on Floridas fr eeways, especially at the interc hange areas. The frequency and timing of construction activ ities are other major con cerns of truck drivers. Truck lane restrictions: No trucks are allowed in the left-most lane on some interstate highways in Florida. Truck drivers feel that th e truck lane restriction makes the overall traffic

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119 flow worse and is also an unfair policy for truc k traffic. Truck driv ers have difficulty in consistently maintaining a desired speed in the ri ght-side lanes. They often have to drive in a traffic stream where some passenger cars travel much slower than other cars or others cut in their way to merge into or diverge from the freeway. It is difficult for drivers of large trucks with low acceleration and deceleration capabili ties to travel comfortably within the traffic flow with all the slow vehicles and merging or di verging traffic, especially with the maneuverability of passenger cars being much better. In addition, much of the motoring public does not know how to drive around trucks safely. Truck drivers need to always be attentive to their often abrupt behaviors. On the other hand, large trucks traveling in the right -side lanes make it more difficult for the passenger car drivers to find a safe gap to merge on to or off of the freeway. When a line of large trucks are formed behind slow vehicles, mane uverability of the passenge r cars is limited to a great degree. During the peak hours, traffic in the right-side lanes is extremely congested with a considerable amount of onand off-ramp traffi c. Some independent truck drivers get really impatient and go around the right-side lanes by usi ng the left-most lane to maintain their high desired speed even though it is no t allowed. They usually get paid by every mile and/or for a drop. Shortening travel time and di stance would be more important for them than for other truck drivers. Those drivers run right behind other truc k drivers, flashing their lights to get them move out of their way. Speed differential between trucks and autos: The major truck companies restrict the maximum speed of their trucks for safety and fu el economy through the use of engine governors. This maximum speed is usually around 65 mi/h. Independent truck drivers typically do not use speed governors; thus, their maximum speed is us ually much higher than 65 mi/h. Additionally, some states, though not Florida, have implemen ted a lower posted speed limit on the freeways

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120 for trucks than passenger vehicles This truck speed limit is usually 5 or 10 mi/h lower than the passenger vehicle speed limit. Whether by engine governing or speed limit posting, truck drivers strongly oppose these speed differentials. They f eel that a safe and efficient traffic flow is greatly hampered by the speed differential between tr ucks and other vehicles. It is hard for truck drivers to consistently maintain a safe following distance with the restricted speed limit. They often get tailgated or meet other drivers cutting in front of them. Motoring publics attitudes: The motoring public often has a negative impression of trucks and truck drivers even though they are carrying goods necessary for the publics lives. Courteous driving behavior by the motoring public is important to the quality of a truck trip. Passenger vehicle drivers often get aggressive around large trucks, not kn owing or ignoring the characteristics and limitations of truck driv ing. They should be cautious and knowledgeable enough to mingle with large trucks in a traffic stream safely. In this respect, it is important to publicize the importance of the tr ucking industry to our society and educate the motoring public about how to safely drive around large trucks in the traffic stream. Many passenger car drivers just want to go faster than ev erybody else. A big crash often occurs with their unpredictable aggressive driving be havior around trucks. Truck-only lanes: The designation of truck-only lanes is a good idea only if they are in the left-hand lanes. When truck-only lanes are designated to the right-hand lanes on freeways, it may just result in restricting the truck drivers us e of all the other lanes on the left side. Other types of vehicles still have to use the right-hand lanes for getting on and off the freeways, and this weaving activity aro und the trucks can pose a significant safety hazard. A couple of drivers provided a specific exampl e in Chicago on I-94. Along this freeway is 5 or 6 lanes in each direction. The right two la nes are for trucks only. Truck drivers cannot use

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121 the 3 or 4 lanes on the left side (the penalty for using them is $250 and 3 points). Truck drivers travel at a low speed of about 30 miles per hour constantly in the right two lanes on the freeway because of the number of merging and diverging drivers and th eir aggressive driving behavior, and increased truck density. They tr y to maintain a safe following distance. As a result, many truck drivers try to avoid th is section of the fr eeway if possible. Truck drivers would like to have more alterna tive routes, preferably some that only allow trucks on them. The truck-only routes could relieve the congestion on the freeways, improving traffic flow to a great degree by separa ting trucks from other vehicle types. Frequency of rest areas: Long-haul drivers often need to take a rest or get some sleep at rest areas or truck plazas. However, there are generally not enough rest areas or truck parking spaces along the freeways. In South Florida, the nearest place for drivers to park is at a mile marker 130. There is no truck parking f acility south of this mile marker. Frequency and timing of construction activities: More and more construction activities are being planned and conducted on major Interstates in Florida. Truck drivers choose to save travel time by taking back roads rather than tr aveling on the freeways with frequent construction activities. This makes the transportation serv ice improvements on the ot her facilities (e.g., twolane highways, multilane highways, or arterials) more important. They also want construction activities to be scheduled to avoid holidays or major activities like ra cing days in Daytona. Traveler Information Systems (TIS): Truck drivers appreciate the availability of TIS such as Variable Message Signs (VMS), Highway Advisory Radio (HAR), Citizen Band Radio (CBR), XM radio (a satellite ra dio service), 511 (Americas trav eler information phone number), etc. However, they would like to hear more th an just current traffic and roadway conditions or expected travel time on their route or greeting me ssages. They want to be informed of better

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122 ways to reach their destination when the driving condition on their traveli ng route is expected to be poor, considering travel time and/or safet y. The extensive use of XM radio for TIS is recommended in that a significant portion of truc k drivers listen to the traffic channels on the radio. Additionally, truck drivers look forward to using the 511 servic e all over the state of Florida, not just in the Tampa region. 4.1.2.2 Factors affecting truck trip quality on urban arterials The perceived quality of a truck trip on ar terials primarily depends on physical roadway conditions, in terms of safe and efficient through or turning movements of trucks. These factors generally include curb radii, la ne and shoulder widths, locations of trees, poles, street hardware, and utility lines. The roadway infrastructu re in some old towns was not designed to accommodate many of the larger trucks on the road today. Truck drivers also want to minimize stops and delays while traveling along an arterial. The control factors such as traffic signal spacing, yellow interval signal timing, traffic si gnal responsiveness and coordination were all mentioned by the drivers as influencing their percep tion of the quality of their trip. Availability and condition of signage and marking are importa nt for guidance to thei r destination. Other drivers behavior is also cons idered to have an impact on truck trip quality on arterials. Ease of turning maneuvers: The main concern of truc k drivers on arterials is the difficulty in making turning movements. Inadeq uate curb radii, misplaced trees and poles on corners, improper locations of stop lines at inte rsections, and the motoring publics poor attitudes about trucks and truck drivers ar e the primary factors affecting th is concern. The new areas of a town usually have wide enough roads and intersec tions for P&D (Pick-up and Delivery) drivers to make a turning maneuver, but most old part s of a town are not physically suitable to accommodate truck traffic. It is easier for truck dr ivers to make a left turn with a protected left turn traffic signal than to make several right turn s to go left. If a left turn signal phase operates

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123 as permitted only, it is hard for truck drivers to find a large enough gap to clear the intersection. When there is a very wide median along an arte rial, they could do a two-stage maneuver to make a left turn instead of waiting long for both ways to be cleared. Inexperienced drivers are much more likely to have an accident making a left turn than a right turn because they are naturally inclined to thinking that left turns are not difficult They are relatively easier than right turns, but still not easy. At many intersec tions, there is not enough room on th e right side of a truck for a safe right turn to be made. It is also hard for truck drivers to notice bicyclists or pedestrians crossing on the right side of a truck. Truck driver s will not attempt to make a U turn unless it is absolutely necessary. It is considered to be a very high-risk maneuver. Truck drivers are generally opposed to closing medians for access ma nagement purposes, as this usually increases the odds of having to make a U-turn. They woul d much rather have a ccess to and from their destination driveway th rough a traffic signal. Level of congestion: The congestion level of an arterial is closely re lated to the quality of a truck trip. As the roads get cr owded with cars, bikes, or pedest rians, truck drivers have more difficulty driving safely at a desired speed. They experience more delay and stop-and-go conditions. It also gets hard for them to change lanes or make turns. As the congestion level of a road goes up, the more concerned truck drivers beco me with the safety conditions of the arterial. Number of stop-and-go conditions: It requires a lot more atte ntion and effort to stop and re-maneuver a truck than any other vehicle on th e road. Thus, truck drivers are reluctant to travel on an arterial with heavy tr affic or short intersection spacing. Familiarity of the roads: Truck drivers are more likely to run into problems when they travel on arterials they have never been on. They might encounter such p hysical constraints as

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124 inadequate curb radii, low overhanging trees, mispl aced poles and trees at the corner, etc, or may have difficulty locating th eir delivery destination. Signage conditions: Missing or poorly mainta ined street name or truck route signs are a source of concern for truck drivers. Also, sign positioning can restrict sight distance in some instances. One driver gave a specific example of being stopped at the end of an exit ramp and a street sign blocking their view of oncoming traffic from the left. If the ramp is not signalized, they are forced to pull further in front of the sign and put th eir bumper out into traffic. Motoring publics attitudes: The general motoring public are either unaware of the characteristics of truck driving or just ignore them when they are driving on arterials. The majority of them do not know that there is a blind spot on the right side of trucks when truck drivers are making a right-turn maneuver. They just try to sneak in every space they can find around the trucks in order to ge t by them as fast as possible. The motoring public sometimes stops beyond the stop lines at in tersection approaches, either through ignorance or indifference, and thus making it very difficulty for trucks to make a turning maneuver from an adjacent approach. In these cases, a truc k driver will often find themselves needing to back up at some point during the turning maneuver. However, it is very dangerous for truck drivers to back up and many drivers will not do it, inst ead opting to just wait for the other vehicle drivers to make space, even if their truck is blocking the inte rsection. The motoring public should understand the limitations of truck driving and give trucks plenty of room, but they often try to use all the space around the trucks to go faster. Educating th e motoring public to understand and support truck operations on arterials is essentia l as the volume of truck traffic continues to grow in Florida. Trees, electrical lines: Truck drivers need to be more alert for overhead and side objects, such as low hanging trees, power lines, TV lines, telephone lines, and street furniture, on arterials

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125 than on the open highway. Trees seem to be beco ming a more popular arteri al roadside feature, but are just problematic for truck drivers, as they often damage their trailers by colliding with branches. Truck drivers are especi ally not fond of tree canopy roads. Length of yellow interval signal timing: Truck drivers prefer to have longer yellow intervals for clearing the signalized intersections sa fely. They are apt to be in a predicament if they decide to run through the intersections at the moment when the light changes to yellow. Stopping before entering the inte rsection is difficult with the poor braking capabilities of the truck, and clearing the intersection with the larg e size of their vehicles before the conflicting movements begin takes several s econds. Additionally, very short green intervals (of just a few seconds) are very undesirable. Unless they are fi rst in queue, there is little chance for them to accelerate in time to clear intersection. At signa lized intersections near highway-railroad grade crossings, truck drivers often have to run the re d light to avoid stopping on the tracks. The loop detectors are usually located at the clear storage distance areas that can only accommodate passenger cars. Thus, trucks cannot be in the right position to trigger a green signal. Traffic signal responsiveness: During the late night hours, drivers sometimes have to wait a long time at an intersection approach for the green signal even when there is no other traffic on the other approaches. The traffic signa ls need to respond to th e approaching vehicles to eliminate unnecessary delay. It is also impor tant to assign green sign als fairly to all the vehicles approaching an intersec tion. Truck drivers are likely to get more impatient when the green signal assignment seems unfair to them. Truck lane restrictions: Trucks are restricted to the right-hand lanes at many towns in Florida. When traffic is backed up and trucks are lined up in the right hand lane at a signalized intersection, only 2 or 3 trucks can get through the inte rsection within one tr affic signal cycle due

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126 to their low acceleration cap abilities. The passenger car driv ers behind the trucks probably get frustrated, especially the right turners. 4.1.2.3 Factors affecting truck trip quality on two-lane highways Truck drivers generally do not like to travel on two-lane hi ghways. It usually exposes them to additional safety hazards, while not saving them any travel time relative to the freeways. However, they do often use two-lane highway s when there are accidents or construction activities on the freeways. They also use them to go around some routinely congested stretches of freeways. Sometimes, they are the only rout e to their destination. The importance of twolane highways is increasing with th e increase of growth and interstate traffic in Florida. Truck drivers are most concerned with the physical road way conditions for the quality of a truck trip on two-lane highways. The lane and/or shoulder widths are often too na rrow to provide truck drivers with much room for error. Even 12-feet of lane width (the st andard lane width) are considered narrow for these roadways. And if th ey should encounter a problem and need to pull off the road, many two-lane highways have either no shoulder or a narrow shoulder. Even for locations with a shoulder, it is often not paved, a nd in wet weather, will be too soft to support the weight of a loaded truck and tr ailer. Some truck drivers r outinely encounter pedestrians, bicyclists, farming equipment, or wildlife cros sing on two-lane highways that pose a safety hazard. Additionally, when a low speed vehicle is encountered, the only way to pass is usually in the oncoming lane, which truck drivers are very reluctant to do. Thus their perceived trip quality deteriorates rapidly in this situation. Shoulder width and condition: Many stretches of two-lane highway contain no shoulder at all. For other locations that include a shoulder, it is often no t wide enough to park a truck on (in case of emergency) without partially blocking the travel lane. This can be a very dangerous situation for both the truck driv er and passing passenger vehicles. Additionally, paved shoulders

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127 are strongly preferred. Some driv ers recalled stories of when they pulled their truck off onto an unpaved, wet shoulder. In some instances, the shoul der was so soft that their truck tipped over, and thus two trucks were needed to remove it; one to stand it up and the other to pull it out. Many two-lane highways also have large drainage ditches on the side of the road. Truck drivers feel it is less risky to travel on multilane highways than on two-lane highways because the shoulders on most multilane highw ays are wider and better paved. Crowning condition: Some roads are reverse crowned or have a significant side slope, which makes it more challenging for the driver to keep the truck from st eering onto the shoulder or into the opposing lane. Pavement condition: Some roads are grooved and not ma intained properly. There is only a little pavement or patches al ong the roads. The poor pavement condition makes the tires wear out faster and inhibits the safe delivery of fragile goods or hazardous materials. Lane widths: Most roadway lanes are tight fo r truck movements. Even though a significant portion of them have the standard lane width of 12 f eet, it does not allow truck drivers much room for error. This problem gets bigg er on roads with constr uction activities, where travel lanes are often narrowed. Unexpected pedestrians, wildlife, or others: Pedestrians, wildlife, or others in close proximity to two-lane highways can pose a safety hazard. For some truck drivers, it is a major deterrent to traveling on two-lane highways. It is not rare to encounter some folks out there on the roads that are sleepy, tired, or drunk in the very early morning, late evening, or in the summer. Wildlife such as deer, opossums, or raccoons of ten shows up on two-lane highways. On a twolane highway in a farming community, truck drivers need to be careful not to conflict with farm trucks, peanut wagons, cotton hoppers, or the like.

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128 Passing maneuvers on two-lane highways: When truck drivers encounter somebody who is going at a considerably lower speed than the roadway speed limit, most of them usually try to pass him/her even though they have to take significant risks due to the large size and low acceleration capability of their truck. If a passing lane is upcoming, they will usually wait for that, but many two-lane highways do not provide passing lanes. If a l eading vehicle is only going a little slower than the tr uck drivers desired speed, they usually will not try to pass. However, their trip quality is definitely negatively affected. 4.1.2.4 Factors affecting truck tr ip quality on hub facilities The perceived safety level and operation of access to a hub facility are the two major concerns of the truck drivers using hub facili ties. There are some access highways that are routinely congested. Those roads need traffic sign als or wide medians for truck drivers to get in and out of the traffic streams eas ily. It is good to have hub facili ties at some locations where the access of truck traffic is easy. Truck drivers so metimes have to wait for a long time for their freight to be unloaded at so me hub facilities because of overbooked appointments. Some receivers or receiving department s at retail stores are not suppor tive to get their shipments from truck drivers at a scheduled time. It is a si gnificant problem for truc king companies and truck drivers, especially the drivers who get paid by the miles because they are not paid for waiting there. Some trucking companies stipulate how to compensate for the delay into the contracts with their customers. The old and deteriorated pavement condition at some old hub facilities is another problem. 4.1.3 Improvement Priority of Transportation Services It was inferred from the focus group discussions that the order of roadway types in which more improvements are needed, are two-lane highways, freeways, multilane highways, and urban arterial facilities, from the truck drivers perspective. The increasing traffic volume and

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129 construction activities on freeways lead truck dr ivers to take two-lane or multilane highways more often than ever. The main subjects of im provement on those facilities are narrow lane and shoulder widths and deteriorated pavement condition, which mostly do not provide them adequate room for error. Although the partic ipants were mostly satisfied with freeway conditions in Florida, they are still very important for truck drivers as th ey spend most of their time on them. Truck drivers do not want the left la ne restricted from truck use or a lower speed limit only applied to truck traffic. They also indicated that educating the motoring public, in addition to truck drivers, would be one of th e key factors for improving driving conditions on freeways. Given the fact that much less of truck drivers travel mileage occurs on arterials, they were thought to be least in need of improvement among the listed f acility types. However, truck driving environment on arterials ar e important exclusively to LTL or short-haul drivers. Some arterial facilities, especi ally in old towns, were considered to be in need of renovation in terms of physical roadway conditions (e.g., curb radii, placement of trees or light poles, etc.) to accommodate trucks whose sizes are larger than they used to be many years ago. The participants indicated that the best m easures for better truck operations would be constructing more alternative routes, preferably truck-only routes. They believe that designated truck routes would not only help them cope with the ever increasing number of cars in Florida, but also eliminate possible safety hazards caused by inconsistent tra ffic flow with all the vehicles having different operating characteristics (e.g., acceleration or deceleration capability, braking distance, etc.). 4.1.4 Truck Delivery Schedule Reliability Unexpected traffic congestion incurred by accide nts, construction, etc. is the major cause of a late delivery. The impact of road cons truction activities on on-time delivery performance is greater for the long-haul drivers than for the shorthaul drivers in that they usually do not travel

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130 on regular routes, so they often have no idea where the construction zones are. Long-haul drivers are more likely to jeopardize themselves by speeding up to meet the arranged time for a delivery. If they are late for a delivery, there is a possibility that they are not paid for the long trip and also fail to pick up other loads for b ack-hauling on their way back. Independent truck drivers also are sensitive to on-time performan ce. They often try to set up more delivery appointments to earn more money, and then they have to speed up on roadways to meet their tightened delivery schedules. They may also need some time to change the loading sequence of the goods by themselves according to the newly set up delivery appointments. Owner-operator truck drivers have difficulty delivering the goods as scheduled if their trucks are not ready. They cannot start their trip unt il their trucks are fixed. That is not the case for company-hired drivers whom their companies provide with a wide collecti on of trucks. Most longhaul truck drivers in Florida are rarely late for their deliveries (almost 100% early or ontime delivery). It is mainly because they are given enough time to make on-time deliveries. An average speed of 47 mi/h is typically used by truck company managers to calcu late the total delivery travel time. Managers often use an average speed less than 47 mi/h to allow more travel ti me for the drivers that have to travel through some routinely congested areas. Some impatient drivers try to save a signifi cant amount of time by driving fast. Also, new drivers will often not admit th at they are sleepy and pull over, as they want to make a good impression to their employers. Thus, they would have a higher likelihood of being involved in an accident than others. On-time delivery perf ormance is important, espe cially at the seaport facilities. Some receivers there will not take the freight even if it is only 5 minutes late. 4.2 Perceptions of Truck Company Managers The following section describes the results from a focus group session held with three truck company managers. They were all from the major Truck Load (TL) carriers operating

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131 more than 400 trucks. One manager was from a private company primarily carrying groceries. The other two were working for for-hire companie s delivering various types of goods. One of these two was involved in a company dealing w ith hazardous materials, mostly using multilane or two-lane highways. The information on th e socio-economic and working characteristics of the participants is summarized in Appendix E. It was noted by the participants that some portion of their perceptions was formed by the communica tions with the truck drivers working for their trucking business. Again, the text presented in this section only contains summaries and paraphrasing of the comments made by the focus group participants. It is not necessarily the opinions of the researchers. 4.2.1 Truck Route and Departure Time Selection Typically, dispatch managers or driver mana gers at the trucking companies decide on a truck travel route and de parture time. They usually use routing software to choose a shortest or quickest route. However, they are also open to the suggestions (e.g., traffic or clearance conditions on a route) their drivers make. When the problems of a route are thought to negatively affect their trucking business (in terms of overall operating cost, on-time performance, or drivers trip satisfaction) they occasionally change the route or manipulate the routing software to reflect those problems. Sometimes, truc k drivers try to take alternative routes instead of the routes designated by thei r managers. Managers normally allow them to do it as long as they are legal routes, but they get paid by the di spatched routes. Commer cial for-hire carriers discuss with customers to figure out what time is the best for delivery, while private carriers operate various delivery windows for their own goods. Once a freight arrival time is set up, truck departure time is normally calculated with a 45 mi/h average truc k speed (47 mi/h is most often used) considering DOT hours of service, time to rest, and all the possible situations truck drivers may encounter.

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132 Truck travel route and departure time are sele cted primarily based on shortest travel time or distance, using routing software. Although th e software saves much time for managers, it does not always make the best decisions. Thus, ma nagers also take into account perceived safety risks, time of day congestion, cons truction activities, pavement c onditions, operating cost, etc. of potential routes. Driving on two-lane highways may be cons idered dangerous due to the intermittent unexpected presence of pedestrian s and no amount of room for error (e.g., no shoulder, narrow lane width). School zones, in particular, are avoided by hazardous material carriers and it is also often noted in the delivery contracts with thei r customers. Some routes are avoided at certain times of a day (e.g., AM peak hours) due to routinely congested traffic conditions. Construction activitie s sometimes cause unexpected delays, so are considered in the decision. Pavement conditions are important to the truck drivers hauling fragile goods such as glass bottles. Managers sometimes also try to avoid roadways requiring high tolls, unless the travel time savings is relatively very large. Although some truck drivers would prefer to travel on freeways for time savings, there are some other drivers, who would rather drive on mu ltilane highways because of the congestion and safety hazards on freeways. The major cause of congestion is frequent construction activities currently in place on many freeways in Florida. Most trucks in major carriers are governed at certain speeds lower than the speed limits on freew ays for travel safety and fuel economy. Some truck drivers prefer to drive on multilane highways to avoid the speed differential between trucks and passenger cars caused by the governed truck speeds. The moto ring public generally does not have much respect for trucks and are also not knowledgeable about how to drive around trucks safely. Many truck drivers hate driving on some two-lane highways with a lot of tourists and people passing. On the other hand, they are more comfortable on some other two-lane highways

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133 with double solid yellow lines for good stretches, even though th ere may be many traffic lights along the highway. 4.2.2 Factors Affecting Truck Trip Quality Managers generally believe that travel time and safety are the two most important issues for determining truck trip quality perceived by tr uck drivers. On-time delivery performance is considered to be significantly affected by tra ffic volume, construction activities, and traffic signal controls on various transportation facilities Driving safety is thought to be mostly affected by the motoring publics negative attitudes about trucks and truck drivers and their lack of knowledge about truck drivi ng characteristics. Physical roadway conditions are also considered to be important for accommodating truc ks with their large si zes and low acceleration and deceleration capabilities. 4.2.2.1 Factors affecting truck trip quality on freeways Truck company managers prefer to route their drivers to freeways for continuous and fast traffic flow. However, this advantage has been di minished with the significant increase in traffic volume and construction activities on freeways in Florida. Truck drivers consistently complain about the motoring publics unfavor able attitudes about trucks a nd the speed differential between trucks and other vehicles on freeways. Those i ssues negatively affect the perceptions by truck drivers of the quality of a truck trip with respec t to driving safety and comfort. Some drivers often try to travel on multilane highways instead of freeways for these reasons. Ideally, truckonly routes would be provided to separate truck traffic from others and reduce the congestion level. Speed differential between trucks and autos: Many truck drivers are restricted to travel at a maximum speed ~5 miles lower than the posted speed limit of a freeway by the engine speed governors. The speed differential betwee n speed-governed trucks and other vehicles

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134 deteriorates the overall traffic flow on freeways. However, it is required to support safe driving behavior by the truck drivers. Ability of the driver to cope with a dangerous moment drops significantly as the truck speed in creases above a certain level. For many truck companies, this level is 65 mi/h. According to one manager, his drivers believed that the safety level would be enhanced if they could travel as fast as the others in a traffic stream, but it was disproved statistically with the safety reco rds of their company. On the ot her hand, the slow trucks are the main obstacle for the motoring public. They keep the other drivers from consistently maintaining a desired travel speed. In addition, truck drivers often travel next to each other to talk while driving, sometimes blocking more than three lanes simultaneously. Motoring publics attitudes: The motoring public is a significant deterrent to the safe driving performance of truck drivers in a traffic stream. They generally have little respect for trucks and truck drivers, and also are not knowledgeable about how to drive around large trucks safely. The motoring public does not usually ma intain a safe following distance and they often cut off in front of trucks or pass by them with a high speed while their movements are unexpected or sometimes even unnoticeable by the truck drivers. Many managers would route their truck drivers to multilane highways rather than freeways if it took a similar amount of time to reach the destination. Level of congestion: The main benefit of using freeways is a lower travel time. However, the traffic volume on Floridas freeways has incr eased significantly in the past several years, while the roadway capacities have not changed much. This increases the travel time of the users and aggravates the safety level on freeways. Ther e are not many alternative routes in Florida to avoid the traffic congestion. Thus, truck compan y managers always try to schedule a delivery during non-peak hours. Most truck drivers and managers prefer a night-time delivery. The

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135 trucking community often utilizes multilane highways in case of congestion, accidents, or construction activities on freeways. However, two-lane highways are hardly considered as alternatives due to their longer tr avel time, potential safety hazard s, inadequate shoulders, etc. Frequency and duration of construction activities: Construction activities on Florida interstates have significantly increased over the pa st few years. They have become so frequent and long in duration as to have a significant ne gative impact on trucking business with respect to on-time delivery performance and truck operating cost. Poor temporary pavement surface during construction periods is another concern for keeping the freight in good condition. Truck lane restrictions: It was thought by all three managers that left-lane truck restrictions (i.e., trucks are not allowed to travel in the left-mos t lane) are not a big concern to truck drivers as long as more than two travel lanes are provided to them in each direction. This was based on the reasoning that there is almost no possibility of truc k drivers passing other vehicles through the left-most lane because the maxi mum speed of most trucks is restricted under the posted speed limit by the engine speed governors of their truck companies. However, they felt that truck drivers need to be allowed in th e left-most lane on a roadway with only two lanes in each direction. Traveler Information Systems (TIS): Truck drivers and truck company mangers are always welcomed to use the TIS. However, it is not really beneficial to the trucking community due to the lack of alternative rout es in Florida. Its value will be much higher if more alternative routes are provided. It is best to have truck-on ly routes with at least two travel lanes in each direction. Weigh stations: The passage of truck drivers thr ough weigh stations is not much of a concern for the Truck Load (TL) trucking communit y. Nearly all weigh stations in Florida allow

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136 most truck drivers to pass by the stations without being weighed at all, once the safety records of their truck companies verify their good performa nce regularly. Only one out of seven passing trucks gets stopped for a weight in spection. It is very rare that total weight (the sum of weights of vehicle, equipment, and freight) is over th e maximum limit allowed by law (80,000 pounds). The only situation that truck compan ies are eligible to carry above the limit is when they deliver necessities (e.g., frozen foods) to areas of imp act during a state of emergency declared by the governor. Truck company managers sometimes encounter some customers at ports with international containers weighing more than 80,000 pounds coming from overseas. They need a permit before delivering the freigh t from the port to the destination. Thus, they try to have the permit obtained by who is responsible for it. The weigh stations at the st ate borders investigate the axle distribution as well as the total weight For this reason, the tr uck drivers frequently crossing the state lines may have some different concerns about the weigh station facilities. One manager complained about the location of a weigh station in Florida. The weigh station is located in the median area on highway 60. The speed limit of the roadway is 65 mi/h and the acceleration lane is only 100 yards long. Truck driver s have difficulty accelerating their trucks to safely merge into the left-most lane (the fastest travel lane). There were some rear-end accidents at this site. 4.2.2.2 Factors affecting truck trip quality on urban arterials Managers mainly talked about physical roadwa ys and traffic conditions not suitable for trucks turning movements. The influencing factors are curb radi us, stop line position, existence of protected turning phases, motoring publics knowledge and attitudes about trucks turning maneuvers. They mentioned that length of ye llow signal timing and tra ffic signal coordination along an arterial would also aff ect truck trip quality perceive d by truck drivers. Level of congestion on Florida arterials has gone up to a great degree by a consiste ntly increasing number

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137 of cars and traffic signals. The increased c ongestion level has a direct negative impact on efficiency in truck operations. Thus, managers always take into account time-of-day congestion (at AM or PM peak) when selecting truck routes and departure times for deliveries. Ease of turning maneuvers: Truck drivers sometimes have difficulty making turning movements. A considerable number of inte rsections do not provide adequate space and appropriate traffic signal control for the turning movements of trucks. The turning path of a truck is often partially blocked by the curbs with sm all radii at intersection corners. The vehicles waiting for a green signal at an intersection some times obstruct the turning movement of a truck. Many drivers stop beyond the stop line in a turn lane on an adjacent approach, which reduces the available turning area. Thus, shaving off a sectio n of a corner to make an angled corner or placing the stop lines further back would be bene ficial. When truck drivers get stuck in the middle of turning at an intersec tion, managers advise them to not back up and re-maneuver, as there are great risks of them conflicting with other drivers, bicyclists, or pedestrians. They want the driver to wait until the potentially obstructi ng traffic clear their wa y. Additionally, light poles, and electrical wires at th e edge of the curbs were not relocated for truck turning movements in some renovations for old downtown ar eas. This should be always considered for the development of a new town as well as the future renovation of an old town. Most commercial carriers have a company policy against U-turning movement because it has been one of the major causes of accidents. Sa fety managers regularly remind their drivers of the danger of making a U-turn and demonstrate to them how to reach destinations without a Uturn when they miss a leftor ri ght-turn. Some carriers use a r outing database to eliminate the need of their drivers for U-turn maneuvers by guiding them to other routes on which no U-turn maneuver is necessary. Many car riers prohibit a U-turn maneuve r exclusively in residential

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138 areas because of the possibility of trucks collidin g with pedestrians, elect rical lines, or something else. The managers felt that trucks need much mo re time and space for turning maneuvers than other vehicle types. It is preferable to turn at controlled intersections with enough curb radii and shoulder width. Truck drivers pref er the existence of exclusive turning signals (e.g., left turn arrow signals) with no permitted vehicle moveme nts (e.g., no right turn on red signs) during turning maneuvers due to the difficulty in finding an adequate traffic gap and space. McCord (2006) reported that UPS now has an of ficial policy that instruct their drivers to avoid making left turns as often as possibl e. Steve Goodrich, U PS Community Relations Manager, indicated that UPS cannot eliminate left turns entirel y, but the idea is to reduce the number as much as possible. He stated that its benefits are threefold. First, it saves travel time by not having to wait for a large traffic gap and space. Second, it also saves fuels as truck drivers idle waiting the left turn opportunity. Third, left turns are not as safe to make as right turns. Level of congestion: Congestion levels on Florida arterials have been increased significantly over the past a few years. Additional traffic lights are added to the existing roadway infrastructure to control the increased number of cars, motorcyclists, bicyclists, and pedestrians on many arterials. Th is makes it harder than ever fo r truck drivers to travel along arterials or through intersections. The increased levels of cong estion just lead to poor on-time performance, upward truck operating cost, and unhappy truck drivers and customers. During AM or PM peak hours, driving through an urban arte rial is often an ordeal. Thus, truck drivers are directed to travel during nonpeak hours in every possible case.

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139 Number of stops versus overall delays through signalized intersections: It is difficult to discern which one of the two aspects is more im portant to the quality of a truck trip. However, the importance of number of stops is certainly gr eater for truck drivers than for the passenger car drivers, in that it takes much more time and effo rt for truck drivers to decelerate and accelerate their vehicles. They also need to pay special at tention to keep all the equipment and goods safe, so it is a big concern especially for fra gile or hazardous material carriers. Motoring publics attitudes: Automobile drivers need more etiquette or knowledge about the characteristics of truck turning movements. They often pull thei r cars beyond the stop lines, or drive around a truck making a turning movement at intersections, ignor ing its wide turning radius. When a truck driver gets stuck at an in tersection in the middle of a turning maneuver, the cooperative attitude (i.e., yieldi ng behavior) of other drivers is sometimes crucial for the truck driver to get out of the predicament safely. Length of yellow interval signal timing: The lengths of yellow signal timings at signalized intersections are somewh at short for trucks to clear th e intersections safely before conflicting traffic signals turn green. This is because the long lengt h, heavy weight, and poor acceleration and deceleration capabili ties of trucks. At a signalized intersection, drivers are often placed in a dilemma zone where they can neit her stop before the stop line nor clear the intersection. In this situation, they are forced to choos e between abruptly stopping and running the red light. Length of the dilemma zone is usually much longer for large trucks than for passenger cars. There are even some signalized intersections where the dilemma zones only exist for trucks, not for passenger cars. The d ilemma zones for trucks may be eliminated if traffic signal clearance intervals (yellow and re d intervals) are timed properly. Expansion of yellow interval signal timing ma y contribute to this issue.

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140 Traffic signal coordination: Truck drivers are well aware th at even when there is some congestion, they can still go through an arterial wi thout having to stop too mu ch if traffic signals are properly coordinated. Again, it takes much more time and effort for truck drivers to stop and re-maneuver than it does for passenger car drivers. It is important for truck drivers to stop as little as possible along their delivery routes. 4.2.2.3 Factors affecting truck trip quality on two-lane highways As noted at the truck driver focus gro up meetings, shoulder width and condition are exclusively important for truck dr ivers to deal with potentially unexpected situations such as breakdowns, flat tires, etc. Another important concern is routinely encountered pedestrians along two-lane highways, causing safety hazards. Managers have very negative opinions about passing maneuvers of trucks (safety concerns), considering their acceler ation and deceleration capabilities inferior to other ve hicles and much longer length. Shoulder width and condition: A wide and firm shoulder is essential for truck drivers in a case of emergency. Long continuous shoulde rs placed at least every 35 miles may be required for the drivers to deal with emergenc y situations without disturbing the two-way, twolane traffic flow and get back on the road later safely. Moreover, widths of travel lanes on many two-lane highways are not enough for truck driver s to maneuver comfortably. Their travel path is not in the center of travel lanes, but often skew ed to the right. Truck drivers tend to travel on some parts of shoulder. Thus, use of rumble st rips on the boarder of travel lane and shoulder may be beneficial for bicyclists or pe destrians, but can disturb truck driving. Passing maneuvers: Managers generally discourage their drivers from passing other vehicles in any case due to the significant risks accompanied by it. However, many truck drivers still try to do it. One manager commented that his truck drivers would pass a vehicle running at 40 mi/h if they were traveling at 55 mi/h or more. The safety departments at most truck

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141 companies use such educational programs as Value-Driven or Ethics and Techniques by Smith Systems, to make them realize the importa nce of safe driving. The potential hazards of passing and U-turn maneuvers are we ll demonstrated by those programs. Frequency of pedestrians: Managers prefer to not rout e their drivers to pedestriancrowded areas for safety reasons. It usually increases travel tim e and asks the drivers for an additional effort to pay attention to those pedestrians. A truck-pede strian collision is apt to lead to severe pedestrian injury or fatality, soaring recovery and insurance fee, and potentially loss of sales. Traveling on two-lane highways around ma ny tourist spots in Florida is often avoided. 4.2.2.4 Factors affecting truck tr ip quality on hub facilities Post 9/11, port facilities statew ide reinforced their security levels, increasing time and cost for truck operations. Truck drivers are requi red to pass background and security checks for every visit. The truck companies are charged for their use of the port faci lities. Additional cost is imposed to the companies on updating the bac kground information of thei r drivers. The port facilities in Florida have grow n consistently, but access roads fr om FIHS (Florida Intrastate Highway System) to the facilities have not been accordingly upgraded with respect to roadway capacity and safety level. 4.2.3 Truck Delivery Schedule Reliability A primary determinant of on-time delivery performance is the existence of unexpected congestion along the route. In th is respect, accidents, incidents, construction activities, or bad weathers are the main causes of late deliveries. Construction activit ies and bad weathers on delivery routes may be predictable in a short te rm to some degree. However, it is always difficult for long-haul drivers or independent drivers to obtain and update reliable information about construction and bad weather conditions al ong their long routes. Even though they are properly informed of the conditions, they are not ra re and there is often no alternative route as

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142 efficient (primarily time-wise) as their originally desi gnated route. In many cases, they have no choice but to go through the conditions. Travel time through some routinely congested area s is hard to predict. That is, the more congested a delivery route is, the more difficult it is to estimate accurate freight arrival time. Variability of on-time delivery performance (the per cent of the expected travel time by which the actual arrival time is different from the scheduled arrival time) largely depends on the expected level of congestion along the trav eling areas. For example, in th e Sarasota region, variability of on-time performance is within 2 per cent, while it is about 30 percen t in Atlanta. Other than the congestion issues, delivery is sometimes late beca use of problems with shi ppers facilities. This occurs when they are not ready for the goods to be carried. Managers consider roadway capacity increases for trucks as the most efficient way to improve on-time delivery service. This capacity increases may be accomplished by constructing a lternative routes or designating truck only routes. The managers at the meeting agreed that late delivery rarely occu rs, so it is not a major concern for now. However, the importance of on-time performance is on the rise with an increasing demand for just-in-time deliveries and low inventory c ontrols. Managers generally prefer night-time deliveries because truck driv ers can run more efficiently without much congestion. However, the night-time truck deliv eries are uncommon for now. Some shippers only operate Monday through Friday, 6 AM to 6 PM It is also usually difficult for truck companies to save enough money for receivers to employ night crews by delivering just a few truck loads. One manager from a for-hire compa ny stated that truck companies may be able to offer discounts to the receivers for the night cr ews by making a profit by operating each of their trucks day and night by different drivers (day shift and night shif t). The other manager from a private company actually considered night-time delivery as the only way to remain competitive

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143 for its trucking business. Earl y deliveries also cause problem s for both for-hire and private trucking companies. Customers often do not accep t early deliveries and for-hire truck companies still pay for the truck drivers wa iting time. There is a lot more flexibility for private companies than for-hire companies in terms of early deliver ies because they can reallocate labor to pick up their own goods delivered earlier than scheduled. However, it is sometimes difficult to do that especially when unloading crews are busy perfor ming other tasks at the time of early delivery. On-time delivery performance is critical in the trucking busin ess because it is the primary determinant of the delivery serv ice performance level of a tr uck company. Some customers evaluate the trucking service based totally on it. Some other customers rate it primarily on ontime delivery and include billing errors, load co ndition, claims, etc. to evaluate the overall service performance. Impact of late de liveries on trucking business should not be underestimated since they aggravate service le vel of a truck company, which may lead to penalties and loss of sales, not to mention unhappy customers. 4.3 Perceptional Difference between Truck Drivers and Truck Company Managers Truck drivers and truck company managers both believe that quali ty of a truck trip largely depends on three factors such as tr avel safety, travel time, and driv ing comfort. The travel safety aspect of a truck trip is very important for th e two groups, given the fact that truck drivers are mostly graded by their accident history and freq uency of accidents has a great effect on trucking business operated by truck company managers. Trav el time aspect of a truck trip is more important for truck company managers than for tr uck drivers. Truck company managers always have to focus on the on-time delivery performance of their drivers, which is a primary measure for customers to evaluate the overall performan ce level of their truck company. On the other hand, most truck drivers are given enough time to make a delivery on time, and thus it is rare that they are late for a delivery. Even when a late delivery occurs, truck company managers are often

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144 responsible for it since they typically select the travel route and departur e time for deliveries. Truck drivers have more concerns on driving comfort aspect of a truck trip than truck company managers. It is because they are the ones who sp end most of their time dr iving their truck on the road as a job, and thus they are very sensitive to this aspect. Out of th e factors perceived to be important to driving comfort by truck drivers, truck company managers are concerned mainly with the factors affecting their trucking busine ss with respect to overa ll operating cost and ontime delivery performance. Most of the factors perc eived to be important to quality of a truck trip by the two groups were overlapped, but the perceptions of truck drivers on the relativ e importance of those factors were different from those of tr uck company managers. Truck lane restriction, in particular, was not perceived to be a problem by the manager gr oup as long as there are at least two or more lanes in each direction allowed for truck traffic. However, it was perceived to be very important to quality of a truck trip by the truck drivers. It was considered to have a significantly negative effect on both safety and maneuverab ility aspects of their truck trip.

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145 CHAPTER 5 SURVEY DATA ANALYSIS RESULTS A total of 459 truck drivers and 38 truck comp any managers responded to the survey effort of this study. The survey responses were anal yzed statistically to in vestigate their general perceptions on the importance of the traffi c, roadway, and control factors on various transportation facilities. This chapter provides th e findings from the survey data analysis. The relative importance, satisfaction, and improvement priority of each factor on truck trip quality was examined from the truck driver survey re sponses, while the relative importance of each factor on operating cost, on-time delivery perfor mance, truck drivers trip satisfaction, and overall trucking business was examined from the truck company manager survey responses. The perceptions of the both groups on the improvement priority of various roadway types and the preference on truck driving times of day were also explored. 5.1 Backgrounds of the Participants This section describes the ge neral background characteristics of the survey respondents. The background characteristics of the driver respondents from FTDC event and from postagepaid mail-back surveys are presented separately as well as in a combined form, while those of the managers are presented only in a comb ined form, given the small sample size. 5.1.1 Truck Driver Participants The socio-economic and working characteristics of a total of 459 driver survey respondents are shown in Table 5-1. About 32 percent of the respondents (148/459) were from the FTDC event, while almost 68 percent of them ( 311/459) were postage-paid mail-back survey respondents. About five percent of all the respondents were wo men and most respondents (95%) were at the age of 30 years. The average truck driving job experience was more than 17 years and the number of respondent s from for-hire carriers was more than three times that from

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146 private carriers. The ratio of the respondent s from TL carriers to those from LTL carriers was 54:19. About one fourth of them were independen t truck drivers. More than 75 percent of the respondents were long-haul drivers and more than 80 percent of th e drivers operate trucks that are speed-governed. The average governe d speed of the respondents was 68.4 mi/h. More than half of the respondents from FTDC ev ent were short-haul drivers, but a majority of the postage-paid mail-back survey respondents ( 92%) were long-haul driv ers. It is because the surveys were distributed at the agricultural inspection stations on freeways which are mostly used by long-haul drivers. This explains the differences in several other background characteristics between the two respondent groups. The postage-p aid survey respondents consisted of more percent of TL drivers, owner/op erator drivers, and drivers getting paid by the mile than the respondents from the FTDC event. Their average one-way truck driving distance and average governed truck speed were also higher. More background questions were asked only to the FTDC respondents. The summary of the additional backgrounds of the respondents is shown in Table 5-2. A majority of the respondents (70%) were caucasian and about 76 percent were edu cated up to the level of high school. More than half of the respondents earn between 50,000 and 69,000 dollars annually as truck drivers. Their truck comp any fleet sizes and type of goods they carry varied. The drivers use freeways most often among various roadway types. An average of 51 percent of their truck trips is made on freeways and they spend about 52 percent of their truck driving hours on freeways. They spend the average of 1.3 nights away from their home per week. 5.1.2 Truck Company Manager Participants The backgrounds of a total of 38 manager su rvey respondents are s hown in Table 5-3. Twenty seven of them were postage-paid survey re spondents and the rest were the participants at the FTDC event. The participant background info rmation from the two recruitment sources was

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147 combined and investigated as a whole due to the small sample size. Most respondents were male with their age ranging from 30 to 59 years. A high er percentage of them were for-hire and/or TL carriers than private and/or LTL carriers. They were mostly transportation/logistics or safety managers at truck companies and their main job duties included management of truck operation safety, truck travel route selec tion and scheduling for deliveries. The fleet sizes of their truck companies varied. An average of about 45 per cent of their concerns was on the operating cost aspect of truck operation, while 30 percent of their concerns were for on-time delivery performance and 25 percent on tr uck drivers trip quality. Table 5-4 summarizes the additional backgr ounds of the 11 respondents at the FTDC event. More than half of the respondents indi cated that truck travel route and departure time decisions are made only by the managers at their companies. The income levels of the respondents varied with almost 30 percent of them earning 50,00060,000 dollars annually as truck company managers. Thirty six percent of the respondents were educated at more than a high school level. The truck type s their companies operate varied, but straight truc ks, 4-axle and 5-axle tractor semi-trailers were more often used than other tr uck types. The type of goods carried by their companies also varied. 5.2 Perceptions on the Relative Import ance of Each Factor on Freeways The relative importance and sati sfaction of each factor on the quality of a truck trip was asked in the driver survey, while the relative im portance on operating cost, on-time performance, and truck drivers trip satisfac tion was asked in the manager su rvey. The relative improvement priority of the listed factors was elicited from the importance and satisfaction scores of each factor. The relative importance of each factor on the overall trucking industry was also postulated from the manager survey data. Explor atory Factor Analysis (EFA) was performed on the relative importance of each factor to determine if there are some common factors that

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148 adequately explain the variances of the items. The relative significance of each aspect of the truck driving environment is described at the end of this section. The analyses were performed for each of the three roadway facilities (i.e., freew ays, urban arterials, and two-lane highways). 5.2.1 Relative Importance of Each Factor The average Relative Importance, Satisfact ion, and Improvement Priority Scores (RIS, RSS, and IPS) of each factor on the quality of a truck tr ip on freeways are presented in Table 5-5. As discussed in chapter 3, the Improvement Priority Score (IPS) is higher for the factors perceived to be relatively more im portant and/or le ss satisfied. The IPS was calculated for each factor for each respondent and the average IPS of each factor is also shown in Table 5-5. The rankings of each factor relative to RIS, RSS, and IPS are marked as superscripts with the five most important factors for each issue marked in bold. Based on average RIS, RSS, and IPS, other drivers behavior (i .e., Passenger Car Drivers Knowledge about Truck Driving Ch aracteristics on Freeways and Passenger Car Drivers Road Etiquette) were found to be most important and least satisfied, thus most in need of improvement. The factors relative to physic al roadway conditions (e.g., Availability of Signage, Pavement Condition, Lane Widths, et c.) were perceived to be relatively fairly important, but the respondents were relatively well satisfied with the factors on freeways in Florida. Level of Congestion ranked the fifth in relative importa nce and the fourth in relative satisfaction. Interestingly, the factors concerning truck traffi c restrictions (i.e., Lane(s) Restricted from Truck Use, Lower Speed Limit Only Applied to Truck Traffic, and Governed Truck Speed Lower than Speed Limit) were perceive d to be relatively less important. However, the respondents were definitely opposed to those re strictions, thus the fact ors were perceived to be in need of significant improvement. A vailability/Publicity/Advertising of Traveler Information Systems (TIS) was considered to be least important and least in need of

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149 improvement. It is probably partly because there are not many alternative routes the truck drivers can take to detour in Florida, or ma ny truck drivers may be regular drivers, who are already familiar with time-dependent traffic and other conditions on thei r potential routes in Florida, not admitting the importance of TIS. Table 5-6 shows the results of the truck company manager surv eys. The average relative importance scores of each factor on Overall Trucking Business (OTB), Operating Cost (OC), Ontime Performance (OP), and Truck drivers trip Satisfaction (TS) are presented. As explained in chapter 3, the relative impor tance of each factor on OTB was calculated for each respondent by weighting the values of relative importance of OC, OP, and TS by their corresponding percents of the respondents concern. Again, the five most important factors for each issue are marked in bold. Based on average OTB, OC, OP, and TS, Level of congestion was most important in all the aspects of trucking business. Frequency and Timing of Construction Activities and Availability of alternative Routes followed next. It implies that construction activities are a significant constraint to their tr ucking business (probably with re spect to increased travel time) and availability of alternative routes is important to avoid congested sections of freeway. The two factors representing other drivers behavior ranked sixth and seventh for on-time performance, but they ranked between second and fifth for all other issues. Pavement Condition was perceived to be relatively more impor tant for truck drivers trip satisfaction than for any other issues. Number of Lanes and Availability and Condition of Signage were perceived more important to on-time performance than other drivers behavior. The truck company managers may believe that many signs on freeways are misplaced or not appropriately designed or maintained; thus they often negative ly affect the on-time pe rformance of a delivery

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150 by keeping the drivers from finding their way ea sily. Availability/Publicity/Advertising of Traveler Information Systems (TIS) was perceived to be least important for most of the issues in the managers perspectives as well. The RIS and TS were compared to discover the per ceptional differences between drivers and managers on the relative importance of each fact or on freeway truck trip quality. The factors concerning travel time (or traffic capacity) were perceived to be more important by the managers, while the factors regarding other dr ivers behavior and physical roadway condition were perceived to be more important by the driver s. Both groups stated that TIS-related factors are least important. An Exploratory Factor Analysis (EFA) was applied to find common factors that account for the patterns of collinearity among the variable s. The analysis was executed with a total of 147 truck driver surveys by the principle component extraction method and varimax rotation in SPSS version 15 (SPSS Inc., 2006). The varimax rotati on was specifically used to obtain a clear separation among the factors. Bartletts test of sphericity was found to be significant ( = 867.8, df = 171, < 0.01) and the KaiserMeyerOlkin measure of sampling adequacy was 0.77, justifying application of the factor-analytic procedure. Th e five factors with latent roots (eigenvalues) greater than one were retained. A scree plot also supported the decision. Table 57 displays the latent factors and the rotated fa ctor loadings and communalities of their allied items. A factor loading is the correlation betwee n an item and a factor that has been extracted from the data and the communality of an item indicates how much of the variance in the item is accounted for by the five factors extracted. Cons idering the sample size of 147, the items with a loading at 0.5 or above on one factor and le ss than 0.35 on others were first identified (the loadings on the factor are in bold print). Th e three items (Pavement Condition, Number of

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151 Lanes, and Amount of Merge or Diverge Traffi c) with a factor loading of 0.5 and above on one factor, but equal to or more than 0.35 on the other(s) were considered as loading highly on two or more factors. The othe r item (Availability of Alternative Routes) without loadings at 0.5 or above was noted as not loading highly on a ny factor. In a conventional EFA procedure, those items are excluded from the analysis, but this survey study purpor ted to evaluate the relative importance of all the items. Thus, it was not intended to exclude any item in the analysis. The four items were assigned to one of the factors with respect to the perceptional correlations between the items and factors, or the levels of the factor loadings. The communalities of most items were 0.5 or more, denoting that at leas t half of the variance in each of the items is explaine d by the factor solution. For quality of a truck trip on freeways, 58.4% of the total vari ance of the observed variables was explained by five latent factors (17.6% by factor 1, 10.5% by factor 2, 10.4% by factor 3, 10.0% by factor 4, and 9.9% by factor 5), resulting in 41.6% unexplained or lost variance. The first five factors extracted, in order of proportion of variance explained, were labeled physical roadway component s, passenger car dr ivers behavior, t raveler information usage, truck travel restrictions , and volume/capacity ratio to reflect the meaning and context of the corresponding items. Basic summated-scale descriptive statistics were calculated for each latent factor to assess its relative importance to the freeway truck trip quality. The mean RIS of each item was recalled to compare the relative importance of the items w ithin each factor to the freeway truck trip quality. These results are presented in Table 5-8. Factor 2 (Passenger Car Drivers Behavior) included two items and was exclusively most important for freeway truck trip quality among a ll the factors. Factor 1 (Physical Roadway

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152 Components) and its allied items were second most important. This factor was correlated with the most number of items, which generally ranked high in mean RIS score. The signage and pavement were most significant items within this factor. Factor 5 (Vol ume/Capacity Ratio) was third most important. Most items associated with th is factor were also fairly correlated with the other factors. The primary items of this factor were construction and co ngestion. The two least important factors were Factors 3 (Traveler Info rmation Usage) and Factor 4 (Truck Travel Restrictions), and the items relative to those factors were also least important among all the items. The other three factors were relatively much more important than the two factors, according to the factor summated means. The EFA results indicate th at the respondents did not gi ve out one or two common factor(s) that can potentially be used as performance measure(s) by which truck trip quality on a freeway can be sufficiently evalua ted. However, they suggest th at potential freeway truck LOS performance measure(s) should be strongly correl ated with Passenger Car Drivers Behavior and also be associated with Physical Roadwa y Components and Volum e/Capacity Ratio to some degree. 5.2.2 Applicability of Single Hypothetical Perf ormance Measure to Estimate Truck Trip Quality The relative importance of each hypothetical performance measure on freeway truck trip quality was asked on the last page of both the tr uck driver and truck company manager surveys. A total of four performance measures were pres ented identically to the two distinct groups of participants, but were questioned differently. Th e drivers were asked to assess applicability of each performance measure solely to estimate thei r truck trip quality, while the managers were asked to evaluate the relative importance of each performance measure for their trucking

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153 business with respect to overall operating cost, on-time performance, and truck drivers trip satisfaction. The opinions of 389 driver respondents a nd 35 manager respondents are summarized in Table 5-9 and Table 5-10, respectively. The two most important measures for each group are marked in bold. A Consistently Good Ride Quality ranked first by the drivers and third by the managers, based on the average scores. This factor description pr imarily corresponds to pavement quality and may also be correlated with other drivers poor driving behavior to some degree. The effects of pavement condition on trip quality are not considered in the current HCM, but it is evident from this survey that this f actor is very important to truck drivers trip satisfaction. Poor pavement condi tion may result in an uncomfortable riding experience, damage to the equipment (e.g., tires), or condition of goods (e.g., fragile goods, hazardous materials). Unpleasant driving interactions betw een trucks and other vehicles ma y also negatively affect ride quality experienced by the truck drivers, by requir ing them to accelerate or decelerate their truck more frequently. Given the heavy weight and large size of trucks, tr uck drivers are more sensitive to ride quality than th e drivers of other type s of vehicles. This factor, however, was not perceived to be that important to the trucking busine ss by the managers. Ease of Maintaining a Consistent Speed, wh ether Higher or Lower than Posted Speed Limit ranked second by the driv ers and first by the managers. This factor description corresponds to what the researcher s considered as speed or acceler ation variance. That is, the longer a truck driver can travel at a constant speed wit hout needing to accelera te or decelerate the truck, the more satisfied the driver will be. It requires more ti me and effort to accelerate or decelerate trucks than any other vehicle. The frequent or abrupt acce leration or deceleration activities may also increase th e likelihood of an accident. This acceleration variance factor was

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154 perceived very important to bot h the drivers and managers. Th e specific elements contributing to the factor may include other drivers drivi ng behavior, congestion le vel, and frequency of construction activities, incidents, or accidents. Th is factor may be regarded as traffic flow in a larger sense. Ease of Obtaining Useful Travel Condition In formation ranked third by the drivers and fourth by the managers. This factor was percei ved relatively less important to both drivers and managers. As stated in the focus groups, Travel er Information System (TIS) may not be useful in Florida due to the lack of alternative routes The congestion level of Floridas freeways may be not so serious that the trucking community does not need to use TIS much, or many truck drivers may be regular drivers, who think that they are highly inform ed about their potential routes enough to not recognize the need of TIS. Level of conge stion, frequency of construction activities, and availability of alternative r outes may be correlated with this factor. Ease of Driving at or above the Posted Sp eed Limit ranked four th by the drivers and second by the managers. This factor description corresponds to what the researchers considered as the percent of free-flow speed factor. Free -flow speeds are typically higher than the posted speed limit, and thus truck drivers may be dissatisf ied with a situation where they have to travel at a speed less than the posted speed limit. A considerable number of trucks on the roads are engine speed-governed to travel at a speed less than th e posted speed limit. This factor may have been recognized as ability to travel at the highe st possible speed by the drivers of those trucks. An overall effect of this factor on the trucking community is best de scribed as total travel time. This factor was regarded as the least importa nt by the drivers, but more important by the managers, who operate the trucks and th eir drivers to do the trucking business.

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155 Pair-wise multiple comparisons were perfor med to investigate if there is a mean difference among the importance of each hypothetical freeway performance measure statistically from truck drivers perspectives. Games-Howell Po st Hoc test was used for this purpose because sample sizes and variances of th e importance of the performance m easures were not equal, even though Q-Q plots showed that the data are approximately normal. The Games-Howell Post Hoc test results for freeway performance measures are shown in Table 5-11. The mean difference between the importance levels of Factor A and B was not significant. However, the mean importance levels of Factor A and B was significa ntly different from (greater then) those of Factor C and D. 5.3 Perceptions on the Relative Importance of Each Factor on Urban Arterials The same analysis procedure as in the prev ious section was performed to evaluate the relative importance of each factor on quali ty of a truck trip on urban arterials. 5.3.1 Relative Importance of Each Factor The average Relative Importance, Satisfact ion, and Improvement Priority Scores (RIS, RSS, and IPS) of each factor on the quality of a truck trip on urban arterials are presented in Table 5-12. Again, the rankings of each factor relative to RIS, RSS, and IPS are marked as superscripts with the five most importa nt factors for each issue marked in bold. Other drivers behaviors (i.e., Passenger Car Drivers Road Etiquette and Passenger Car Drivers Knowledge about Truck Driving Characte ristics on Urban Arterials) were found to be most important and least satisfied, thus most in need of improvement. Curb Radii for Right Turning at Intersections was perc eived to be next most important and fairly less satisfied by the drivers, indicating significant need of improveme nt. Pavement Condition and Availability and Condition of Signage were fourth and fifth most important, but the respondents satisfaction levels over the factors were relatively high. Thus, those factors were at mo st moderately in need

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156 of improvement among all the factors. Level of Congestion and Frequency and Timing of Construction Activities ranked eighth and ninth with respect to mean RIS, respectively, but, the respondents were strongly dissatisf ied with those factors. Theref ore, the factors were perceived to be significantly in need of improvement. Traffic Signal Coordination was perceived to be sixth most important, but fourth le ast satisfied, ranking fourth in the IPS. Level of Bicycle or Pedestrian Congestion was least important and relatively very hi ghly satisfied, being found to be least in the need for improvement. Table 5-13 shows the results of the truck comp any manager surveys. The average relative importance scores of each factor on Overall Trucking Business (OTB), Operating Cost (OC), Ontime Performance (OP), and Truck drivers trip Satisfaction (TS) are presented. Again, the five most important factors for each issue are marked in bold. Frequency and Timing of Constr uction Activities was most important in all the aspects of trucking business. Pavement Condition was perceived second most important to overall trucking business, but not that important to on-t ime delivery performance. Level of Vehicle Congestion and Existence of Left Turn Signal Phase at Intersections ranked third and fourth, based on the average OTB scores. Roadway Striping Conditi on ranked fifth. Curb Radii for Right Turning at Intersections wa s perceived to be fairly importa nt to operating cost and truck drivers trip satisfaction, but al most least important to on-time performance. Passenger Car Drivers Knowledge about Truck Driving Characte ristics on Urban Arterial s ranked fifth in average OC score and sixth in average OP score, but its importance levels to OTB and TS were found to be much less. Coordinated Traffic Signa l Timings at Intersections along the Arterials was fourth most important to on-time performan ce, but not that important to operating cost or truck drivers trip satisfaction. It should be not ed that the results presen ted here should be much

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157 less focused than the other parts of this docum ent because they came from a small sample size (6). It was also not worth comparing the RIS with TS, given such a small sample size. An Exploratory Factor Analysis (EFA) was applied to find common factors that account for the patterns of collinearity among the variables. The analysis was executed with a total of 64 truck driver surveys by the principle component extraction method and varimax rotation in SPSS version 15. Bartletts test of spheri city was found to be significant ( = 554.7, df = 153, < 0.01) and the KaiserMeyerOlkin measure of sampli ng adequacy was 0.77, justifying application of the factor-analytic procedure. The four factors with latent roots (eigenvalues) greater than one were retained. A scree plot also supported the decision. Table 5-14 displa ys the latent factors and the rotated factor loadings and communalities of their allied items. Considering the sample size of 64, the items with a loading at 0.6 or ab ove on one factor and less than 0.45 on others were first identified (the loadi ngs on the factor are in bold prin t). The five items without any loading at 0.6 or above (Availability and C ondition of Signage, Frequency and Timing of Construction Activities, Roadway Striping Co ndition, Level of Vehicle Congestion, and Curb Radii for Right Turning at Intersections) were noted as not loading highly on any factor. They were assigned to one of the factors with respect to the percepti onal correlations between the items and factors, or the levels of the factor loadings. The communalities of most items were above 0.5, denoting that more than half of the vari ance in each of the items is explained by the factor solution. For quality of a truck trip on urban arterial s, 62.5% of the total variance of the observed variables was explained by four latent factors (18.7% by factor 1, 17.2% by factor 2, 13.7% by factor 3, and 12.9% by factor 4), resulti ng in 37.5% unexplained or lost variance. The first four factors extracted, in order of pr oportion of variance explained, we re labeled roadway and traffic

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158 components, intersection crossing constraints, passenger car driv ers behavior, and physical driving deterrents to reflect the meani ng and context of th e corresponding items. The basic summated-scale descriptive statistics were calculated for each latent factor to assess its relative importance to the arte rial truck trip quality. The mean RIS of each item was recalled to compare the relative importance of the items within each factor to the arterial truck trip quality. These results are presented in Table 5-15. Factor 3 (Passenger Car Drivers Behavior) included two items and was exclusively most important for arterial truck trip quality among all the factors. Factor 1 (Roadway and Traffic Components) and its allied items were second most important. This factor was correlated with the most number of items, which ranked from fourth to twelfth in mean RIS scores. The pavement and signage were most significant items within this factor. The Factor 2 (Intersection Crossing Constraints) was third most important. The primary items of this factor were curb radii and coordinated traffic signal timings. The l east important factor wa s Factors 4 (Physical Driving Deterrents) and the items associated with this factor we re also least important among all the items. The importance of this factor was much less than the other thr ee factors, according to the factor summated means. The EFA results indicate th at the respondents did not gi ve out one or two common factor(s) that can potentially be used as performance measure(s) by which truck trip quality on an arterial can be sufficien tly evaluated. However, they suggest that potential ar terial truck LOS performance measure(s) should be strongly correl ated with Passenger Car Drivers Behavior and also be associated with Roadway and Traffic Components a nd Intersection Crossing Constraints to some degree.

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159 5.3.2 Applicability of Single Hypothetical Perf ormance Measure to Estimate Truck Trip Quality The relative importance of each hypothetical pe rformance measure on arterial truck trip quality was asked on the last page of both the tr uck driver and truck company manager surveys. A total of seven performance measures were pres ented identically to the two distinct groups of participants, but were questioned differently. Th e drivers were asked to assess applicability of each performance measure solely to estimate thei r truck trip quality, while the managers were asked to evaluate the relative importance of each performance measure for their trucking business with respect to overall operating cost, on-time performance, and truck drivers trip satisfaction. The opinions of 387 driver respondents and 33 manager respondents are summarized in Table 5-16 and Table 5-17, respectiv ely. The two most important measures for each group are marked in bold. A Consistently Good Ride Quality ranked fi rst by the drivers and f ourth by the managers, based on the average scores. Again, this factor description primarily corresponds to pavement quality and may also be somewhat correlated with other drivers poor driving behavior. Similar to the freeway EFA results, this factor was percei ved to be most important by the drivers (highest mean and lowest standard deviation), but not that important by the managers. Ease of Changing Lanes ranked second by the drivers and third by the managers. This factor description corresponds to what the researchers considered as dens ity factor. As the traffic volume in a given section of a roadway in creases, drivers ability to change lanes is aggravated. Truck drivers are more sensitive to this factor than the drivers of other vehicle types due to the large size, heavy wei ght, and poor acceleration and deceler ation capabilities of trucks. Truck drivers often need much la rger traffic gap and yi elding behavior of ot her drivers to make

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160 lane changes. Congestion level and other trav elers driving behavior (road etiquette or knowledge about limitations on truck driving) prob ably have a great effect on this factor. Ease of Rightor Left-Turn Maneuvers ranked third by the driv ers and first by the managers. It requires much more time and effort to make turning maneuvers with trucks than the other vehicle types considering their physical an d operational characteristic s. This factor is primarily influenced by physical roadway conditi on of an intersection (e .g., shoulder width, curb radii), but traffic signal operati on (e.g., existence of a protected left-turn signal) and other drivers behavior also have an impact on it. Th is factor was perceived to be most important to the trucking business by the managers. The manage rs at the focus group meeting indicated that it creates a serious safety hazard on a truck get stuck in the middle of making a turn and this issue is one of the biggest considerat ions for their arterial route choice. Ease of Maintaining a Consistent Speed, wh ether Higher or Lower than Posted Speed Limit ranked fourth by the drivers and second by the managers. This factor description corresponds to what the researcher s considered as speed or acceler ation variance. This factor was relatively more important for the managers than for the drivers. The major elements contributing to this factor may include the leve l of congestion, inters ection spacing, and traffic signal conditions (e.g., signal co ordination, signal responsivene ss) along the arterial. The importance of this factor was much less for arteri al truck trip quality than freeway truck trip quality. Ease of Passing through Signalized Intersec tions along the Arterial ranked fifth by both the drivers and managers. This factor description corre sponds to what the re searchers considered as number of stops or delay factor. Base d on the previous focus group discussions, truck drivers are more sensitive to number of stops than overall delay experienced while traveling

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161 along an arterial. The specific el ements contributing to this f actor include intersection spacing and traffic signal conditions such as signal coordination, signal re sponsiveness, length of yellow interval timing at signalized intersections. This factor was perceived to be much less important than ease of turning maneuvers, wh ich implies that they are more concerned with making a safe and easy turning maneuver than reducing number of stops or delay experienced during through movements at intersections. Ease of Driving at or above the Posted Speed Limit ranked sixth by both the drivers and managers. This factor description corresponds to what the researchers considered to be a percent of free-flow speed. The level of this factor directly affects average travel time, which is currently used by the HCM to define level of service on urban arterials. This factor, however, was perceived to be much less important than most other factors by bot h the drivers and the managers. This factor primarily depends on th e level of congestion on an arterial route. Ease of U-Turn Maneuvers was perceived to be least important by both the drivers and managers. According to the focus group discus sions, given the high potential safety risks associated with the U-turn maneuvers, many truck drivers are reluctant to make a U-turn unless it absolutely is necessary and most major carriers have a company policy against it. Pair-wise multiple comparisons were performed to investigate if there is a mean difference among the importance of each hypothetical arterial pe rformance measures statistically from truck drivers perspectives. The Games-Howell Post Hoc test results for arterial performance measures are shown in Table 5-18. There was no significant mean difference among the importance levels of Factor B, C, and D. The im portance of Factor A was greatest, but the mean of the importance of Factor A was barely significan tly different (greater) from those of Factor B, C, and D at 95% confidence level. The mean di fference between the importance of Factor E and

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162 F was not significant. The mean of the importan ce level of Factor G was significantly lower than those of other Factors. Overa ll, the test results s uggest that potential arterial performance measure(s) should address various aspects of truc k driving conditions such as Factor A, B, C, and D. 5.4 Perceptions on the Relative Importance of Each Factor on Two-Lane Highways The same analysis procedure as in the prev ious section was performed to evaluate the relative importance of each factor on quali ty of a truck trip on two-lane highways. 5.4.1 Relative Importance of Each Factor The mean Relative Importance, Satisfac tion, and Improvement Priority Scores (RIS, RSS, and IPS) of each factor on the quality of a truck trip on two-lane highways are presented in Table 5-19. Again, the rankings of each factor relative to RIS, RSS, and IPS are marked as superscripts with the five most important fact ors for each issue marked in bold. Other drivers behavior (i.e., Passenger Car Drivers Knowledge about Truck Driving Characteristics on Two-Lane Highways and P assenger Car Drivers Road Etiquette) was found to be most important and least satisfied, thus most in need of improvement. Availability and Condition of Signage and Pavement Condition were perceived to be third and fourth most important, but fairly well satisfied by the truc k drivers, so their improvement priority was mediocre among all the factors (e leventh and ninth in the mean IPS rankings). Lighting Conditions at Night ranked fifth in the mean RIS and IPS. Its importance was much greater for two-lane highways than freeways in that it ranked fourteenth and fifteenth in the mean RIS and IPS for freeways. Shoulder Width and Condition ranked sixth in the mean RIS, but third in both the mean RSS and IPS. It is noted that its importance on travel safety and operation was emphasized in the focus group meetings. Frequ ency and Timing of Co nstruction Activities were considered to be fourth most in need of improvement. Level of Congestion and

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163 Frequency of Passing Lanes ranked tenth and eleven in the mean RIS respectively, but the respondents were so dissatisfied with these factors that their re lative need of improvement was fairly high (sixth and eighth in the mean IPS rankings). Similar to the freeway results, TISrelated factors were perceived to be least important and least in need of improvement. Table 5-20 shows the results of the truck comp any manager surveys. The average relative importance scores of each factor on Overall Trucking Business (OTB), Operating Cost (OC), Ontime Performance (OP), and Truck drivers trip Satisfaction (TS) are presented. Again, the five most important factors for each issue are marked in bold. Roadway Striping Condition ranked first in the mean OTB and OC scores, but was perceived to be not that important to OP and TS. Level of Vehicle Congestion and Pavement Condition were perceived to be fairly important to every trucking business issue, ranking second and third in the mean OTB scores, respectively. Passenge r Car Drivers Knowledge about Truck Driving Characteristics ranked fourth in the mean OTB, but was perceived to be second most important to OP and most important to TS. Shoulder Width and Condition ranked fifth in the mean OTB scores and third in the mean OC scores. Frequency a nd Timing of Construction Activities ranked sixth in the mean OTB scores, but was perceived to be most important to Ontime Performance (OP). Sight Distance at Horizontal Cu rvatures and Frequency of Vehicles much Slower than Your Truck were pe rceived to be much more important for TS than for the other issues. The importance of Availability and Condition of Signage was much less for twolane highways than for freeways or arterials. F requency of Faster Vehicl es Passing Your Truck was least important in the managers perspectiv es. Again, it should be noted that the results presented here should be much less focused than the other parts of this document because they

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164 came from a small sample size (4). No comparison was made between the RIS with TS, given such a small sample size. An Exploratory Factor Analysis (EFA) was applied to find common factors that account for the patterns of collinearity among the variables. The analysis was executed with a total of 64 truck driver surveys by the principle component extraction method and varimax rotation in SPSS version 15. Bartletts test of spheri city was found to be significant ( = 485.2, df = 171, < 0.01) and the KaiserMeyerOlkin measure of sampli ng adequacy was 0.59, justifying application of the factor-analytic procedure. The five factors with latent roots (eigenvalues) greater than one were retained. A scree plot also supported the decision. Table 5-21 displa ys the latent factors and the rotated factor loadings and communalities of their allied items. Considering the sample size of 64, the items with a loading at 0.6 or ab ove on one factor and less than 0.45 on others were first identified (the loadi ngs on the factor are in bold prin t). The five items without any loading at 0.6 or above (Shoulder Width and C ondition, Frequency of Faster Vehicles Passing Your Truck, Level of Vehicle Congestion, F requency and Timing of C onstruction Activities, and Lane Widths) were noted as not loading hi ghly on any factor. The one item (Availability and Condition of Signage) with a factor loading of 0.6 and above on one factor, but equal to or more than 0.45 on the other(s) was considered as loading highly on two or more factors. The six items were assigned to one of the factors with respect to the percepti onal correlations between the items and factors, or the levels of the factor loadings. The communalities of most items were above 0.5, denoting that more than half of the vari ance in each of the items is explained by the factor solution. For quality of a truck trip on two-lane hi ghways, 61.0% of the total variance of the observed variables was explained by four latent factors (15.1% by factor 1, 13.4% by factor 2,

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165 12.2% by factor 3, 11.2% by factor 4, 9.2% by factor 5), resulting in 39.0% unexplained or lost variance. The first five latent factor s extracted, in order of pr oportion of variance explained, were labeled travel safety elements, travele r information usage, travel speed constraints, physical roadway components, and passenger car drivers behavior to reflect the meaning and context of the co rresponding items. The basic summated-scale descriptive statistics were calculated for each latent factor to assess its relative importance to the two-la ne highway truck trip quality. The mean RIS of each item was recalled to compare the relative importance of the items within each factor to the twolane highway truck trip quality. Thes e results are presented in Table 5-22. Factor 5 (Passenger Car Drivers Behavio r) included two items and was exclusively important for two-lane highway truck trip qual ity among all the factors. Factor 4 (Physical Roadway Components) and its alli ed items were second most important. All the items of this factor ranked relativ ely high in the mean RIS. The signage and paveme nt were the two most significant items within this factor. Factor 3 (Travel Speed Constraints) was third most important. The primary items of this factor we re construction and conges tion level. Factor 1 (Travel Safety Elements) was sec ond least important, but the mean RIS of Lighting Conditions at Night and Shoulder Width and Condition were fairly high (fifth a nd sixth in the mean RIS). The least important factor was Factors 2 (Travele r Information Usage) and the items relative to those factors were also least important among all the items. The importance of this factor was much less than the other four factors, according to the factor summated means. The EFA results indicate th at the respondents did not gi ve out one or two common factor(s) that can be potentially used as performance measure(s) by which truck trip quality on a two-lane highway can be sufficien tly evaluated. However, they suggest that potential two-lane

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166 highway truck LOS performance measure(s) should be strongly correlated with Passenger Car Drivers Behavior and also be associated with Physical Roadway Components such as signage, pavement, lighting, and shoulder conditions It is probably true that Travel Speed Constraints begin to matter as the traffic vol ume gets close to roadway capacity due to congestion, construction, or accident. 5.4.2 Applicability of Single Hypothetical Perf ormance Measure to Estimate Truck Trip Quality The relative importance of each hypothetical performance measure on two-lane highway truck trip quality was asked on the last page of both the truck driver a nd truck company manager surveys. A total of seven performance measures were presented identically to the two distinct groups of participants, but were questioned di fferently. The drivers were asked to assess applicability of each performance measure solely to estimate thei r truck trip quality, while the managers were asked to evaluate the relative importance of each performance measure for their trucking business with respect to overall operating cost, on-time pe rformance, and truck drivers trip satisfaction. The opinions of 385 driver respondents and 34 manager respondents are summarized in Table 5-23 and Table 5-24, respectiv ely. The two most important measures for each group are marked in bold. Probability of Bing Passed or Followed by Fast er Vehicles ranked firs t by the drivers and fifth by the managers, based on the average scores. This factor description corresponds to what may be considered as Percent-Time-Being-followe d (PTBF) as the current HCM uses PercentTime-Spent-Following (PTSF) as one of the serv ice measures to determine level of service on two-lane highways. That is, the perceptions of truck drivers on truck tr ip quality become poor, as more drivers try to follow their trucks in a close proximity or pass them. The perceptional

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167 difference between the two groups on this factor was so obvious that this factor was considered to be most important by the drivers, but least important by the managers. A Consistently Good Ride Quality ranke d second by the drivers and fourth by the managers. This factor description primarily corresponds to pavement quality and may also be correlated with other drivers poor driving behavior to some degree. This is a factor that was identified, in the previous section, as most impor tant for truck drivers traveling on freeways or arterials. The importance of th is factor was perceived to be greater by the drivers than by the managers. Width of Travel Lane and Shoulder, or Shoulder Type ranked th ird by the drivers and first by the managers. This factor description corresponds to what the focus group participants considered to be amount of room for error fact or. That is, truck driv ers are uncomfortable on a two-lane highway where travel la nes and shoulders are not wide or solid enough for them to deal with an emergency situation wit hout hampering the two-way traffi c flow. This was the hottest issue regarding two-lane highway truck trip quali ty in the focus group discussions. This factor was perceived to be most important by the mana gers, but not that important by the drivers. Probability of Encountering Possible Conflicts ranked fourth by the drivers and third by the managers. This factor contributes to travel safety aspect of truck operation. Ability of truck drivers to deal with unexpected obs tacles is inferior to that of the drivers of other vehicle types due to the low acceleration and deceleration capabili ties of trucks. As mentioned in the focus group study, truck driving safety is a huge concer n for the trucking community. In addition to the damage to the drivers, equipment, and the goods, an accident brings about the considerable increase in truck operating cost (e.g., insurance cost loss of sales, or loss of the contract with the

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168 customers). The importance of this factor was pe rceived to be at most average among the factors by both the drivers and managers. Opportunity for Passing Other Cars, through Passing Zones or Passing Lanes ranked fifth by the drivers and second by the managers. This f actor is highly correlated with Percent-Time Spent-Following concept that is used to defi ne level of service on two-lane highways by the current HCM. The more frequent is the pass ing opportunity, the less time is spent to follow slower vehicles. Given the long length and lo w acceleration and deceleration capabilities of trucks, passing maneuver is much more difficult a nd dangerous for truck dr ivers than the drivers of other vehicle types. This factor was percei ved to be second most important by the managers, but least important by the drivers. The managers may have thought that truck travel time is affected by this factor to a considerable degree. Pair-wise multiple comparisons were performed to investigate if there is a mean difference among the importance of each hypothetical two-lane highway performance measures statistically from truck drivers perspectives. The Games-Ho well Post Hoc test results for two-lane highway performance measures are shown in Table 5-25. The mean difference between the importance of Factor A and B was not significant. The mean im portance level of Factor C was not significantly different from that of Factor D as well. Howeve r, the mean importance levels of Factor A and B were significantly different (greater) from those of Factor C and D. The mean importance level of Factor E was significantly lower than those of any other Factors. 5.5 Relative Importance of Each Category of Factors to Quality of a Truck Trip Four categories of factors on each roadway type were compared amongst each other in terms of their contributions to truck trip qual ity. The same set of categories (i.e., traffic conditions, roadway conditions, trav eler information systems, and other drivers behavior) were evaluated for both freeway and two-lane highway facilities, while traffic signal conditions were

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169 included in the four categories in stead of traveler information systems to be appraised for urban arterial facilities. 5.5.1 Relative Importance of Each Category of Factors for Freeways The perceptions of 40 drivers on the relative importance of the four categories are shown in Figure 5-1. Based on the mean ranking values, th e order of the categories, in order from most important to least important, was traffic condition s, other drivers behavior, roadway conditions, and traveler information systems. The difference in the importan ce levels between the first two categories was small. The importance level of roadway conditions was average in that about half of the respondents (21/40) considered the third category to be most important or second most important, while a significan t portion of the respondents ( 28/40 or 70%) perceived that traveler information systems are least important. The perceptions of 7 truck comp any managers were similar to those of the drivers. The order of the factor categories in order of their importance based on the mean ranking values, were other drivers behavior (mean rank = 1.9) traffic conditions (mean rank = 2.0), roadway conditions (mean rank = 2.1), and trav eler information (mean rank = 4.0). 5.5.2 Relative Importance of Each Cate gory of Factors for Urban Arterials The perceptions of 34 drivers on the relative importance of the four categories are shown in Figure 5-2. Based on the mean ranking values, th e order of the categories, in order from most important to least important, was traffic condition s, other drivers behavior, roadway conditions, and signal conditions. The differences in the im portance levels among the first three categories were relatively small. However, a consider able portion of the respondents (23/34 or 68%) perceived other drivers behavior to be either most or leas t important, while the frequencies of the importance ranks of roadway conditions we re almost uniformly distributed. Only 3 respondents (3/34 or 9%) perceived that si gnal conditions were most important.

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170 The perceptions of 6 truck comp any managers were similar to those of the drivers. The order of the factor categories in order of their importance based on the mean ranking values, were traffic conditions (mean rank = 1.5), other drivers behavior (mean rank = 2.7), roadway conditions (mean rank = 2.8), and signal conditions (mean rank = 3.0). 5.5.3 Relative Importance of Each Categor y of Factors for Two-lane Highways The perceptions of 37 drivers on the relative importance of the four categories are shown in Figure 5-3. Based on the mean ranking values, th e order of the categories, in order from most important to least important, was roadway conditi ons, traffic conditions, ot her drivers behavior, and traveler information system s. The difference in the impor tance levels among the first two categories was small. Less than half of the respondents (18/37) considered other drivers behavior is either most or second most important while more than half (20/37 or 54%) perceived that traveler information systems were least important. Five truck company managers perceived traffic conditions to be most important (mean rank = 1.6). The order of the rest of the factor categories, in order of their importance based on the mean ranking values, were other drivers behavior (mean rank = 2.2), roadway conditions (mean rank = 2.6), and traveler information (mean rank = 3.6). 5.6 Comparisons of the Importance of Each Factor Category on Various Roadway Facilities The importance levels of each factor categor y were compared across the three roadway facility types (i.e., freeways, urban arterials, a nd two-lane highways) to investigate the relative importance of each factor categor y between facilities. This co mparison is shown in Figure 5-4. The importance of roadway conditions was gr eater for two-lane highways than for the other two facilities. The importa nce levels of roadway conditions for freeways and arterials were somewhat similar, but only 10 percent of the re spondents (4/40) considered this factor for

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171 freeways to be least important wh ile almost 25 percent of the res pondents (8/34) considered this factor for arterials to be least important. The relative importance of traffic conditions fo r the three roadway facilities is shown in Figure 5-5. The importance of tr affic conditions was much more significant for freeways than for the other two facility types. Almost 70 pe rcent of the respondents (2 7/40) considered this factor to be at least s econd most important and only 10 percent (4 /40) considered this factor to be least important. The importance of this factor fo r two-lane highways was little greater than that for arterials in that 65 percent of the respondents (24/37) consider ed this factor for two-lane highways to be at least second most important while 53 percen t (18/34) did for arterials. The relative importance of other drivers behavi or for the three roadway facilities is shown in Figure 5-6. The importance of this factor was less for two-lane highways than for the other two facilities. The importance of this factor for freeways was somewhat bigger than that for arterials in that only 13 percent of the respondents (5/40) considered this factor for freeways to be least important while more than 30 percent (11/ 34) considered this factor for arterials to be least important. 5.7 Perceptions on the Improvement Priority of Various Roadway Types Twenty-five truck drivers answered the quest ion about the improvement priority of the four types of roadway facilities (i.e., freeways, urban arterials, rural multilane highways, and rural two-lane highways). Figure 5-7 shows the distributions of the responses on the relative improvement priority. Based on the mean ranking values, the order of the roadway types, in order from highest to lowest, identified as most in need of improvement was urban arterials, rural multilane highways, rural two-lane highways, and freeways. The differences in the need for improvement rankings among the first three facility types were small. Rural multilane highways, overall, were identified as only mo derately in need of improvement. Eighty-eight percent of the

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172 respondents (22/25) indicated that th is facility type is either s econd or third most in need of improvement. The rankings on the relative improve ment need of freeways varied the most. Out of the total of 25 drivers, seven drivers (28%) perceived the freeway facility to be most in need of improvement and twelve drivers (48%) perceived it to be least in need of improvement. The perceptions of 5 truck company managers also showed similar results. Based on the mean ranking values, it turned out that urban arterials were perceive d to be most in need of improvement (mean rank = 2.0). Rural multilane and two-lane highways followed next (mean rank = 2.6). Again, freeways were perceived to be least in need of improvement (mean rank = 2.8). 5.8 Relationships between Truck Drivers Ba ckgrounds and Their Perceptions on the Applicability of Each Hypoth etical LOS Performance Measure Potential correlations between truck driver s working and socio-economic characteristics and their perceptions on applic ability of each hypothetical perfo rmance measure for truck LOS estimation (hereafter referred to as just im portance of each performance measure) were investigated with non-parametric statistical tests. Each backgr ound characteristics of the drivers (a potential explanatory variable) was individually tested with th eir perceptions, in order to find out whether their perceptions sign ificantly varied by the levels of the background characteristics. The Kruskal-Wallis test and Mann-Whitney test ( non-parametric version of Analysis of Variance (ANOVA) and t-test) were applied to the truck driv er survey data. The truck drivers background characteristics used in these analyses are presented in Table 5-26 and 5-27. The variables with only two levels were evaluated wi th Mann-Whitney test to investigate if the two samples came from the same population. The variable s with three levels we re evaluated with the Kruskal-Wallis test to investigate if they came from the same population. For the variables that

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173 were significant in the Kruskal-Wallis test, they were evaluated again with the Mann-Whitney test for the pair-wise comparisons to find out which items were different one another. 5.8.1 Truck Drivers Backgrounds that Explai n Their Perceptions on the Importance of Each Hypothetical LOS Performance Measure on Freeways The correlations between each background ch aracteristics of the drivers and their perceptions on the importance of each freeway performance measure were investigated. The results of the Kruskal-Wallis and Mann-Whitney tests are shown in Table 5-28 and 5-29. The perceptional difference by truck kinds and the type of goods carried was not statistically significant, so it was not tabulated. The perceptions on the importance of A C onsistently Good Ride Quality (Factor A) differed by recruitment sources, earning methods, and current truck driv ing time of day. The postage-paid survey respondents considered this factor to be more important than the FTDC respondents. It is noted that over 90 percent of the postage-p aid respondents were long-haul drivers, while more than half of the FTDC res pondents were short-haul dr ivers. Truck drivers getting paid by miles driven perceived this f actor more important, wh ile those paid by hours driven perceived it less important. It is typi cal that long-haul driver s normally get paid by the mile, but short-haul drivers usually get paid by the hour to get compensated by the delay they often experience in traveling urban environments Truck drivers currently driving between noon and 3 PM perceived this factor to be less important than others di d. The results show that truck drivers having some general char acteristics of long-haul driv ers (frequent freeway users) perceived this factor to be more important than others did. They travel at a much higher speed than short-haul drivers, so are likely to be more sensitive to ride quality. The perceptions on the importance of Ease of Maintaining a Consistent Speed, Whether Higher or Lower than Posted Speed Limit (Fact or B) varied by race and level of education.

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174 African American truck drivers, in particular, perceived this f actor to be less important, but drivers educated at least up to co llege level had more preference on this factor than others did. The importance of this factor may be more no ticeable by drivers educated more than others. The perceptions on the importance of Ease of Obtaining Useful Travel Conditions Information (Factor C) differed by recruitmen t sources, earning methods, maximum governed truck speed, and level of educati on. The postage-paid survey res pondents considered this factor to be more important than the FTDC respondents. Truck driver s getting paid by miles driven perceived this factor more important than ot hers did. Truck drivers whose truck is speedgoverned at more than 65 mi/h considered this factor to be more impor tant than others did. Truck drivers educated at least up to college level perceived this factor to be more important. Again, the truck drivers having some general ch aracteristics of long-haul drivers (frequent freeway users) perceived this factor to be mo re important than others did. Truck drivers educated more than other drivers may be mo re concerned with various TIS technologies. The perceptions on the importance of Ease of Driving at or above the Posted Speed Limit (Factor D) differed by recruitment sources earning types, company types, primary load types, hauling distance, maximum governed tr uck speed, and truck driving time of day. The postage-paid survey respondents considered this factor to be more important than the FTDC respondents. Truck drivers getting paid by hours perc eived this factor less important than others did. Truck drivers from for-hire or TL carriers perceived this fact or to be more important than the others did. Long-haul drivers were more sens itive to this factor than short-haul drivers. Truck drivers whose truck is speed governed at more than 65 mi/h considered this factor to be more important than others did. Truck driver s currently driving in the morning time (9AM noon) were less sensitive to this factor. It is ju st natural to consider that long-haul drivers

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175 (frequent freeway users) are more concerned about this factor than shorthaul drivers, because it will be much more frustrating to not be able to drive at or above the posted speed limit on a freeway than on an arterial. 5.8.2 Truck Drivers Backgrounds that Explai n Their Perceptions on the Importance of Each Hypothetical LOS Performance Measure on Urban Arterials The correlations between each background ch aracteristics of the drivers and their perceptions on the importance of each arterial LOS performance measure were investigated. The results of the Kruskal-Wallis and Mann-Whitney tests are shown from Table 5-30 to 5-34. The perceptional difference by truck ki nds was not statistically signifi cant, so it was not tabulated. The perceptions on the importance of A Cons istently Good Ride Quality (Factor A) varied by maximum governed truck speed and truc k driving hours per day. Truck drivers whose truck is speed-governed at more than 65 mi/h per ceived this factor more important than others did. Truck drivers traveling at a higher speed are likely to be more concerned about the ride quality. Truck drivers driving more than 8 hours pe r day were less sensitive to this factor. Truck drivers working more hours per day may ge t relatively numb about the ride quality. The perceptions on the importance of Ease of Changing Lanes (Factor B) differed by earning methods, truck driving hours per day, and types of goods carried. Truck drivers who get paid by the load considered this factor to be le ss important than the other drivers. Truck drivers driving more than 8 hours per day were less sensitive to this factor. Long-haul drivers may drive more hours per day than short-haul drivers, and those frequent freeway users may be less concerned with lane changing movements than dr ivers often traveling in urban environments. Truck drivers carrying food, auto pa rts, textiles, metals, paper a nd allied products, chemicals and allied products, equipment, furniture, or hazar dous materials were more concerned about this factor than other drivers. The truck driver s delivering those damage-sensitive goods, probably

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176 by operating relatively large trucks may be more sensitive to a potential conflict with other vehicles, feeling more difficulty in changing lanes. The perceptions on the importance of Ease of Rightor Left-Turn Maneuvers (Factor C) differed by earning methods, company business types, truck driving hours per day, and types of goods carried. Truck drivers who get paid by the load perceived this factor to be less important than other drivers. Truck drivers from companies operating both for-hire and private businesses had less concerns on this factor than ot her drivers from either private or for-hire truck companies. Truck drivers driving more than 8 ho urs per day were less sensitive to this factor. Again, long-haul drivers may drive more hours pe r day than short-haul drivers, and those frequent freeway users may be less concerned w ith those turning movements than drivers often traveling in urban environments. Truck drivers carrying paper or allied products considered this factor to be important mo re than other drivers. The perceptions on the importance level of e ither Ease of Maintaining a Consistent Speed, Whether Higher or Lower than Posted Sp eed Limit (Factor D), or Ease of Passing through Signalized Intersections along the arte rial (Factor E) did not vary by any background characteristics of the survey respondents. The perceptions on the importance level of Eas e of Driving at or above the Posted Speed Limit (Factor F) differed by recruitment sources earning methods, primary load types, hauling distance, maximum governed truck speed, percent of late delivery, and truck driving days per week. Long-haul drivers or truck drivers ha ving some general charact eristics of long-haul drivers (i.e., postage-paid survey respondents, drivers operating a truck with more than 65 mi/h of a maximum engine-governed speed, TL drivers, and truck drivers working more than 5 days per week) perceived this factor to be more important than othe rs did. Again, this factor is

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177 important for the drivers traveling longer time an d distance for deliveries. They mostly drive on freeways to save their travel time and those fr equent freeway users will get relatively easily frustrated for not being able to travel at a free-flow speed. Truck drivers who get paid by hours (mostly short-haul drivers) were less sensitive to this factor. T hose at most 5 percent of whose truck trips are late were more sensitive to this fact or. This factor is highly associated with travel time aspect of a truck trip. They may be so much concerned with travel time that they rarely are late for their deliveries. The perceptions on the importance level of Ease of U-Turn Maneuvers (Factor G) varied by recruitment sources, age, earning met hods, company business type s, hauling distance, percent of late delivery, truc k driving hours per day, truck driving time of day, and types of goods carried. Postage-paid survey respondents perc eived this factor to be less important than FTDC respondents. Middle-aged truck drivers we re more concerned about this factor than young drivers. Truck drivers who get paid by hour s were more sensitive to this factor, while those paid by the load were less sensitive to this factor. Truck drivers from private carriers perceived this factor to be more important than others did. Shorthaul drivers or those more than 5 percent of whose truck trips ar e late were more concerned about this factor. Truck drivers working more than 8 hours per day or those cu rrently driving at the time period between 6AM and 9AM considered this factor to be less impor tant than others did. Truck drivers carrying food, in particular, were more concerned about this factor. Overall, shor t-haul drivers or truck drivers having some general charac teristics of short-haul drivers (i.e., FTDC survey respondents, drivers getting paid by the hour, drivers with more than 5 percent of their trips late, and drivers carrying foods) perceived this factor to be more important than othe rs did. They are the primary truck mode users on urban arterial facilities.

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178 5.8.3 Truck Drivers Backgrounds that Explai n Their Perceptions on the Importance of Each Hypothetical LOS Performa nce Measure on Two-Lane Highways The correlations between each background ch aracteristics of the drivers and their perceptions on the importance of each two-lane highway LOS performance measure were investigated. The results of the Kruskal-Wallis and Mann-Whitney tests are shown from Table 5-35 to 5-37. The perceptions on the importance of Probabi lity of Being Passed or Followed by Faster Vehicles (Factor A) differed by recruitment sour ces, gender, level of tr uck driving experience, percent of empty truck trips, race, current truc k driving time of day, type of goods carried, and truck types. Postage-paid survey respondents considered this factor to be important more than FTDC respondents did. They mostly are long-h aul drivers, probably using two-lane highways more often than short-haul driver s. Male drivers or Hispanic dr ivers were concerned with this factor more than others did. They may be more likely to get impatient when being passed or followed by other vehicles than other truck driver s may. Truck drivers with at least 15 years of job experience were less concerned with this fact or than others were. They may have become less sensitive to this factor, having plenty of experience with this factor. Truck drivers with more than 25 percent of their truck trips empty perceive d this factor to be less important than others did. When they drive an empty truck, it is a lot easier for them to maneuver (i.e., accelerate or decelerate) due to its lighter weight and no possi ble freight damage, being less sensitive to the other vehicles following or pa ssing their truck. Truck driver s traveling at the time period between noon and 3PM perceived this factor to be more important than others did. Truck drivers carrying auto parts, stone, clay, or concrete prod ucts perceived this factor to be more important than the others did, while the drivers carrying unknown packages (FedEx packages) or drivers operating a straight (single unit) tr uck, in particular, were less con cerned about this factor. It is

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179 likely that truck drivers carrying heavy or damage -sensitive goods, or driv ing larger trucks are more concerned with this factor because their mane uverability is much more inferior to that of other truck drivers. The perceptions on the importance of A Cons istently Good Ride Quality (Factor B) differed by percent of late deli very, annual income level, numbe r of working days per week, types of goods carried, and truck types. Truck dr ivers more than 5 percent of whose truck trips are late perceived this fa ctor to be less important than others did. It is possible that drivers who are more punctual on deliveries drive at a hi gher speed than other drivers do, being more concerned with the ride quality. Truck drivers with annual income between $50,000 and $70,000 were more concerned about this factor. Tr uck driver carrying grai ns/feed perceived this factor to be more important than other did. They may be afraid of their grains/feed falling out of their truck, being more concerned with the ride qu ality than other truck dr ivers. Truck drivers working more than 5 days per week were mo re concerned about this factor, while those operating turnpike double were le ss concerned about this factor. The perceptions on the importance of Width of Travel Lane and Shoulder, or Shoulder Type (Factor C) varied by annua l income level and types of goods carried. Truck drivers with annual income between $50,000 and $70,000 were more concerned about this factor. Truck drivers carrying grains/feed or haza rdous materials were more sensit ive to this factor. They may often need more spaces (i.e., part of shoulder width) than just one narrow lane to feel safe to travel with those collision-sensi tive materials, or the importance of an adequate shoulder width and good shoulder condition (i.e., hard pavement) is greater for those driver s to park their truck safely in case of emergency (e.g., a tire-blowout ) than for other truck drivers, due to worse consequences accompanied by a potential conflict with other vehicles in the main traffic stream.

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180 The perceptions on the importance of Proba bility of Encountering Possible Conflicts (Factor D) varied by recruitment sources, level of truck driving experience, primary load types, percent of truck trips that are no t made on familiar roads, truck dr iving time of day, and types of goods carried. Postage-paid survey respondents consid ered this factor to be more important than the FTDC respondents. Most of them were longhaul drivers, who trav el two-lane highways more often than short-haul driv ers. Truck drivers with less than 5 years of truck driving experience considered this factor to be more important than ot hers. They have less experience traveling on two-lane highways, thus may be more cautious a bout potential conflicts on the roads. Truck drivers from a truck compa ny which operates both TL and LTL were more concerned about this factor than those from a company operating either TL or LTL. Truck drivers who travel on a familiar road less often were more concerned about this factor. Truck drivers will be more likely to be afraid of a pot ential conflict when they travel on an unfamiliar road. Truck drivers traveli ng during the time period between 7PM and midnight were less concerned about this factor, wh ile those carrying waste and scrap were more concerned about this factor. The perceptions on the importance of Oppor tunities for Passing Other Cars, through Passing Zones or Passing Lanes (Factor E) vari ed only by truck types. Truck drivers operating truck/trailer perceived this factor to be more important than other drivers did. They were more concerned with this factor probably because it is more difficult for them to find a chance to pass other vehicles due to the large le ngth of their truck than for straig ht truck, or tractor semi-trailer truck drivers. 5.9 Perceptions on Truck Driving Environment by Time of Day A total of 147 truck drivers answered the ques tions pertaining to the time of day that they currently drive their truck and the time of day th at they would prefer to drive it. A multiple-

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181 response question was utilized for this issue, so the response frequencies exceeded the number of respondents. The response frequencies of current and preferred truck driv ing times of day were observed to explore how the quality of truck dr iving environment may differ by time of day. The intent of these questions was to determine whet her drivers perceive conditions to be better at other times of day than the times that they actu ally travel, since many dr ivers do not have control over their driving times. The relationships between the drivers backgrounds and their preference on truck driving time of day were also investigated. 5.9.1 Current and Preferred Truck Driving Time of Day The distribution of the current and preferred truck driving time of day of the drivers are presented in Figure 5-8. A cons iderable number of the drivers (34/147 or 23%) were not driving at specific times of day regularly. The time peri od most used and preferre d for truck driving was from 9AM to noon, while a small portion of the driv ers were driving or pr eferred to drive their truck between 7PM and midnight. The time pe riods between 6AM and 9AM, noon and 3PM, and 3PM and 7PM were used for truck driving to a similar degree, but more drivers (56/147 or 38%) preferred to drive between 6AM to 9AM while fewer driver s (33/147 or 22%) preferred to drive between 3PM and 7PM. Ten percent more drivers (51/147 or 35% versus 37/147 or 25%) preferred to drive from midnight to 6AM than were actually driving during that time period. Overall, most of the drivers were driving dur ing the day time (6AMPM). The time period from midnight to noon was relatively more pref erred by the drivers than that from noon to midnight. The preference on truck driving times of day was also examined from another perspective. It was examined whether the drivers who curren tly drive during each time period still preferred to drive during that same time interval. These pr oportions are shown in Figure 5-9. This was to observe how much portion of the dr ivers using each time frame still prefers to drive on the time

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182 period. The time periods between midnight a nd 3PM were generally preferred by about 80 percent of the current users, while the time peri ods between 3PM and midnight were preferred by only about 55 percent of the current users. A total of 38 truck company managers identif ied their preference on truck driving time of day as shown in Figure 5-10. Most of the resp ondents preferred nighttime deliveries. A significant portion of them (22/38 or 58%) preferred their drivers to travel between midnight and 6AM, while day time (from 9AM to 7PM) was ha rdly preferred as truck driving time by the respondents. 5.9.2 Relationships between Truck Drivers Backgrounds and Their Perceptions on Preferred Truck Driving Time of Day Potential correlations between truck driver s working and socio-economic characteristics and their preference on different tr uck driving times of day were i nvestigated with a categorical data analysis method. Each background charac teristics of the respondents was individually tested if their prefer ences on truck driving times of day vary by the levels of the background characteristics. Chi-squared test of independe nce were applied to the survey data. When a variable (i.e., background charac teristics) with three levels was significant in the test, Chisquared test was performed again for each pair of the levels of the variable to observe how the perceptions differed by different levels of the variable. The Chi-squared test results are presented from Table 5-38 to 5-41. The preference of truck driving time of da y between midnight and 6AM (Time Period A) varied by truck company types, primary load t ypes, hauling distance, number of working days per week, fleet size, type of goods carried, and tr uck types. Truck drivers from private truck companies, long-haul drivers, TL drivers, or drivers working more than 5 days per week preferred to travel during this time period more than others did. Truck drivers whose truck

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183 company operates between 500 and 10,000 trucks had more preference on this time period than those working for a company with at least 10,000 or less than 500 trucks. The preferences on this time period of truck driver s carrying grains/feed, household goods of stationary, auto parts, vehicles, machinery, textiles, metals, manufactur ed goods, waste and scrap, equipment, furniture, wood products, stone, clay, and concrete products glass, and hazardous materials was less than those of other truck drivers. Truck drivers operati ng twin trailer, 3or 4-axle semi-trailer less preferred to travel during this period, while those operating larger trucks such as 5-axle semitrailer or rocky mountain double more preferred to travel during this period. Overall, the results show that truck drivers traveling more distan ce, carrying heavier frei ght, or operating larger trucks more preferred to travel during this time period. They may more prefer to travel at between midnight and 6AM to avoid traffic cong estion more frequently occurring during day time. The maneuverability of their truck is likely to be much more restrict ed than that of other trucks, due to the more wei ght or size of their truck. The preference of truck driving time of day between 6AM and 9AM (Time Period B) varied by earning methods and truck types. Truck drivers who get paid by the mile less preferred to travel during this time period, while truck dr ivers operating 3-axle se mi-trailer preferred to drive during this time period more than others. Most truck drivers getting paid by the mile will try to avoid this AM peak time (6AM 9AM) becau se they cannot travel as much farther as they can travel during other times of day. The preference of truck driving time of day between 9AM and noon (Time Period C) varied by earning methods, primary load types, hauling distance, number of working days per week, type of goods carried, and truck types. Truck drivers who get paid by the mile less preferred to travel during this time period, while those who get paid by the hour had more

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184 preference on this time period. LTL drivers more preferred this time period than TL drivers. Short-haul drivers had more preference on this time period, while drivers working more than 5 days per week less preferred to travel during this time period than others. The preference on this time period of truck drivers carry ing grains/feed, household goods of stationary, auto parts, vehicles, machinery, textiles, metals, manufact ured goods, chemicals and allied products, paper and allied products, coal and pe troleum, equipment, furniture, wood products, stone, clay, and concrete products was more than those of other truck drivers. Truck dr ivers operating 3or 4axle semi-trailer more preferred to travel during this period than others. Overall, the results show that short-haul drivers or drivers having so me general characteristic s of short-haul drivers (i.e., truck drivers getting paid by the hour, truck drivers carrying lighter freight) more preferred to travel during this time period. Short-haul drivers usually get paid by the hour to get compensated for traffic delay experienced on the ro ads unintentionally. Most of them travel in relatively urbanized areas during day time, so are much more likely to experience traffic delay. However, they may not feel the need to avoid th e delay by traveling at night time since they get paid by the hour. It is also tr ue that their delivery schedule is set up during day time that they have no choice but to travel during day time. Most shippers and receivers will not be available at night time for shor t-haul deliveries. The preference of truck driving time of day between noon and 3PM (Time Period D) varied by earning methods, primary load types, ha uling distance, percent of late delivery, number of working days per week, type of goods carried, and truck types. Truck drivers who get paid by the mile had less preference on this time peri od. LTL drivers more preferred this time period than TL drivers. Short-haul driv ers, or drivers working at most 5 days per week more preferred to travel during this time period than others di d. Truck drivers more th an 5 percent of whose

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185 deliveries are late had more preference on this time period. The preferences on this time period of truck drivers carrying grains/feed, household goods of stationary, auto part s, vehicles, textiles, livestock, metals, manufactured goods, chemicals, pa per and allied products, coal and petroleum, waste and scrap, equipment, furniture, stone, clay, and concrete products, and glass were greater than those of other truck drivers. Truck driver s operating 4-axle semi-trailer preferred to travel during this period more. Again, short-haul driver s or drivers having some general characteristics of short-haul drivers (i.e., truc k drivers carrying lighter freight, tr uck drivers more than 5 percent of whose deliveries are late) more preferred to travel during this tim e period than other drivers. Short-haul drivers relatively often travel in urbanized areas, so th ere is more chance of being late for a delivery. The preference of truck driving time of day between 3PM and 7PM (Time Period E) varied by number of working days per week a nd type of goods carried. Truck drivers working more than 5 days per week less pr eferred to travel during this tim e period than others did. Truck drivers carrying textiles, coal a nd petroleum, equipment, stone, clay, and concrete products, or glass had more preference on this time period. The preference of truck driving time of da y between 7PM and midnight (Time Period F) varied by independence, earning methods, maximu m governed truck speed, and truck types. Truck drivers having some genera l characteristics of long-haul drivers (i.e., independent truck drivers, truck drivers who get paid by the mile, or drivers whose truck is governed at more than 65 mi/h) had more preference on th is time period. There is less traffic during this time period than during day time, so they can go a longer di stance by driving faster. Truck drivers operating straight (single unit) truck or tw in trailer had more preference on this time period as well. It is not clear why they preferred to travel during this time period.

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186 5.10 Other Transportation Service Issues for Truck Drivers The survey respondents were asked to list any other factors that might be important to truck trip quality in addition to th e ones presented in the surveys. The driver survey participants at the FTDC event did not provide inputs on this matter, probably due to the long length and complexity of the survey. However, various ot her factors were pointed out by the postage-paid mail-back driver survey respondents. Each of t hose factors is listed with its frequency (i.e., the number of respondents who mentioned the factor) in this section. Most manager respondents did not list any other factor. Some respondents repeated the factor(s) that were already given in the surveys. It was intended to not in clude those factors in the lists. 5.10.1 Freeway Truck Operations The other factors contributing to truck trip quality on freeways are listed in Table 5-42. Availability and security of tr uck parking facilities, frequenc y of scale/inspection stations along a route, and accessibility or location of truck stops were mentioned by more than one respondents. As far as other dr ivers behavior is concerned, a number of specific factors were identified, and thus separately summarized in Table 5-43. The respondents were sensitive to slow vehicles in the left-most or center lane. This issue is obviously correlated with the implementation of left-lane truck restriction. Education of motoring pub lic about truck driving characteristics, other drivers improper use of turn signals, and other drivers use of cell phones without hands-free device s were pointed out by more than one respondent. Many respondents also showed their re peated concerns about truck la ne restriction, frequency and timing of construction activities, and speed di fferential between cars and trucks, even though they were evaluated in the prev ious sections of the surveys.

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187 5.10.2 Urban Arterial Truck Operations The other factors contributing to truck trip quality on urban ar terials are listed in Table 544. Various aspects of signage condition were id entified by four respondents. Availability, size, and law enforcement of truck parking space s were mentioned by three respondents and two respondents were concerned with n ight-time lighting condition. W ith respect to other drivers behavior, frequency of the driver s cutting off in front of trucks and frequency of the drivers speeding up not to allow trucks to change lanes were mentioned by two respondents, respectively. 5.10.3 Two-Lane Highway Truck Operations The other factors contributing to truck trip quality on two-lane highways are listed in Table 5-45. Availability of turning maneuvers was identified by two respondents. Unlike passenger car drivers, truck driver have di fficulty turning their truck when they happen to travel in the wrong direction on a two-lane highway, due to its big size. They may have to wait until they reach an intersection wide enough for them to turn. Two respondents were concerned with frequency of school buses. When truck driver s travel behind a slow school bus on a long stretch of a two-lane highway, they are not allowe d to pass it, having to fo llow it for a long time. Traffic signal operational characteristics in small towns on a route was identified by two respondents as well. Truck driver s often have to pass small town s on two-lane highway routes. They want to minimize delays and number of st ops experienced in the small towns as they do on urban arterial routes.

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188 Table 5-1. Truck Driver Survey Re spondent Background Summary Statistics Variable Surveys collected at FTDC event Postage-paid Mail-back Surveys Total Surveys Gender (%): male/female/NA(1) 99/1/0 90/7/3 93/5/2 Age in years (%): 209/30/409/50/60+/NA 4/24/48/21/3/0 4/17/36/28/13/2 4/19/40/26/10/1 Existence of dependents (%): yes/no/NA 92/7/1 77/21/2 82/16/2 Average truck driving job experience in years (standard deviation in parenthesis) 18.1 (8.9) 17.3 (11.5) 17.6 (10.7) Company type (%): private/for-hire/both/NA 36/55/7/2 14/71/13/2 21/66/11/2 Load type (%): TL/LTL/both/NA 36/42/21/1 63/8/27/2 54/19/25/2 Owner-Operator truck driver (%): yes/no/NA 7/93/0 32/67/1 24/75/1 Average one-way truck driving distance for a delivery in miles (standard deviation in parenthesis) 298 (479) 856 (625) 674 (637) Truck travel route and departure time determination (%): Driver/manager/both/NA 9/62/28/1 39/25/35/1 29/37/33/1 Hauling distance (%): short haul/long haul/both/NA 55/42/2/1 6/92/0/2 22/76/1/1 Truck speed governed (%): yes/no/NA 96/3/1 74/24/2 81/17/2 Average governed truck speed in miles per hour (standard deviation in parenthesis) 65.5 (3.3) 70.1 (5.5) 68.4 (5.3) Earning methods (multiple choices, %): by mile/hour/salary/drop/load/other 34/42/4/14/4/2 64/6/2/11/16/1 53/20/3/12/11/1 Average percent of truck trips that are empty (standard deviation in parenthesis) 16.2 (16.7) 17.8 (16.1) 17.3 (16.3) Average percent of truck deliveries that are late (standard deviation in parenthesis) 6.2 (12.2) 2.0 (4.5) 3.3 (8.1) Average percent of truck trips that are made on unfamiliar roads (standard deviation in parenthesis) 8.7 (12.9) 17.4 (19.9) 14.6 (18.5) (1) Not Available survey responses (the respondents did not answer the question.)

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189 Table 5-2. Additional Truck Driver Survey Respondent Background Summa ry Statistics (only from the respondents at FTDC event) Variable Surveys collected at FTDC event Race (%): Caucasian/Native American/African American/Hispanic/Others 70/5/6/17/2 Education level (%): Some or no high school/High school diploma or equivalent/ Technical college degree (A.A.)/Co llege degree/Post-graduate degree/NA(1) 6/76/ 9/7/1/1 Annual income level in thousand dollars (%): 254/35/509/70/100+/NA 2/20/54/18/3/3 Company fleet size in number of trucks (%): <50/5099/10099/500999/1,000,999/5,0000,000/10,000+/NA 5/3/14/13/12/21/31/1 Average truck driving days per week (standa rd deviation in parenthesis) 5.1 (0.6) Average truck driving hours per day (standa rd deviation in parenthesis) 9.9 (2.1) Average number of nights per week away from their home for a delivery (standard deviation in parenthesis) 1.3 (2.1) Average percent of number of tr uck trips on each roadway type: freeway/rural multilane highway/rural 2-lane highway/urban arterial 51/17/14/17 Average percent of truck driving hours on each roadway type: freeway/rural multilane highway/rural 2-lane highway/urban arterial 52/16/15/17 Truck types (multiple choices, %): Straight truck/Truck trailer/Twin trailer or Doubles/ 3-axle tractor semitrailer/4-axle tractor semitrailer/5-axle tractor semitrailer/ Truck double trailer/Turnpike double/Tank truck/Flat bed 6.3/3.9/19.1/ 11.7/19.1/33.2/ 0.4/2.0/2.0/2.3 Type of goods carried (multiple choices, %): food/grains, feed/household goods or stationary/auto parts/vehicles/ machinery/textiles/livestock/metals /manufactured goods/chemicals/ paper and allied products/c oal or petroleum/chemic als and allied products/ waste and scrap/equipment/furniture /wood products except furniture/ stone, clay, and concrete products/glass/hazardous materials/ unknown packages (LTL)/flower 7.6/4.1/7.6/6.3/1.8/ 4.7/5.7/0.4/4.9/6.9/5.7/ 6.5/1.1/3.7/ 1.4/4.8/5.7/4.6/ 4.9/4.7/5.7/ 0.6/0.2 (1) Not Available survey responses (the re spondents did not answer the question.)

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190 Table 5-3. Truck Company Manager Survey Respondent Background Summary Statistics Variable Total Surveys Gender (%): male/female 89/11 Age in years (%): 20/30/4049/50/60+ 3/21/26/37/13 Average truck company manager job experience in years (standard deviation in parenthesis) 12.8 (10.9) Company type (%): private/for-hire/both 24/68/8 Load type (%): TL/LTL/both/NA(1) 66/13/16/5 Job duties as a truck company manager (multiple choices, %): Manage truck travel routes and schedules for delivery Manage trucking equipment or facilities Manage truck operation safety Make contracts with customer s or manage public relations Manage personnel 31 8 33 20 8 Company fleet size in number of trucks (%): <50/5099/10099/500999/1,000,999/5,0000,000/10,000+/NA 18/3/29/13/16/13/5/3 Average percent of hauling distance of the truck trips their companies make (standard deviation in parenthesis): Short haul Long haul 47.3 (39.2) 52.7(39.2) Average percent of independent truck drivers working at their companies (standard deviation in parenthesis) 25.2 (40.4) Average percent of their companies tr uck deliveries that are late (standard deviation in parenthesis) 8.9 (9.6) Average percent of their truck deliv ery service performance level that is evaluated by on-time delivery (standard deviation in parenthesis) 76.5 (32.8) Preferred truck driving time (multiple choices, %): Midnightam/6amam/9amnoon/ Noonpm/3pmpm/7pmmidnight 47/21/7/ 4/6/15 Average percent of the respondents co ncerns on following issues on Floridas transportation services for trucking business (standard deviation in parenthesis): Operating cost On-time performance Truck drivers trip satisfaction 46.8 (21.6) 30.1 (22.8) 24.1 (15.0) (1) Not Available survey responses (the re spondents did not answer the question.)

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191 Table 5-4. Additional Truck Company Mana ger Survey Respondent Background Summary Statistics (only from the respondents at FTDC event) Variable Surveys collected at FTDC event Truck travel route and depart ure time determination (%): Driver/manager/both driver and manager/ customer/driver, manager, and customer 18/55/9/ 9/9 Annual income level in thousand dollars (%): 209/30/409/ 509/70/100149 9/18/18/ 28/18/9 Education level (%): High school diploma or equivalent/ Technical college degree (A.A.)/College degree 64/ 18/18 Methods by which truck drivers of their companies get paid (multiple choices, %): By mile/hour/salary/drop/load/other 34/42/4/14/4/2 Truck types their companies typical ly operate (multiple choices, %): Straight truck/Truck trailer/Twin trailer or Doubles/ 3-axle tractor semitrailer/4-axle tractor semitrailer/5-axle tractor semitrailer/ Tank truck/Dump truck/Fat bed 20/4/12/ 4/16/32/ 4/4/4 Type of goods carried by their companies (multiple choices, %): food/grains, feed/household goods or stationary/auto parts/vehicles/ machinery/textiles/livestock/metals /manufactured goods/chemicals/ paper and allied products/c oal or petroleum/chemic als and allied products/ waste and scrap/equipment/furniture /wood products except furniture/ stone, clay, and concrete products/glass/hazardous materials/others 12.0/4.5/6.0/6.0/0.0/ 4.5/4.5/0.0/7.0/9.0/6.0/ 7.5/0.0/4.5/ 1.5/3.0/4.5/6.0/ 1.5/1.5/6.0/4.5

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192 Table 5-5. Truck Drivers Perceptions on Each Factor on Truck Travel Quality of Service on Freeways Factor Mean RISa (Rank in Parentheses) Mean RSSb (Rank in Parentheses) Mean IPSc (Rank in Parentheses) Passenger Car Drivers Knowledge about Truck Driving Characteristics on Freeways 6.69 (1) 1.95 (1) 27.46 (1) Passenger Car Drivers Road Etiquette 6.49 (2) 2.09 (2) 25.58 (2) Availability and Condition of Signage 6.28 (3) 5.13 (19) 2.36 (16) Pavement Condition 6.06 (4) 4.39 (12) 5.62 (9) Level of Congestion 6.01 (5) 3.28 (4) 12.30 (4) Lane Widths 5.88 (6) 4.93 (18) 2.35 (17) Roadway Striping Condition (including reflectors) 5.87 (7) 4.68 (17) 4.40 (12) Shoulder Width and Condition 5.80 (8) 4.52 (15) 4.00 (14) Frequency and Timing of Construction Activities 5.77 (9) 3.81 (7) 7.51 (7) Length of Merge or Diverge Lanes 5.73 (10) 4.39 (12) 4.48 (11) Availability of Alternative Routes 5.71 (11) 3.90 (8) 6.63 (8) Number of Lanes 5.60 (12) 4.47 (14) 4.01 (13) Amount of Merge or Diverge Traffic 5.55 (13) 4.15 (9) 4.86 (10) Lighting Conditions at Night 5.52 (14) 4.63 (16) 2.45 (15) Lane(s) Restricted from Truck Use 5.50 (15) 3.09 (3) 14.11 (3) Lower Speed Limit Only Applied to Truck Traffic 5.33 (16) 3.30 (6) 11.35 (5) Governed Truck Speed Lower than Speed Limit 5.19 (17) 3.28 (4) 11.31 (6) Availability of Traveler Information Systems (HAR, 511, CB Radio, VMS, etc.) 4.91 (18) 4.36 (11) 1.77 (18) Publicity/Advertising of Traveler Information Systems 4.65 (19) 4.16 (10) 1.57 (19) Sample Size 163167 180187 152159 a Relative Importance Score of Each Factor (1, 1=Least Important, 7=Most Important) b Relative Satisfaction Score of Each Factor (1, 1=Least Satisfied, 7=Most Satisfied) c Improvement Priority Score of Each Factor ( 42 +42)

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193 Table 5-6. Managers Perceptions on Rela tive Importance of Each Factor on Freeways Factor Mean OTBa (Rank in Parentheses) Mean OCb (Rank in Parentheses) Mean OPc (Rank in Parentheses) Mean TSd (Rank in Parentheses) Level of Congestion 5.95 (1) 5.82 (1) 6.06 (1) 6.42 (1) Frequency and Timing of Construction Activities 5.66 (2) 5.00 (2) 6.06 (1) 6.03 (3) Availability of Alternative Routes 5.21 (3) 4.78 (4) 5.24 (4) 5.54 (7) Passenger Car Drivers Knowledge about Truck Driving Characteristics on Freeways 5.12 (4) 4.91 (3) 4.82 (6) 5.81 (4) Passenger Car Drivers Road Etiquette 5.00 (5) 4.73 (5) 4.62 (7) 6.11 (2) Number of Lanes 4.82 (6) 4.45 (7) 5.03 (5) 5.42 (8) Pavement Condition 4.69 (7) 4.48 (6) 4.29 (11) 5.64 (5) Availability and Condition of Signage 4.64 (8) 3.81 (13) 5.39 (3) 5.37 (9) Lower Speed Limit Only Applied to Truck Traffic 4.62 (9) 4.36 (9) 4.47 (9) 5.19 (10) Governed Truck Speed Lower than Speed Limit 4.58 (10) 4.45 (7) 4.53 (8) 4.97 (15) Amount of Merge or Diverge Traffic 4.56 (11) 4.12 (11) 4.03 (12) 5.61 (6) Lane(s) Restricted from Truck Use 4.41 (12) 4.18 (10) 4.44 (10) 4.94 (17) Lighting Conditions at Night 4.17 (13) 3.88 (12) 3.85 (14) 5.11 (12) Roadway Striping Condition (including reflectors) 4.06 (14) 3.58 (15) 3.88 (13) 5.19 (10) Shoulder Width and Condition 3.88 (15) 3.70 (14) 3.47 (18) 5.00 (14) Lane Widths 3.86 (16) 3.47 (16) 3.82 (15) 4.97 (15) Length of Merge or Diverge Lanes 3.66 (17) 3.41 (17) 3.13 (19) 5.11 (12) Availability of Traveler Information Systems (HAR, 511, CB Radio, VMS, etc.) 3.64 (18) 3.29 (18) 3.66 (16) 4.44 (18) Publicity/Advertising of Traveler Information Systems 3.47 (19) 3.26 (19) 3.53 (17) 4.09 (19) Sample Size 280 313 314 346 a Relative Importance Score of Each Factor to Overall Trucking Business (1, 1=Least Important, 7=Most Important) b Relative Importance Score of Each Factor to Op erating Cost (1, 1=Least Important, 7=Most Important) c Relative Importance Score of Each Factor to Ontime Performance (1, 1=Least Important, 7=Most Important) d Relative Importance Score of Each Factor to truck drivers Trip Satisfaction (1, 1=Least Important, 7=Most Important)

PAGE 194

194 Table 5-7. Exploratory Factor Anal ysis Results (Freeways) Rotated Factor Loadings Latent Factor and Allied Items F1 F2 F3 F4 F5 Communality Factor 1: Physical Roadway Components Lighting Conditions at Night .73 .04 .12 .04 .18 .58 Shoulder Width and Condition .69 .19 .17 .09 .14 .57 Lane Widths .64 .06 .04 .16 .25 .50 Length of Merge or Diverge Lanes .62 .08 .09 .00 .28 .48 Availability and Condition of Signage .57 .09 .00 .20 .09 .38 Roadway Striping Condition (including reflectors) .52 .22 .20 .01 .20 .40 Pavement Condition (.43) .21 .32 .02 (.56) .65 Factor 2: Passenger Car Drivers Behavior Passenger Car Drivers Knowledge about Truck Driving Characteristics on Freeways .14 .90 .07 .11 .07 .85 Passenger Car Drivers Road Etiquette .09 .89 .10 .11 .17 .85 Factor 3: Traveler Information Usage Availability of Traveler Information Systems (HAR, 511, CB Radio, VMS, etc.) .00 .07 .88 .13 .10 .81 Publicity/Advertising of Traveler Information Systems .09 .01 .78 .07 .17 .65 Availability of Alternative Routes (.36) .22 (.48) .11 .09 .43 Factor 4: Truck Travel Restrictions Lower Speed Limit Only Applied to Truck Traffic .03 .04 .02 .85 .04 .73 Governed Truck Speed Lower than Speed Limit .15 .16 .04 .75 .03 .61 Lane(s) Restricted from Truck Use .10 .00 .02 .68 .33 .58 Factor 5: Volume/Capacity Ratio Frequency and Timing of Construction Activities .01 .00 .10 .09 .68 .48 Level of Congestion .20 .26 .34 .10 .52 .50 Number of Lanes (.46) .20 .18 .07 (.52) .56 Amount of Merge or Diverge Traffic (.56) .22 .07 .10 (.37) .51 Sum of Squares (Eigenvalue) 3.3 2.0 2.0 1.9 1.9 11.1 Percent of Trace 17.6 10.5 10.4 10.0 9.9 58.4 the item loaded highly on two or more factors the item did not load highly on any factor

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195 Table 5-8. Importance of Each Factor on Tr uck Travel Quality of Service on Freeways Factor Items Mean RIS (Overall Rank in Parentheses) Factor Summated Mean (Standard Deviation in Parentheses) Passenger Car Drivers Knowledge about Truck Driving Characteristics on Freeways 6.69 (1) Passenger Car Drivers Behavior (F2) Passenger Car Drivers Road Etiquette 6.49 (2) 6.61 (.89) Availability and Condition of Signage 6.28 (3) Pavement Condition 6.06 (4) Lane Widths 5.88 (6) Roadway Striping Condition (including reflectors) 5.87 (7) Shoulder Width and Condition 5.80 (8) Length of Merge or Diverge Lanes 5.73 (10) Physical Roadway Components (F1) Lighting Conditions at Night 5.52 (14) 5.90 (.82) Level of Congestion 6.01 (5) Frequency and Timing of Construction Activities 5.77 (9) Number of Lanes 5.60 (12) Volume/ Capacity Ratio (F5) Amount of Merge or Diverge Traffic 5.55 (13) 5.76 (.92) Lane(s) Restricted from Truck Use 5.50 (15) Lower Speed Limit Only Applied to Truck Traffic 5.33 (16) Truck Travel Restrictions (F4) Governed Truck Speed Lower than Speed Limit 5.19 (17) 5.30 (1.43) Availability of Alternative Routes* 5.71 (11) Availability of Traveler Information Systems (HAR, 511, CB Radio, VMS, etc.) 4.91 (18) Traveler Information Usage (F3) Publicity/Advertising of Traveler Information Systems 4.65 (19) 5.09 (1.02) the item loaded highly on two or more factors the item did not load highly on any factor

PAGE 196

196 Table 5-9. Truck Drivers Pe rceptions on Applicability of Single Performance Measure (ASPM) to Determine Truck Travel Quality of Service on Freeways ASPM(1) Hypothetical Single Performance Measure Mean Standard Deviation A Consistently Good Ride Quality (to enhance ride comfort and minimize impact on goods or equipment) 5.58 1.44 Ease of Maintaining a Consistent Speed, whether Higher or Lower than Posted Speed Limit (to enhance driv ing safety and minimize acceleration and deceleration) 5.38 1.66 Ease of Obtaining Useful Travel Conditions Information (to avoid expected congested areas or harsh weather) 4.88 1.73 Ease of Driving at or above the Posted Speed Limit (to minimize total travel time) 4.82 1.95 (1) How well each performance measure would be applicab le to evaluate the quality of a truck trip, if it were the only performance measure used (1, 1=No t at all Applicable, 7=Perfectly Applicable) Table 5-10. Truck Company Managers Percepti ons of Relative Importance of Each Truck Driving Condition on Freeways for trucking business RI(1) Hypothetical Truck Driving Condition Mean Standard Deviation Ease of Maintaining a Consistent Speed, whether Higher or Lower than Posted Speed Limit (to enhance driv ing safety and minimize acceleration and deceleration) 6.20 0.99 Ease of Driving at or above the Posted Speed Limit (to minimize total travel time) 5.66 1.78 A Consistently Good Ride Quality (to enha nce ride comfort and minimize impact on goods or equipment) 5.57 1.31 Ease of Obtaining Useful Travel Conditions Information (to avoid expected congested areas or harsh weather) 5.43 1.54 (1) Relative Importance of Each Truck Driving Cond ition on Freeways for Trucking Business (1, 1=Least Important, 7=Most Important)

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197 Table 5-11. Games-Howell Post Hoc Test Results (Freeways) Pairwise Mean Comparisons (1) d.f q (calculated) Results Factor A vs Factor B 762 2.51 Population means are not different Factor A vs Factor C 750 8.59 Population means are different Factor A vs Factor D 713 8.71 Population means are different Factor B vs Factor C 773 5.77 Population means are different Factor B vs Factor D 755 6.11 Population means are different Factor C vs Factor D 763 0.69 Population means are not different Mean Comparison Summary Factor A Factor B > Factor C Factor D (1) Factor Labels A. A Consistently Good Ride Quality B. Ease of Maintaining a Consistent Speed, whethe r Higher or Lower than Posted Speed Limit C. Ease of Obtaining Useful Travel Conditions Information D. Ease of Driving at or above the Posted Speed Limit Note: Bolded q values are significant at the 95% confidence level () 4 ( 05 0 q = 3.63)

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198 Table 5-12. Truck Drivers Per ceptions of Each Factor on Truck Travel Quality of Service on Urban Arterials Factor Mean RISa (Rank in Parentheses) Mean RSSb (Rank in Parentheses) Mean IPSc (Rank in Parentheses) Passenger Car Drivers Road Etiquette 6.16 (1) 2.11 (1) 21.49 (1) Passenger Car Drivers Knowledge about Truck Driving Characteristics on Urban Arterials 6.14 (2) 2.30 (2) 19.44 (2) Curb Radii for Right Turning at Intersections 6.07 (3) 3.74 (6) 8.07 (5) Pavement Condition 5.99 (4) 4.45 (16) 5.31 (7) Availability and Condition of Signage 5.89 (5) 4.53 (18) 3.15 (16) Coordinated Traffic Signal Timings at Intersections along the Arterial for Continuous Traffic Flow 5.78 (6) 3.40 (4) 9.27 (4) Lane Widths 5.70 (7) 4.22 (14) 4.15 (12) Level of Vehicle Congestion 5.66 (8) 3.12 (3) 9.86 (3) Frequency and Timing of Construction Activities 5.58 (9) 3.66 (5) 8.35 (6) Roadway Striping Condition 5.54 (10) 4.31 (15) 3.30 (15) Shoulder Width and Condition 5.53 (11) 4.20 (13) 3.43 (14) Number of Lanes 5.45 (12) 3.96 (10) 4.60 (11) Existence of Left Turn Signal Phase at Intersections 5.45 (13) 3.91 (9) 4.59 (10) Length of Yellow Signal Timing at Intersections 5.44 (14) 3.81 (7) 4.66 (9) Traffic Signal Responsiveness at Intersections 5.37 (15) 3.82 (8) 4.83 (8) Placement of Light Poles, Trees, etc. at Roadside 5.31 (16) 4.18 (12) 2.24 (17) Stop Bar Position for Truck Turning at Intersections 5.25 (17) 4.00 (11) 3.63 (13) Level of Bicycle or Pedestrian Congestion 4.65 (18) 4.48 (17) 0.91 (18) Sample Size 73~76 93~97 64~68 a Relative Importance Score of Each Factor (1, 1=Least Important, 7=Most Important) b Relative Satisfaction Score of Each Factor (1, 1=Least Satisfied, 7=Most Satisfied) c Improvement Priority Score of Each Factor ( +42)

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199 Table 5-13. Managers Perceptions on Relative Importance of Each Factor on Urban Arterials Factor Mean OTBa (Rank in Parentheses)Mean OCb (Rank in Parentheses)Mean OPc (Rank in Parentheses) Mean TSd (Rank in Parentheses) Frequency and Timing of Construction Activities 5.89 (1) 5.86 (1) 6.25 (1) 6.44 (1) Pavement Condition 5.74 (2) 5.86 (1) 5.38 (5) 6.33 (2) Level of Vehicle Congestion 5.59 (3) 5.71 (3) 6.00 (2) 6.11 (4) Existence of Left Turn Signal Phase at Intersections 5.41 (4) 5.57 (4) 5.75 (3) 6.11 (4) Roadway Striping Condition 5.38 (5) 5.00 (9) 4.88 (12) 5.67 (11) Curb Radii for Right Turning at Intersections 5.25 (6) 5.43 (5) 4.63 (16) 6.22 (3) Length of Yellow Signal Timing at Intersections 5.23 (7) 5.00 (9) 5.00 (8) 5.78 (8) Traffic Signal Responsiveness at Intersections 5.22 (8) 5.00 (9) 5.00 (8) 5.78 (8) Passenger Car Drivers Knowledge about Truck Driving Characteristics on Urban Arterials 5.17 (9) 5.43 (5) 5.25 (6) 5.67 (11) Coordinated Traffic Signal Timings at Intersections along the Arterial for Continuous Traffic Flow 5.17 (9) 5.29 (7) 5.63 (4) 6.00 (6) Availability and Condition of Signage 5.04 (11) 5.29 (7) 5.00 (8) 5.56 (16) Passenger Car Drivers Road Etiquette 5.02 (12) 4.71 (16) 4.75 (13) 5.67 (11) Placement of Light Poles, Trees, etc. at Roadside 4.97 (13) 4.71 (16) 5.00 (8) 5.44 (17) Shoulder Width and Condition 4.89 (14) 5.00 (9) 4.75 (13) 5.89 (7) Stop Bar Position for Truck Turning at Intersections 4.87 (15) 4.57 (18) 4.38 (18) 5.67 (11) Number of Lanes 4.73 (16) 4.86 (13) 5.25 (6) 5.67 (11) Lane Widths 4.72 (17) 4.86 (13) 4.63 (16) 5.78 (8) Level of Bicycle or Pedestrian Congestion 4.54 (18) 4.86 (13) 4.75 (13) 5.33 (18) Sample Size 6 7 8 9 a Relative Importance Score of Each Factor to Overall Trucking Business (1, 1=Least Important, 7=Most Important) b Relative Importance Score of Each Factor to Op erating Cost (1, 1=Least Important, 7=Most Important) c Relative Importance Score of Each Factor to Ontime Performance (1, 1=Least Important, 7=Most Important) d Relative Importance Score of Each Factor to truck drivers Trip Satisfaction (1, 1=Least Important, 7=Most Important)

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200 Table 5-14. Exploratory Factor Analysis Results (Urban Arterials) Rotated Factor Loadings Latent Factor and Allied Items F1 F2 F3 F4 Communality Factor 1: Roadway and Traffic Components Number of Lanes .83 .12 .03 .12 .72 Lane Widths .68 .26 .04 .29 .62 Pavement Condition .66 .42 .23 .03 .67 Shoulder Width and Condition .65 .01 .37 .41 .73 Availability and Condition of Signage* (.52) .21 .00 .20 .35 Frequency and Timing of Construction Activities (.53) .29 .40 .10 .54 Roadway Striping Condition (.52) .01 .37 .40 .57 Level of Vehicle Congestion .40 .31 .35 .06 .38 Factor 2: Intersection Crossing Constraints Existence of Left Turn Signal Phase at Intersections .30 .78 .01 .09 .71 Length of Yellow Signal Timing at Intersections .00 .76 .02 .43 .76 Coordinated Traffic Signal Timings at Intersections along the Arterial for Continuous Traffic Flow .18 .74 .36 .10 .72 Traffic Signal Responsiveness at Intersections .17 .70 .15 .33 .65 Curb Radii for Right Turning at Intersections* .28 (.51) .10 .02 .35 Factor 3: Passenger Car Drivers Behavior Passenger Car Drivers Road Etiquette .13 .09 .91 .22 .90 Passenger Car Drivers Know ledge about Tr uck Driving Characteristics on Urban Arterials .07 .21 .89 .12 .86 Factor 4: Physical Driving Deterrents Placement of Light Poles, Trees, etc. at Roadside .32 .13 .10 .75 .69 Level of Bicycle or Pedestrian Congestion .06 .15 .25 .73 .62 Stop Bar Position for Truck Turning at Intersections .12 .21 .13 .63 .47 Sum of Squares (Eigenvalue) 3.4 3.1 2.5 2.3 11.3 Percent of Trace 18.7 17.2 13.7 12.9 62.5 the item did not load highly on any factor

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201 Table 5-15. Importance of Each Factor on Truck Travel Quality of Service on Urban Arterials Factor Items Mean RIS (Overall Rank in Parentheses) Factor Summated Mean (Standard Deviation in Parentheses) Passenger Car Drivers Road Etiquette 6.16 (1) Passenger Car Drivers Behavior (F3) Passenger Car Drivers Knowledge about Truck Driving Characteristics on Urban Arterials 6.14 (2) 6.09 (1.37) Pavement Condition 5.99 (4) Availability and Condition of Signage* 5.89 (5) Lane Widths 5.70 (7) Level of Vehicle Congestion 5.66 (8) Frequency and Timing of Construction Activities 5.58 (9) Roadway Striping Condition 5.54 (10) Shoulder Width and Condition 5.53 (11) Roadway and Traffic Components (F1) Number of Lanes 5.45 (12) 5.65 (.89) Curb Radii for Right Turning at Intersections* 6.07 (3) Coordinated Traffic Signal Timings at Intersections along the Arterial for Continuous Traffic Flow 5.78 (6) Existence of Left Turn Signal Phase at Intersections 5.45 (12) Length of Yellow Signal Timing at Intersections 5.44 (14) Intersection Crossing Constraints (F2) Traffic Signal Responsiveness at Intersections 5.37 (15) 5.58 (.82) Placement of Light Poles, Trees, etc. at Roadside 5.31 (16) Stop Bar Position for Truck Turning at Intersections 5.25 (17) Physical Driving Deterrents (F4) Level of Bicycle or Pedestrian Congestion 4.65 (18) 5.03 (1.19) the item did not load highly on any factor

PAGE 202

202 Table 5-16. Truck Drivers Pe rceptions of Applicability of Single Performance Measure (ASPM) to Determine Truck Travel Quality of Service on Urban Arterials ASPM(1) Hypothetical Single Performance Measure Mean Standard Deviation A Consistently Good Ride Quality (to enhance ride comfort and minimize impact on goods or equipment) 5.14 1.58 Ease of Changing Lanes (to prepare for making turns) 4.79 1.75 Ease of Rightor Left-Turn Maneuvers 4.79 1.78 Ease of Maintaining a Consistent Speed, whether Higher or Lower than Posted Speed Limit (to enhance driving saf ety and minimize acceleration and deceleration) 4.76 1.87 Ease of Passing through Signalized Intersections along the Arterial (to minimize stops or delays) 4.33 1.91 Ease of Driving at or above the Posted Speed Limit (to minimize total travel time) 4.32 1.83 Ease of U-Turn Maneuvers 3.62 2.14 (1) How well each performance measure would be applicable to evaluate quality of truck trip, if it were the only performance measure used (1, 1=Not at all Applicable, 7=Perfectly Applicable) Table 5-17. Truck Company Managers Percepti ons of Relative Importance of Each Truck Driving Condition on Urban Arterials for trucking business RI( 1) Hypothetical Truck Driving Condition Mean Standard Deviation Ease of Rightor Left-Turn Maneuvers 5.94 1.41 Ease of Maintaining a Consistent Speed, whether Higher or Lower than Posted Speed Limit (to enhance driv ing safety and minimize acceleration and deceleration) 5.70 1.33 Ease of Changing Lanes (to prepare for making turns) 5.45 1.66 A Consistently Good Ride Quality (to enha nce ride comfort and minimize impact on goods or equipment) 5.30 1.26 Ease of Passing through Signalized Intersections along the Arterial (to minimize stops or delays) 5.18 1.49 Ease of Driving at or above the Posted Speed Limit (to minimize total travel time) 4.94 1.84 Ease of U-Turn Maneuvers 4.33 2.3 (1) Relative Importance of Each Truck Driving Conditi on on Urban Arterials for Trucking Business (1, 1=Least Important, 7=Most Important)

PAGE 203

203 Table 5-18. Games-Howell Post Hoc Test Results (Urban Arterials) Pairwise Mean Comparisons (1) d.f q (calculated) Results Factor A vs Factor B 764 4.17 Population means are different Factor A vs Factor C 762 4.17 Population means are different Factor A vs Factor D 748 4.33 Population means are different Factor A vs Factor E 744 9.09 Population means are different Factor A vs Factor F 756 9.49 Population means are different Factor A vs Factor G 709 15.88 Population means are different Factor B vs Factor C 772 0.03 Population means are not different Factor B vs Factor D 766 0.29 Population means are not different Factor B vs Factor E 765 4.90 Population means are different Factor B vs Factor F 771 5.17 Population means are different Factor B vs Factor G 741 11.72 Population means are different Factor C vs Factor D 768 0.26 Population means are not different Factor C vs Factor E 767 4.84 Population means are different Factor C vs Factor F 771 5.10 Population means are different Factor C vs Factor G 745 11.62 Population means are different Factor D vs Factor E 769 4.46 Population means are different Factor D vs Factor F 769 4.71 Population means are different Factor D vs Factor G 756 11.13 Population means are different Factor E vs Factor F 769 0.14 Population means are not different Factor E vs Factor G 760 6.88 Population means are different Factor F vs Factor G 752 6.88 Population means are different Mean Comparison Summary Factor A > Factor B Factor C Factor D > Factor EFactor F > Factor G (1) Factor Labels A. A Consistently Good Ride Quality B. Ease of Changing Lanes C. Ease of Rightor Left-Turn Maneuvers D. Ease of Maintaining a Consistent Speed, whethe r Higher or Lower than Posted Speed Limit E. Ease of Passing through Signalized Intersections along the Arterial F. Ease of Driving at or above the Posted Speed Limit G. Ease of U-Turn Maneuvers Note: Bolded q values are significant at the 95% confidence level () 7 ( 05 0 q = 4.17)

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204 Table 5-19. Truck Drivers Per ceptions of Each Factor on Truck Travel Quality of Service on Two-Lane Highways Factor Mean RISa (Rank in Parentheses) Mean RSSb (Rank in Parentheses) Mean IPSc (Rank in Parentheses) Passenger Car Drivers Knowledge about Truck Driving Characteristics on Two-Lane Highways 6.38 (1) 2.18 (1) 21.10 (1) Passenger Car Drivers Road Etiquette 6.28 (2) 2.36 (2) 19.59 (2) Availability and Condition of Signage 6.17 (3) 4.45 (19) 4.50 (11) Pavement Condition 5.99 (4) 4.04 (14) 5.50 (9) Lighting Conditions at Night 5.81 (5) 3.73 (7) 7.19 (5) Shoulder Width and Condition 5.80 (6) 3.45 (3) 8.78 (3) Lane Widths 5.80 (6) 3.95 (11) 6.41 (7) Frequency and Timing of Construction Activities 5.64 (8) 3.69 (6) 7.35 (4) Roadway Striping Condition 5.59 (9) 4.19 (16) 3.88 (13) Level of Vehicle Congestion 5.58 (10) 3.54 (4) 7.02 (6) Frequency of Passing Lanes 5.47 (11) 3.65 (5) 5.51 (8) Sight Distance at Horizontal Curvatures 5.38 (12) 4.13 (15) 2.91 (15) Frequency of Passing Zones (Dashed Yellow Lines) 5.07 (13) 3.96 (12) 2.71 (16) Frequency of Faster Vehi cles Passing Your Truck 5.04 (14) 3.79 (9) 3.26 (14) Frequency of Vehicles much Slower than Your Truck 5.03 (15) 3.79 (9) 4.90 (10) Frequency of Faster Vehicles Following Your Truck 4.97 (16) 3.74 (8) 4.17 (12) Frequency of Farm Tractors, Bicyclists, Pedestrians 4.80 (17) 4.21 (17) 0.75 (17) Availability of Traveler Information Systems (HAR, 511, CB Radio, VMS, etc.) 4.62 (18) 4.23 (18) 0.71 (18) Publicity/Advertising of Traveler Information Systems 4.27 (19) 3.99 (13) 0.50 (19) Sample Size 66~69 75~78 61~64 a Relative Importance Score of Each Factor (1, 1=Least Important, 7=Most Important) b Relative Satisfaction Score of Each Factor (1, 1=Least Satisfied, 7=Most Satisfied) c Improvement Priority Score of Each Factor ( 42 +42)

PAGE 205

205 Table 5-20. Managers Perceptions of Relativ e Importance of Each Factor on Two-Lane Highways Factor Mean OTBa (Rank in Parentheses)Mean OCb (Rank in Parentheses)Mean OPc (Rank in Parentheses) Mean TSd (Rank in Parentheses) Roadway Striping Condition 5.65 (1) 5.71 (1) 5.25 (3) 6.13 (2) Level of Vehicle Congestion 5.46 (2) 5.14 (4) 5.00 (4) 5.75 (5) Pavement Condition 5.23 (3) 5.43 (2) 5.00 (4) 6.00 (3) Passenger Car Drivers Knowledge about Truck Driving Characteristics 5.11 (4) 5.14 (4) 5.38 (2) 6.25 (1) Shoulder Width and Condition 5.03 (5) 5.29 (3) 4.88 (6) 5.63 (8) Frequency and Timing of Construction Activities 4.96 (6) 5.14 (4) 5.50 (1) 5.63 (8) Lighting Conditions at Night 4.76 (7) 4.57 (9) 4.50 (11) 4.88 (13) Sight Distance at Horizontal Curvatures 4.72 (8) 4.86 (7) 4.63 (8) 5.88 (4) Passenger Car Drivers Road Etiquette 4.70 (9) 4.50 (11) 4.43 (14) 5.57 (7) Frequency of Passing Zones (Dashed Yellow Lines) 4.70 (9) 4.57 (9) 4.63 (8) 5.25 (10) Frequency of Vehicles much Slower than Your Truck 4.62 (11) 4.71 (8) 4.75 (7) 5.75 (5) Frequency of Passing Lanes 4.54 (12) 4.43 (12) 4.50 (11) 5.13 (11) Lane Widths 4.53 (13) 4.43 (12) 4.13 (16) 4.88 (13) Availability of Traveler Information Systems (HAR, 511, CB Radio, VMS, etc.) 4.49 (14) 3.83 (17) 3.57 (18) 4.43 (18) Frequency of Faster Vehicles Following Your Truck 4.16 (15) 4.00 (16) 3.88 (17) 4.75 (17) Frequency of Farm Tractors, Bicyclists, Pedestrians 4.08 (16) 4.29 (14) 4.63 (8) 4.88 (13) Availability and Condition of Signage 4.01 (17) 4.29 (14) 4.25 (15) 4.88 (13) Publicity/Advertising of Traveler Information Systems 4.00 (18) 3.40 (19) 3.50 (19) 3.50 (19) Frequency of Faster Vehi cles Passing Your Truck 4.00 (18) 3.71 (18) 4.50 (11) 5.13 (11) Sample Size 4~6 5~7 6~8 6~8 a Relative Importance Score of Each Factor to Overall Trucking Business (1, 1=Least Important, 7=Most Important) b Relative Importance Score of Each Factor to Op erating Cost (1, 1=Least Important, 7=Most Important) c Relative Importance Score of Each Factor to Ontime Performance (1, 1=Least Important, 7=Most Important) d Relative Importance Score of Each Factor to truck drivers Trip Satisfaction (1, 1=Least Important, 7=Most Important)

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206 Table 5-21. Exploratory Factor Analysis Results (Two-Lane Highways) Rotated Factor Loadings Latent Factor and Allied Items F1 F2 F3 F4 F5 Communality Factor 1: Travel Safety Elements Sight Distance at Horizontal Curvatures .71 .10 .16 .16 .15 .59 Frequency of Faster Vehicles Following Your Truck .68 .09 .05 .26 .16 .57 Lighting Conditions at Night .65 .27 .08 .31 .09 .61 Frequency of Farm Tractors, Bicyclists, Pedestrians .62 .08 .33 .18 .14 .55 Shoulder Width and Condition* (.59) .19 .03 .17 .16 .44 Frequency of Faster Vehi cles Passing Your Truck* .39 (.58) .26 .14 .23 .63 Factor 2: Traveler Information Usage Availability of Traveler Information Systems (HAR, 511, CB Radio, VMS, etc.) .11 .83 .01 .22 .07 .75 Publicity/Advertising of Traveler Information Systems .03 .73 .01 .18 .04 .57 Frequency of Vehicles much Slower than Your Truck .31 .72 .10 .22 .03 .67 Factor 3: Travel Speed Constraints Frequency of Passing Zones (Dashed Yellow Lines) .01 .08 .90 .07 .02 .82 Frequency of Passing Lanes .06 .18 .86 .01 .02 .78 Level of Vehicle Congestion* .04 .16 (.47) .27 .26 .39 Frequency and Timing of Construction Activities* .16 .03 .42 .01 .07 .25 Factor 4: Physical Roadway Components Pavement Condition .29 .15 .07 .78 .06 .72 Roadway Striping Condition .40 .06 .19 .70 .03 .69 Availability and Condition of Signage .16 .08 .00 .62 (.45) .61 Lane widths* .24 .30 .29 .41 .09 .41 Factor 5: Passenger Car Drivers Behavior Passenger Car Drivers Road Etiquette .14 .36 .07 .05 .81 .81 Passenger Car Drivers Knowledge about Truck Driving Characteristics on 2Lane Highways .32 .08 .15 .08 .80 .78 Sum of Squares (Eigenvalue) 2.9 2.5 2.4 2.1 1.7 11.6 Percent of Trace 15.1 13.4 12.2 11.2 9.2 61.0 the item loaded highly on two or more factors the item did not load highly on any factor

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207 Table 5-22. Importance of Each Factor on Tr uck Travel Quality of Service on Two-Lane Highways Factor Items Mean RIS (Overall Rank in Parentheses) Factor Summated Mean (Standard Deviation in Parentheses) Passenger Car Drivers Knowledge about Truck Driving Characteristics on 2Lane Highways 6.38 (1) Passenger Car Drivers Behavior (F5) Passenger Car Drivers Road Etiquette 6.28 (2) 6.39 (.85) Availability and Condition of Signage 6.17 (3) Pavement Condition 5.99 (4) Lane widths* 5.80 (6) Physical Roadway Components (F4) Roadway Striping Condition 5.59 (9) 5.86 (.84) Frequency and Timing of Construction Activities* 5.64 (8) Level of Vehicle Congestion* 5.58 (10) Frequency of Passing Lanes 5.47 (11) Travel Speed Constraints (F3) Frequency of Passing Zones (Dashed Yellow Lines) 5.07 (13) 5.47 (.88) Lighting Conditions at Night 5.81 (5) Shoulder Width and Condition* 5.80 (6) Sight Distance at Horizontal Curvatures 5.38 (12) Frequency of Faster Vehi cles Passing Your Truck* 5.04 (14) Frequency of Faster Vehicles Following Your Truck 4.97 (16) Travel Safety Elements (F1) Frequency of Farm Tractors, Bicyclists, Pedestrians 4.80 (17) 5.29 (.84) Availability of Traveler Information Systems (HAR, 511, CB Radio, VMS, ) 4.62 (18) Publicity/Advertising of Traveler Information Systems 4.27 (19) Traveler Information Usage (F2) Frequency of Vehicles much Slower than Your Truck 5.03 (15) 4.65 (1.10) the item loaded highly on two or more factors the item did not load highly on any factor

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208 Table 5-23. Truck Drivers Pe rceptions of Applicability of Single Performance Measure (ASPM) to Determine Truck Travel Quality of Service on Two-Lane Highways ASPM(1) Hypothetical Single Performance Measure Mean Standard Deviation Probability of Being Passed or Followed by Faster Vehicles 5.10 1.87 A Consistently Good Ride Quality (to enhance ride comfort and minimize impact on goods or equipment) 5.02 1.53 Width of Travel Lane and Shoulder, or Shoulder Type (to cope with unexpected situations) 4.55 1.99 Probability of Encountering Possible Conflic ts (with farm tractors, bicyclists, pedestrians, wildlife, etc.) 4.53 1.88 Opportunities for Passing Other Cars, through Passing Zones or Passing Lanes (to minimize total travel time) 3.70 1.99 (1) How well each performance measure would be applicable to evaluate quality of truck trip, if it were the only performance measure used (1, 1=Not at all Applicable, 7=Perfectly Applicable) Table 5-24. Truck Company Managers Percepti ons of Relative Importance of Each Truck Driving Condition on Two-Lane Highways for trucking business RI(1) Hypothetical Truck Driving Condition Mean Standard Deviation Width of Travel Lane and Shoulder, or Shoulder Type (to cope with unexpected situations) 5.5 1.58 Opportunities for Passing Other Cars, through Passing Zones or Passing Lanes (to minimize total travel time) 5.29 1.66 Probability of Encountering Possible Conflic ts (with farm tractors, bicyclists, pedestrians, wildlife, etc.) 5.24 1.42 A Consistently Good Ride Quality (to enha nce ride comfort and minimize impact on goods or equipment) 5.09 1.46 Probability of Being Passed or Foll owed by Faster Vehicles 5.06 1.56 (1) Relative Importance of Each Truck Driving Cond ition on Freeways for Trucking Business (1, 1=Least Important, 7=Most Important)

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209 Table 5-25. Games-Howell Post Hoc Test Results (Two-Lane Highways) Pairwise Mean Comparisons (1) d.f. q (calculated) Results Factor A vs Factor B 739 0.95 Population means are not different Factor A vs Factor C 765 5.61 Population means are different Factor A vs Factor D 768 5.98 Population means are different Factor A vs Factor E 763 14.15 Population means are different Factor B vs Factor C 720 5.19 Population means are different Factor B vs Factor D 738 5.59 Population means are different Factor B vs Factor E 717 14.47 Population means are different Factor C vs Factor D 765 0.18 Population means are not different Factor C vs Factor E 766 8.29 Population means are different Factor D vs Factor E 763 8.35 Population means are different Mean Comparison Summary Factor A Factor B > Factor C Factor D > Factor E (1) Factor Labels A. Probability of Being Passed or Followed by Faster Vehicles B. A Consistently Good Ride Quality C. Width of Travel Lane and Shoulder, or Shoulder Type D. Probability of Encountering Possible Conflicts E. Opportunities for Passing Other Cars, through Passing Zones or Passing Lanes Note: Bolded q values are significant at the 95% confidence level () 5 ( 05 0 q = 3.86)

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210 0 5 10 15 20 25 30 1234 Relative Importance (1 = Most Important, 4 =Least Important)Response Frequenc y Traffic Conditions (mean rank = 2.1) Other Drivers' Behaviors (mean rank = 2.2) Roadway Conditions (mean rank = 2.3) Traveler Information (mean rank = 3.4) Figure 5-1. Relative Importance of Each Factor Category for Freeways

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211 0 2 4 6 8 10 12 14 1234Relative Importance (1 = Most Important, 4 = Least Important)Response Frequency Traffic Conditions (mean rank = 2.3) Other Drivers' Behavior (mean rank = 2.4) Roadway Conditions (mean rank = 2.5) Signal Conditions (mean rank = 2.9) Figure 5-2. Relative Importa nce of Each Factor Cate gory for Urban Arterials

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212 0 5 10 15 20 25 1234Relative Importance (1 = Most Important, 4 = Least Important)Response Frequency Roadway Conditions (mean rank = 2.1) Traffic Conditions (mean rank = 2.2) Other Drivers' Behavior (mean rank = 2.5) Traveler Information (mean rank = 3.2) Figure 5-3. Relative Importa nce of Each Factor Cate gory for Two-lane Highways

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213 0 10 20 30 40 50 60 70 80 90 100 1234Relative Importance (1 = Most Important, 4 = Least Important)Cumulative Response Frequency (%) Freeway Arterial Two-Lane Highway Figure 5-4. Relative Importance of Roadwa y Conditions on Different Roadway Types

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214 0 10 20 30 40 50 60 70 80 90 100 1234Relative Importance (1 = Most Important, 4 = Least Important)Cumulative Response Frequency (%) Freeway Arterial Two-Lane Highway Figure 5-5. Relative Importance of Traffi c Conditions on Different Roadway Types

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215 0 10 20 30 40 50 60 70 80 90 100 1234Relative Importance (1 = Most Important, 4 = Least Important)Cumulative Response Frequency (%) Freeway Arterial Two-Lane Highway Figure 5-6. Relative Importance of Other Drivers Behavior on Different Roadway Types

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216 0 2 4 6 8 10 12 14 1234Relative Improvement Priority (1 = Most in Need of Improvement, 4 = Least in Need of Improvement)Response Frequency Urban Arterial (mean rank = 2.2) Rural Multilane Highway (mean rank = 2.4) Rural Two-lane Highway (mean rank = 2.5) Freeways (mean rank = 2.8) Figure 5-7. Improvement Priority of Various Roadway Facilities for Truck Trip Quality

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217 Table 5-26. Definitions of Independent Variables used in Statistical Tests (1) Variable Description Code Definition 0 Survey respondents at the FTDC event Source Recruitment Sources 1 Postage-page mail-back survey respondents 0 Man Gen Gender 1 Woman 0 < 30 (young) 1 30 and age < 50 (middle-aged) Age Age in Years 2 50 (senior) 0 No Dep Existence of Dependent(s) 1 Yes 0 < 5 years 1 5 years and < 15 years Emp Years of Truck Driving Job Experience 2 15 years 0 No Indep Independent Truck Driver 1 Yes 0 No Paid by Miles Driven (M) 1 Yes 0 No Paid by Hours Driven (H) 1 Yes 0 No Paid by Salary (S) 1 Yes 0 No Paid by Drop (D) 1 Yes 0 No Earn (Multiple Choices) Paid by Load (L) 1 Yes 0 Private (carry own goods) 1 For-hire (carry other peoples goods) CType Businesses Types of Truck Company 2 Combination of private and for-hire 0 TruckLoad (TL) 1 Less-Than-truckLoad (LTL) LType Primary Load Types 2 Both TL and LTL (approximately equally) 0 Truck Driver 1 Manager (transportation/logistics/dispatch) RDTSel Selection of Truck Route and Departure Time 2 Both truck driver and manager 0 Short-haul HDist Hauling Distance 1 Long-haul 0 No GSpeed Whether the Truck Sped is Engine-Governed 1 Yes 0 65 mi/h MGSpeed Engine-Governed Maximum Truck Speed 1 > 65 mi/h

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218 Table 5-27. Definitions of Independent Variables used in Statistical Tests (2) Variable Description Code Definition 0 < 10 percent 1 10 percent and < 25 percent ETrip Percent of Truck Trips that are Empty 2 25 percent 0 5 percent LDel Percent of Truck Trips that are Late 1 > 5 percent 0 < 10 percent 1 10 percent and < 25 percent PFam Percent of Truck Trips that are not made on Familiar Roads 2 25 percent 0 No Caucasian (CC) 1 Yes 0 No Native American (NA) 1 Yes 0 No African American (AA) 1 Yes 0 No Race Hispanic (HP) 1 Yes 0 No College Edu Level of Education 1 College or Post-graduate Degree 0 < $50,000 1 $50,000 and < $70,000 Inc Annual Income 2 $70,000 0 5 days DayW Number of Working Days per Week 1 > 5 days 0 8 hours HourD Number of Working Hours per Day 1 > 8 hours 0 < 2 nights NightW Number of Nights Staying away from Home 1 2 nights 0 < 500 trucks 1 500 trucks and < 10,000 trucks FSize Company Fleet Size 2 10,000 trucks 0 No (for each type of goods) G (Multiple Choices) Types of Goods Carried 1 Yes (for each type of goods) 0 No (for each truck type) T (Multiple Choices) Truck Types 1 Yes (for each 11 truck type) 0 No (for each time of day) CTDTime (Multiple Choices) Current Truck Driving Time of Day 1 Yes (for each time of day)

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219 Table 5-28. Kruskal-Wallis and Mann-Wh itney Test Statistics (Freeways-1) Factors Variable Factor A* Factor B* Factor C* Factor D* Source (0, 1) 1.96 (z) 1.33 (z) 2.54 (z) 3.86 (z) Gen (0, 1) 0.98 (z) 0.91 (z) 1.22 (z) 0.73 (z) Age (0, 1, 2) 3.34 ( 2 df=2) 0.03 ( 2 df=2) 4.71 ( 2 df=2) 3.88 ( 2 df=2) Dep (0, 1) 0.75 (z) 1.17 (z) 0.05 (z) 1.64 (z) Emp (0, 1, 2) 3.57 ( 2 df=2) 1.13 ( 2 df=2) 1.33 ( 2 df=2) 1.95 ( 2 df=2) Indep (0, 1) 1.25 (z) 0.94 (z) 1.72 (z) 0.93 (z) Earn_M (0, 1) 2.07 (z) 0.70 (z) 2.13 (z) 1.86 (z) Earn_H (0, 1) 2.39 (z) 0.51 (z) 1.92 (z) 3.63 (z) Earn_S (0, 1) 0.61 (z) 0.16 (z) 1.37 (z) 0.62 (z) Earn_D (0, 1) 0.99 (z) 1.77 (z) 0.86 (z) 0.43 (z) Earn_L (0, 1) 0.91 (z) 0.55 (z) 0.16 (z) 0.69 (z) 1.99 ( 2 df=2) 1.05 ( 2 df=2) 1.18 ( 2 df=2) 8.00 ( 2 df=2) 1.64 (z, 0 vs 1) 1.94 (z, 1 vs 2) CType (0, 1, 2) 2.87 (z, 0 vs 2) 0.82 ( 2 df=2) 0.61 ( 2 df=2) 3.28 ( 2 df=2) 8.09 ( 2 df=2) 2.81 (z, 0 vs 1) 2.14 (z, 1 vs 2) LType (0, 1, 2) 0.41 (z, 0 vs 2) RDTSel (0, 1, 2) 0.29 ( 2 df=2) 0.24 ( 2 df=2) 1.26 ( 2 df=2) 1.52 ( 2 df=2) HDist (0, 1) 0.91 (z) 0.18 (z) 1.85 (z) 3.74 (z) GSpeed (0, 1) 0.48 (z) 0.39 (z) 0.31 (z) 0.14 (z) MGSpeed (0, 1) 1.77 (z) 1.40 (z) 2.66 (z) 5.09 (z) ETrip (0, 1, 2) 0.69 ( 2 df=2) 2.77 ( 2 df=2) 1.35 ( 2 df=2) 1.37 ( 2 df=2) PFam (0, 1, 2) 1.65 ( 2 df=2) 0.94 ( 2 df=2) 1.45 ( 2 df=2) 2.39 ( 2 df=2) LDel (0, 1) 0.92 (z) 0.04 (z) 0.15 (z) 0.92 (z) Race_CC (0, 1) 0.31 (z) 1.48 (z) 0.60 (z) 1.18 (z) Race_NA (0, 1) 1.03 (z) 1.07 (z) 0.21 (z) 0.16 (z) Race_AA (0, 1) 0.70 (z) 2.02 (z) 1.25 (z) 0.33 (z) Race_HP (0, 1) 1.44 (z) 0.19 (z) 1.05 (z) 1.34 (z) Edu (0, 1) 1.92 (z) 2.04 (z) 2.44 (z) 0.70 (z) Inc (0, 1, 2) 0.37 ( 2 df=2) 0.10 ( 2 df=2) 0.10 ( 2 df=2) 0.10 ( 2 df=2) DayW (0, 1) 0.03 (z) 0.31 (z) 0.28 (z) 1.87 (z) Factor Labels A. A Consistently Good Ride Quality B. Ease of Maintaining a Consistent Speed, whethe r Higher or Lower than Posted Speed Limit C. Ease of Obtaining Useful Travel Conditions Information D. Ease of Driving at or above the Posted Speed Limit Note: Calculated 2 values are the Kruskal-Wallis ANOVA test results and calculated z values are the Mann-Whitney pair-wise comparison test results. Negative z value indicates that the group with smaller code value perceived the corresponding factor to be more important than the other group did. Bolded values are significant at the 95% confidence level ( 2 df=2, =0.05 = 5.99, 025 0 2 / z = 1.96 for two-tailed test).

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220 Table 5-29. Kruskal-Wallis and Mann-Wh itney Test Statistics (Freeways-2) Factors Variable Factor A* Factor B* Factor C* Factor D* HourD (0, 1) 1.59 (z) 1.75 (z) 0.32 (z) 0.37 (z) NightW (0, 1) 0.94 (z) 0.67 (z) 0.07 (z) 1.56 (z) FSize (0, 1, 2) 2.83 ( 2 df=2) 5.06 ( 2 df=2) 0.01 ( 2 df=2) 2.60 ( 2 df=2) CTDTime (0, 1) (0AMAM) 1.28 (z) 1.83 (z) 0.14 (z) 0.25 (z) CTDTime (0, 1) (6AMAM) 1.57 (z) 1.94 (z) 0.41 (z) 0.57 (z) CTDTime (0, 1) (9AMNoon) 0.84 ( z ) 1.09 ( z ) 0.72 ( z ) 2.27 ( z ) CTDTime (0, 1) (NoonPM) 2.30 (z) 1.30 (z) 1.43 (z) 0.31 (z) CTDTime (0, 1) (3PMPM) 1.64 (z) 0.52 (z) 1.26 (z) 0.41 (z) CTDTime (0, 1) (7PMAM) 0.92 ( z ) 0.34 ( z ) 0.37 ( z ) 0.25 ( z ) Factor Labels A. A Consistently Good Ride Quality B. Ease of Maintaining a Consistent Speed, whethe r Higher or Lower than Posted Speed Limit C. Ease of Obtaining Useful Travel Conditions Information D. Ease of Driving at or above the Posted Speed Limit Note: Calculated 2 values are the Kruskal-Wallis ANOVA test results and calculated z values are the Mann-Whitney pair-wise comparison test results. Negative z value indicates that the group with smaller code value perceived the corresponding factor to be more important than the other group did. Bolded values are significant at the 95% confidence level ( 2 df=2, =0.05 = 5.99, 025 0 2 / z = 1.96 for two-tailed test).

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221 Table 5-30. Kruskal-Wallis and Mann-Whitney Test Statistics (Urban Arterials-1) Factors Variable Factor A* Factor B* Factor C* Factor D* Source (0, 1) 1.25 (z) 0.66 (z) 0.15 (z) 1.13 (z) Gen (0, 1) 0.28 (z) 0.02 (z) 0.65 (z) 0.65 (z) Age (0, 1, 2) 2.60 ( 2 df=2) 1.34 ( 2 df=2) 2.38 ( 2 df=2) 0.55 ( 2 df=2) Dep (0, 1) 0.89 (z) 1.13 (z) 1.05 (z) 1.59 (z) Emp (0, 1, 2) 1.73 ( 2 df=2) 1.21 ( 2 df=2) 1.52 ( 2 df=2) 0.27 ( 2 df=2) Indep (0, 1) 1.56 (z) 0.10 (z) 1.11 (z) 1.34 (z) Earn_M (0, 1) 0.84 (z) 0.67 (z) 0.90 (z) 0.54 (z) Earn_H (0, 1) 1.91 (z) 0.88 (z) 0.53 (z) 0.69 (z) Earn_S (0, 1) 1.06 (z) 0.26 (z) 0.06 (z) 0.26 (z) Earn_D (0, 1) 0.86 (z) 1.86 (z) 0.99 (z) 0.50 (z) Earn_L (0, 1) 0.07 (z) 2.08 (z) 2.84 (z) 1.58 (z) 3.81 ( 2 df=2) 1.66 ( 2 df=2) 6.33 ( 2 df=2) 3.28 ( 2 df=2) 0.50 (z, 0 vs 1) 2.33 (z, 1 vs 2) CType (0, 1, 2) 2.38 (z, 0 vs 2) LType (0, 1, 2) 0.96 ( 2 df=2) 0.52 ( 2 df=2) 0.89 ( 2 df=2) 0.53 ( 2 df=2) RDTSel (0, 1, 2) 0.18 ( 2 df=2) 2.55 ( 2 df=2) 2.37 ( 2 df=2) 3.42 ( 2 df=2) HDist (0, 1) 0.52 (z) 0.11 (z) 0.71 (z) 0.80 (z) GSpeed (0, 1) 0.91 (z) 0.73 (z) 0.06 (z) 1.43 (z) MGSpeed (0, 1) 2.19 (z) 0.00 (z) 0.56 (z) 0.26 (z) ETrip (0, 1, 2) 1.86 ( 2 df=2) 2.57 ( 2 df=2) 1.87 ( 2 df=2) 1.75 ( 2 df=2) PFam (0, 1, 2) 0.66 ( 2 df=2) 0.34 ( 2 df=2) 0.36 ( 2 df=2) 0.05 ( 2 df=2) LDel (0, 1) 1.08 (z) 0.04 (z) 0.50 (z) 0.08 (z) Race_CC (0, 1) 0.89 (z) 0.35 (z) 0.93 (z) 1.23 (z) Race_NA (0, 1) 0.12 (z) 1.48 (z) 1.44 (z) 0.39 (z) Race_AA (0, 1) 1.09 (z) 0.83 (z) 0.60 (z) 1.90 (z) Race_HP (0, 1) 0.23 (z) 0.94 (z) 0.35 (z) 0.33 (z) Edu (0, 1) 0.59 (z) 1.03 (z) 0.93 (z) 0.61 (z) Inc (0, 1, 2) 2.74 ( 2 df=2) 1.34 ( 2 df=2) 1.23 ( 2 df=2) 0.99 ( 2 df=2) DayW (0, 1) 0.13 (z) 0.17 (z) 1.58 (z) 0.27 (z) HourD (0, 1) 2.03 (z) 2.55 (z) 2.41 (z) 1.32 (z) NightW (0, 1) 0.26 (z) 1.59 (z) 0.83 (z) 0.20 (z) FSize (0, 1, 2) 3.21 ( 2 df=2) 5.26 ( 2 df=2) 0.65 ( 2 df=2) 0.26 ( 2 df=2) Factor Labels A. A Consistently Good Ride Quality B. Ease of Changing Lanes C. Ease of Rightor Left-Turn Maneuvers D. Ease of Maintaining a Consistent Speed, whethe r Higher or Lower than Posted Speed Limit Note: Calculated 2 values are the Kruskal-Wallis ANOVA test results and calculated z values are the Mann-Whitney pair-wise comparison test results. Negative z value indicates that the group with smaller code value perceived the corresponding factor to be more important than the other group did. Bolded values are significant at the 95% confidence level ( 2 df=2, =0.05 = 5.99, 025 0 2 / z = 1.96 for two-tailed test).

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222 Table 5-31. Kruskal-Wallis and Mann-Whitney Test Statistics (Urban Arterials-2) Factors Variable Factor A* Factor B* Factor C* Factor D* CTDTime (0, 1) (0AMAM) 0.32 ( z ) 0.49 ( z ) 0.21 ( z ) 0.45 ( z ) CTDTime (0, 1) (6AMAM) 1.09 (z) 1.05 (z) 1.08 (z) 1.44 (z) CTDTime (0, 1) (9AMNoon) 1.26 ( z ) 0.26 ( z ) 0.79 ( z ) 1.57 ( z ) CTDTime (0, 1) (NoonPM) 1.39 (z) 0.35 (z) 0.42 (z) 0.84 (z) CTDTime (0, 1) (3PMPM) 1.33 (z) 0.03 (z) 0.09 (z) 0.49 (z) CTDTime (0, 1) (7PMAM) 1.74 ( z ) 0.75 ( z ) 0.94 ( z ) 0.84 ( z ) Factor Labels A. A Consistently Good Ride Quality B. Ease of Changing Lanes C. Ease of Rightor Left-Turn Maneuvers D. Ease of Maintaining a Consistent Speed, whethe r Higher or Lower than Posted Speed Limit Note: Calculated 2 values are the Kruskal-Wallis ANOVA test results and calculated z values are the Mann-Whitney pair-wise comparison test results. Negative z value indicates that the group with smaller code value perceived the corresponding factor to be more important than the other group did. Bolded values are significant at the 95% confidence level (025 0 2 / z = 1.96 for two-tailed test).

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223 Table 5-32. Kruskal-Wallis and Mann-Whitney Test Statistics (Urban Arterials-3) Factors Variable Factor E* Factor F* Factor G* Source (0, 1) 0.82 (z) 3.32 (z) 3.30 (z) Gen (0, 1) 0.16 (z) 0.87 (z) 0.53 (z) 0.53 ( 2 df=2) 1.03 ( 2 df=2) 6.33 ( 2 df=2) 2.02 (z, 0 vs 1) 1.81 (z, 1 vs 2) Age (0, 1, 2) 1.22 (z, 0 vs 2) Dep (0, 1) 1.27 (z) 1.63 (z) 0.72 (z) Emp (0, 1, 2) 1.52 ( 2 df=2) 0.91 ( 2 df=2) 0.47 ( 2 df=2) Indep (0, 1) 1.65 (z) 0.84 (z) 1.36 (z) Earn_M (0, 1) 1.05 (z) 1.04 (z) 0.81 (z) Earn_H (0, 1) 0.20 (z) 2.82 (z) 2.45 (z) Earn_S (0, 1) 0.34 (z) 0.05 (z) 0.23 (z) Earn_D (0, 1) 0.41 (z) 0.29 (z) 0.53 (z) Earn_L (0, 1) 1.86 (z) 0.31 (z) 2.25 (z) 2.26 ( 2 df=2) 4.61 ( 2 df=2) 6.63 ( 2 df=2) 1.82 (z, 0 vs 1) 1.40 (z, 1 vs 2) CType (0, 1, 2) 2.57 (z, 0 vs 2) 0.84 ( 2 df=2) 7.14 ( 2 df=2) 3.70 ( 2 df=2) 2.66 (z, 0 vs 1) 2.01 (z, 1 vs 2) LType (0, 1, 2) 0.31 (z, 0 vs 2) RDTSel (0, 1, 2) 3.29 ( 2 df=2) 1.82 ( 2 df=2) 2.09 ( 2 df=2) HDist (0, 1) 0.83 (z) 2.13 (z) 2.65 (z) GSpeed (0, 1) 0.93 (z) 0.66 (z) 1.05 (z) MGSpeed (0, 1) 0.53 (z) 3.06 (z) 0.76 (z) ETrip (0, 1, 2) 1.28 ( 2 df=2) 2.89 ( 2 df=2) 2.34 ( 2 df=2) PFam (0, 1, 2) 0.96 ( 2 df=2) 0.63 ( 2 df=2) 1.36 ( 2 df=2) LDel (0, 1) 0.01 (z) 2.14 (z) 2.11 (z) Race_CC (0, 1) 1.90 (z) 0.10 (z) 0.75 (z) Race_NA (0, 1) 0.09 (z) 0.50 (z) 1.67 (z) Race_AA (0, 1) 1.49 (z) 1.30 (z) 0.03 (z) Race_HP (0, 1) 1.70 (z) 0.22 (z) 0.68 (z) Edu (0, 1) 1.00 (z) 0.60 (z) 0.95 (z) Factor Labels E. Ease of Passing through Signalized Intersections along the Arterial F. Ease of Driving at or above the Posted Speed Limit G. Ease of U-Turn Maneuvers Note: Calculated 2 values are the Kruskal-Wallis ANOVA test results and calculated z values are the Mann-Whitney pair-wise comparison test results. Negative z value indicates that the group with smaller code value perceived the corresponding factor to be more important than the other group did. Bolded values are significant at the 95% confidence level ( 2 df=2, =0.05 = 5.99, 025 0 2 / z = 1.96 for two-tailed test).

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224 Table 5-33. Kruskal-Wallis and Mann-Whitney Test Statistics (Urban Arterials-4) Factors Variable Factor E* Factor F* Factor G* Inc (0, 1, 2) 1.81 ( 2 df=2) 1.03 ( 2 df=2) 5.59 ( 2 df=2) DayW (0, 1) 1.58 (z) 2.64 (z) 0.13 (z) HourD (0, 1) 0.76 (z) 0.59 (z) 2.81 (z) NightW (0, 1) 0.91 (z) 0.52 (z) 1.83 (z) FSize (0, 1, 2) 1.71 ( 2 df=2) 3.94 ( 2 df=2) 3.64 ( 2 df=2) CTDTime (0, 1) (0AMAM) 0.23 (z) 1.10 (z) 1.50 (z) CTDTime (0, 1) (6AMAM) 0.59 (z) 0.45 (z) 2.52 (z) CTDTime (0, 1) (9AMNoon) 0.67 ( z ) 1.38 ( z ) 0.92 ( z ) CTDTime (0, 1) (NoonPM) 0.84 (z) 0.71 (z) 1.26 (z) CTDTime (0, 1) (3PMPM) 0.24 (z) 1.46 (z) 0.55 (z) CTDTime (0, 1) (7PMAM) 1.40 ( z ) 0.40 ( z ) 1.11 ( z ) Factor Labels E. Ease of Passing through Signalized Intersections along the Arterial F. Ease of Driving at or above the Posted Speed Limit G. Ease of U-Turn Maneuvers Note: Calculated 2 values are the Kruskal-Wallis ANOVA test results and calculated z values are the Mann-Whitney pair-wise comparison test results. Negative z value indicates that the group with smaller code value perceived the corresponding factor to be more important than the other group did. Bolded values are significant at the 95% confidence level ( 2 df=2, =0.05 = 5.99, 025 0 2 / z = 1.96 for two-tailed test).

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225 Table 5-34. Mann-Whitney Test St atistics (Urban Arterials-5) Factors Types of Goods Carried Factor B* Factor C* Factor G* Food (0, 1) 2.51 (z) 2.04 (z) Auto Parts (0, 1) 2.10 (z) Textiles (0, 1) 2.11 (z) Metals (0, 1) 2.50 (z) Paper and Allied Products (0, 1) 2.04 (z) Chemicals and Allied Products (0, 1) 2.35 ( z ) Equipment (0, 1) 2.05 ( z ) Furniture (0, 1) 2.08 ( z ) Hazardous Materials (0, 1) 2.26 ( z ) Factor Labels B. Ease of Changing Lanes C. Ease of Rightor Left-Turn Maneuvers G. Ease of U-Turn Maneuvers Note: Calculated z values are the Mann-Whitney test results. Positive z value indicates that the group with larger code value perceived the corresponding fact or to be more important than the other group did. Only the z values significant at the 95% confidence level are presented (025 0 2 / z = 1.96 for two-tailed test).

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226 Table 5-35. Kruskal-Wallis and Mann-Whitney Test Statistics (Two-Lane Highways-1) Factors Variable Factor A* Factor B* Factor C* Factor D* Factor E* Source (0, 1) 2.75 (z) 1.76 (z) 0.81 (z) 1.97 (z) 0.41 (z) Gen (0, 1) 2.27 (z) 0.27 (z) 0.62 (z) 1.81 (z) 0.03 (z) Age (0, 1, 2) 5.81 ( 2 df=2) 1.57 ( 2 df=2) 0.36 ( 2 df=2) 3.68 ( 2 df=2) 0.44 ( 2 df=2) Dep (0, 1) 0.94 (z) 0.10 (z) 0.57 (z) 1.64 (z) 1.40 (z) 8.37 ( 2 df=2) 5.61 ( 2 df=2) 0.38 ( 2 df=2) 6.11 ( 2 df=2) 2.30 ( 2 df=2) 0.05 (z, 0 vs 1) 1.7 (z,0 vs 1) 2.7 (z, 1 vs 2) 1.1 (z, 1 vs 2) Emp (0, 1, 2) 1.6 (z, 0 vs 2) 2.4 (z, 0 vs 2) Indep (0, 1) 0.85 (z) 0.05 (z) 1.11 (z) 0.03 (z) 1.29 (z) Earn_M (0, 1) 0.50 (z) 0.37 (z) 1.02 (z) 0.16 (z) 0.45 (z) Earn_H (0, 1) 1.40 (z) 1.27 (z) 1.18 (z) 1.41 (z) 0.05 (z) Earn_S (0, 1) 1.12 (z) 0.84 (z) 0.85 (z) 0.52 (z) 1.01 (z) Earn_D (0, 1) 0.89 (z) 1.92 (z) 1.00 (z) 0.55 (z) 1.30 (z) Earn_L (0, 1) 0.37 (z) 0.45 (z) 1.64 (z) 0.01 (z) 1.92 (z) CType (0, 1, 2) 4.52 ( 2 df=2) 2.37 ( 2 df=2) 0.87 ( 2 df=2) 0.26 ( 2 df=2) 1.80 ( 2 df=2) 3.36 ( 2 df=2) 1.50 ( 2 df=2) 0.82 ( 2 df=2) 7.36 ( 2 df=2) 1.04 ( 2 df=2) 1.3 (z, 0 vs 1) 2.56 (z, 1 vs 2) LType (0, 1, 2) 2.01 (z, 0 vs 2) RDTSel (0, 1, 2) 1.78 ( 2 df=2) 1.99 ( 2 df=2) 1.87 ( 2 df=2) 1.49 ( 2 df=2) 1.93 ( 2 df=2) HDist (0, 1) 0.70 (z) 1.07 (z) 1.26 (z) 0.49 (z) 0.67 (z) GSpeed (0, 1) 0.76 (z) 0.84 (z) 1.26 (z) 1.34 (z) 1.54 (z) 6.09 ( 2 df=2) 4.63 ( 2 df=2) 1.78 ( 2 df=2) 0.40 ( 2 df=2) 0.13 ( 2 df=2) 1.37 (z, 0 vs 1) 2.5 (z, 1 vs 2) ETrip (0, 1, 2) 1.2 (z, 0 vs 2) 3.49 ( 2 df=2) 0.63 ( 2 df=2) 0.76 ( 2 df=2) 7.20 ( 2 df=2) 0.07 ( 2 df=2) 0.84 (z, 0 vs 1) 1.75 (z, 1 vs 2) PFam (0, 1, 2) 2.67 (z, 0 vs 2) LDel (0, 1) 0.92 (z) 2.00 (z) 0.96 (z) 0.90 (z) 0.49 (z) Factor Labels A. Probability of Being Passed or Followed by Faster Vehicles B. A Consistently Good Ride Quality C. Width of Travel Lane and Shoulder, or Shoulder Type D. Probability of Encountering Possible Conflicts E. Opportunities for Passing Other Cars, through Passing Zones or Passing Lanes Note: Calculated 2 values are the Kruskal-Wallis ANOVA test results and calculated z values are the Mann-Whitney pair-wise comparison test results. Negative z value indicates that the group with smaller code value perceived the corresponding factor to be more important than the other group did. Bolded values are significant at the 95% confidence level ( 2 df=2, =0.05 = 5.99, 025 0 2 / z = 1.96 for two-tailed test).

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227 Table 5-36. Kruskal-Wallis and Mann-Whitney Test Statistics (Two-Lane Highways-2) Factors Variable Factor A* Factor B* Factor C* Factor D* Factor E* MGSpeed (0, 1) 0.67 (z) 1.23 (z) 1.57 (z) 0.43 (z) 0.72 (z) Race_CC (0, 1) 0.41 (z) 1.10 (z) 1.28 (z) 0.82 (z) 0.25 (z) Race_NA (0, 1) 0.43 (z) 0.46 (z) 1.29 (z) 0.08 (z) 0.02 (z) Race_AA (0, 1) 1.61 (z) 1.25 (z) 0.37 (z) 1.12 (z) 0.55 (z) Race_HP (0, 1) 2.45 (z) 0.53 (z) 1.18 (z) 0.02 (z) 1.22 (z) Edu (0, 1) 1.18 (z) 1.12 (z) 0.00 (z) 0.96 (z) 0.59 (z) 1.07 ( 2 df=2) 6.92 ( 2 df=2) 6.05 ( 2 df=2) 3.58 ( 2 df=2) 2.29 ( 2 df=2) 1.13 (z, 0 vs 1)0.53 (z, 0 vs 1) 2.5 (z, 1 vs 2) 2.5 (z, 1 vs 2) Inc (0, 1, 2) 1.5 (z, 0 vs 2) 1.5 (z, 0 vs 2) DayW (0, 1) 0.84 (z) 2.24 (z) 0.94 (z) 0.89 (z) 0.12 (z) HourD (0, 1) 0.99 (z) 1.26 (z) 1.94 (z) 1.17 (z) 0.55 (z) NightW (0, 1) 0.34 (z) 1.13 (z) 1.49 (z) 0.12 (z) 1.67 (z) FSize (0, 1, 2) 1.20 ( 2 df=2) 0.63 ( 2 df=2) 1.95 ( 2 df=2) 0.05 ( 2 df=2) 3.14 ( 2 df=2) CTDTime (0, 1) (0AMAM) 1.43 (z) 0.08 (z) 1.11 (z) 0.08 (z) 0.08 (z) CTDTime (0, 1) (6AMAM) 1.11 (z) 0.32 (z) 1.04 (z) 0.76 (z) 0.35 (z) CTDTime (0, 1) (9AMNoon) 1.69 ( z ) 0.30 ( z ) 0.61 ( z ) 0.19 ( z ) 0.70 ( z ) CTDTime (0, 1) (NoonPM) 1.97 (z) 0.62 (z) 0.47 (z) 0.14 (z) 0.65 (z) CTDTime (0, 1) (3PMPM) 1.83 (z) 0.81 (z) 0.27 (z) 0.07 (z) 0.10 (z) CTDTime (0, 1) (7PMAM) 1.65 ( z ) 1.87 ( z ) 1.45 ( z ) 2.14 ( z ) 0.24 ( z ) Factor Labels A. Probability of Being Passed or Followed by Faster Vehicles B. A Consistently Good Ride Quality C. Width of Travel Lane and Shoulder, or Shoulder Type D. Probability of Encountering Possible Conflicts E. Opportunities for Passing Other Cars, through Passing Zones or Passing Lanes Note: Calculated 2 values are the Kruskal-Wallis ANOVA test results and calculated z values are the Mann-Whitney pair-wise comparison test results. Negative z value indicates that the group with smaller code value perceived the corresponding factor to be more important than the other group did. Bolded values are significant at the 95% confidence level ( 2 df=2, =0.05 = 5.99, 025 0 2 / z = 1.96 for two-tailed test).

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228 Table 5-37. Mann-Whitney Test St atistics (Two-Lane Highways-3) Factors Types of Goods Carried Factor A* Factor B* Factor C* Factor D* Factor E* Grains/Feed (0, 1) 2.73 (z) 2.22 (z) Auto Parts (0, 1) 2.12 (z) Waste and Scrap (0, 1) 2.34 (z) Stone, Clay, and Concrete Products (0, 1) 2.12 (z) Hazardous Materials (0, 1) 2.52 ( z ) FedEx (unknown packages) (0, 1) 3.00 ( z ) Truck Type (0, 1) (Straight Truck) 2.63 (z) Truck Type (0, 1) Truck/Trailer 2.18 (z) Truck Type (0, 1) Turnpike Double 2.14 ( z ) Factor Labels A. Probability of Being Passed or Followed by Faster Vehicles B. A Consistently Good Ride Quality C. Width of Travel Lane and Shoulder, or Shoulder Type D. Probability of Encountering Possible Conflicts E. Opportunities for Passing Other Cars, through Passing Zones or Passing Lanes Note: Calculated z values are the Mann-Whitney test results. Positive z value indicates that the group with larger code value perceived the corresponding fact or to be more important than the other group did. Only the z values significant at the 95% confidence level are presented (025 0 2 / z = 1.96 for two-tailed test).

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229 0 10 20 30 40 50 60 70 Midnight 6AM 6AM 9AM9AM NoonNoon 3PM3PM 7PM7PM Midnight No regular hoursTime of DayResponse Frequency Current Truck Driving Time Preferred Truck Driving Time Figure 5-8. Truck Drivers Current and Preferred Truck Driving Times of Day

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230 0 20 40 60 80 100 Midnight 6AM 6AM 9AM9AM NoonNoon 3PM3PM 7PM7PM MidnightTime of DayPercent of the Drivers who Still Preferrd to Drive at Each Time Interval (%) Figure 5-9. Truck Driving Time of Day Preference of Current Users

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231 0 5 10 15 20 25 Midnight 6AM6AM 9AM9AM NoonNoon 3PM3PM 7PM7PM MidnightTime of DayResponse Frequency Figure 5-10. Truck Company Managers Pr eference on Truck Driving Times of Day

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232 Table 5-38. Chi-Squared Test Statistics 1 (2 calculated) Truck Driving Times of Day Variable Time Period A* Time Period B* Time Period C* Time Period D* Time Period E* Time Period F* Age (0, 1, 2) 1.54 0.90 1.29 1.20 0.48 2.74 Dep (0, 1) 0.02 3.42 0.27 0.22 0.15 0.78 Emp (0, 1, 2) 0.28 0.81 0.11 0.55 0.50 2.37 Indep (0, 1) 1.43 0.02 1.38 0.04 0.16 5.38 (+) Earn_M (0, 1) 1.76 6.39 () 5.98 () 5.00 () 0.02 13.93 (+) Earn_H (0, 1) 2.04 0.54 5.75 (+) 1.00 0.01 2.10 Earn_S (0, 1) 0.03 2.14 1.27 1.23 0.48 0.24 Earn_D (0, 1) 0.20 0.00 0.05 0.14 1.26 0.00 Earn_L (0, 1) 0.03 2.14 1.27 0.10 1.10 1.99 13.42 4.58 3.84 3.93 1.98 2.64 7.3 (0, 1, ) 3.9 (1, 2, ) CType (0, 1, 2) 9.2 (0, 2, ) 7.90 1.10 6.39 9.17 4.19 2.11 7.7 (0, 1, ) 5.8 (0, 1, +) 8.9 (0, 1, +) 0.7 (1, 2) 2.6 (1, 2) 0.2 (1, 2) LType (0, 1, 2) 2.3 (0, 2) 0.2 (0, 2) 4.7 (0, 2, +) RDTSel (0, 1, 2) 2.81 0.62 1.11 4.68 2.82 4.82 HDist (0, 1) 4.86 (+) 1.25 6.16 ( ) 9.46 () 2.19 3.75 GSpeed (0, 1) 0.51 3.80 0.55 0.24 0.90 1.23 MGSpeed (0, 1) 2.36 0.02 3.57 0.38 0.69 6.26 (+) ETrip (0, 1, 2) 5.65 2.76 1.23 0.97 0.09 2.24 PFam (0, 1, 2) 4.30 3.83 5.37 3.74 4.20 2.57 LDel (0, 1) 1.09 0.57 0.19 6.45 (+) 0.14 2.07 Race_CC (0, 1) 0.53 0.96 0.14 2.35 1.33 0.01 Race_NA (0, 1) 0.12 0.07 0.73 3.14 2.13 0.11 Race_AA (0, 1) 0.66 3.32 0.00 1.62 0.00 0.39 Race_HP (0, 1) 0.60 0.06 0.22 0.05 0.10 0.48 Edu (0, 1) 0.02 0.52 0.21 1.47 0.03 1.00 Inc (0, 1, 2) 2.31 5.80 0.61 0.69 2.23 2.29 DayW (0, 1) 5.45 0.00 8.90 () 7.40 () 4.40 () 1.44 HourD (0, 1) 0.04 1.34 0.62 2.12 1.15 1.39 Preferred Truck Driving Time of Day Labels A. Midnight 6AM B. 6AM 9AM C. 9AM Noon D. Noon 3PM E. 3PM 7PM F. 7PM Midnight Note: Bolded 2 values are significant at the 95% confidence level ( 2 df=1, =0.05 = 3.84, 2 df=2, =0.05 = 5.99). Positive (+) symbol in parentheses indicate that the gr oup with larger code preferred to drive during the corresponding time period more than the other group did.

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233 Table 5-39. Chi-Squared Test Statistics 2 ( 2 calculated) Truck Driving Times of Day Variable Time Period A* Time Period B* Time Period C* Time Period D* Time Period E* Time Period F* NightW (0, 1) 2.31 0.06 0.35 3.55 0.01 0.92 12.39 1.25 2.31 3.12 0.08 2.57 1.5 (0, 1) 7.3 (1, 2, ) FSize (0, 1, 2) 12.0 (0, 2, ) Preferred Truck Driving Time of Day Labels A. Midnight 6AM B. 6AM 9AM C. 9AM Noon D. Noon 3PM E. 3PM 7PM F. 7PM Midnight Note: Bolded 2 values are significant at the 95% confidence level ( 2 df=1, =0.05 = 3.84, 2 df=2, =0.05 = 5.99). Positive symbol in parentheses indicate that the gro up with larger code preferred to drive during the corresponding time period more than the other group did. Table 5-40. Chi-Squared Test Statistics 3 ( 2 calculated) Truck Driving Times of Day Truck Types Time Period A* Time Period B* Time Period C* Time Period D* Time Period E* Time Period F* Straight Truck (0, 1) 3.97 (+) Twin Trailer (0, 1) 10.94 () 6.38 (+) 3-Axle Semitrailer (0, 1) 13.07 () 5.51 (+) 10.16 (+) 4-Axle Semitrailer (0, 1) 22.83 () 10.81 (+) 7.85 (+) 5-Axle Semitrailer (0, 1) 9.62 (+) Rocky Mountain Double (0, 1) 4.69 (+) Preferred Truck Driving Time of Day Labels A. Midnight 6AM B. 6AM 9AM C. 9AM Noon D. Noon 3PM E. 3PM 7PM F. 7PM Midnight Note: Only the 2 values significant at the 95% confidence level are presented ( 2 df=1, =0.05 = 3.84). Positive symbol in parenthe ses indicate that the group with larger code preferred to drive during the corresponding time period more than the other group did.

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234 Table 5-41. Chi-Squared Test Statistics 4 ( 2 calculated) Truck Driving Times of Day Types of Goods Carried Time Period A* Time Period B* Time Period C* Time Period D* Time Period E* Time Period F* Grains/Feed (0, 1) 7.84 () 5.06 (+) 4.71 (+) Household Goods or Stationary (0, 1) 4.66 () 7.71 (+) 4.39 (+) Auto Parts (0, 1) 6.01 () 8.02 (+) 7.63 (+) Vehicles (0, 1) 5.06 () 3.96 (+) 4.97 (+) Machinery (0, 1) 6.38 () 4.66 (+) Textiles (0, 1) 10.38 () 9.05 (+) 10.65 (+) 4.38 (+) Livestock (0, 1) 6.19 (+) Metals (0, 1) 10.38 () 5.61 (+) 6.07 (+) Manufactured Goods (0, 1) 5.78 () 5.52 (+) 5.23 (+) Chemicals (0, 1) 6.39 (+) 4.29 (+) Paper and Allied Products (0, 1) 11.56 (+) 4.34 (+) Coal and Petroleum (0, 1) 9.44 (+) 6.79 (+) 4.60 (+) Chemicals and Allied Products (0, 1) 6.61 (+) Waste and Scrap (0, 1) 7.04 () Equipment (0, 1) 11.21 () 7.19 (+) 7.27 (+) 5.39 (+) Furniture (0, 1) 6.39 () 4.04 (+) 4.76 (+) Wood Products Except Furniture (0, 1) 7.65 () 7.06 (+) Stone, Clay, and Concrete Products (0, 1) 8.24 () 7.32 (+) 8.01 (+) 4.53 (+) Glass (0, 1) 6.38 () 4.32 (+) 4.06 (+) Hazardous Materials (0, 1) 5.29 () Preferred Truck Driving Time of Day Labels A. Midnight 6AM B. 6AM 9AM C. 9AM Noon D. Noon 3PM E. 3PM 7PM, F. 7PM Midnight Note: Only the 2 values significant at the 95% confidence level are presented ( 2 df=1, =0.05 = 3.84). Positive symbol in parenthe ses indicate that the group with larger code preferred to drive during the corresponding time period more than the other group did.

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235 Table 5-42. Other Factors Affecti ng Truck Trip Quality on Freeways Issues Other Factors Affecting Truck Trip Quality on Freeways Frequency Availability and Security of Rest Areas and/or Truck Parking Spaces (including overnight parking) 12 Accessibility or Location of Truck Stops (near the freeway exit) 2 Accessible Microwave ovens in Turnpike Travel Centers 1 Rest Area /Amenities Availability of Wireless Internet at Rest Areas 1 Frequency of Scales/Inspection Stations 3 Electronic Signs at Weigh Stations Alerting Drivers of Weather Condition (e.g., tornado, thunder storm) 1 Frequency of DOT Inspections at Scales 1 Having to Enter Scales When Bobtailing (only tractor) or Pulling Empty Flatbed 1 Static Scales at Weigh Stations 1 Inspection /Weigh Stations Waiting Time at Agricultural Stations 1 Construction Workers Vigilance of Traffic 1 Ease of Obtaining Truck Drivers License 1 Frequency of Recreational Vehicles (RV) 1 Lower Toll Fees for Trucks 1 Posted Minimum Speed Limit (too low) 1 Traffic Condition /Policy Upgraded Level of Law Enforcement for Speeders and DUIs (Driving Under the Influence of drugs or alcohol) 1 Ease of Obtaining Information about New Regulations 1 Ease of Obtaining Information about Peak Tourist Days 1 Traveler Information Availability of Information about Motels with Truck Parking Spaces 1 Roadway Condition Difference between Vertical Levels of Travel Lane and Shoulder 1

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236 Table 5-43. Other Drivers Behavior Affecting Truck Trip Quality on Freeways Other Drivers Behavior Affecting Tr uck Trip Quality on Freeways Frequency Slow Vehicles in Left-most or Center Lane 9 Education of Motoring Public about Tr uck Driving Characteristics for Safety (e.g., truck braking distance, driving around large trucks) 8 Other Drivers Use of Turn Signal 3 Other Drivers Use of Cell Phones without Hands-free Devices 2 Drivers Cutting in front of Trucks to Enter an On-Ramp 1 Other Drivers Tailgating Behavior 1 Other Drivers Yielding Behavior 1 Passenger Car Drivers Poor Merging Behavior 1 Passenger Car Drivers Understanding of Wei gh Stations and the danger of Trucks 1 Passenger Car Drivers Using Hazard Lights when it rains 1 Speeder/Reckless Drivers 1 Truck Drivers Road Etiquette 1

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237 Table 5-44. Other Factors Affecting Tr uck Trip Quality on Urban Arterials Issues Other Factors Affecting Truck Trip Quality on Urban Arterials Frequency Signage (clarity, brevity, proper location (earlier placement), size, visibility at night) 4 Availability, Proper Sizes, or Law Enforcement of Truck Parking Spaces 3 Roadway Condition Lighting Conditions at Night 2 Accident Accident Clearance time and/or Availability of Alternative Lanes during Accident Clearance Time 1 Traffic Condition Percent of Recreational Vehicles (RV) on the Road 1 Drivers Cutting off in front of Trucks Not Allowing Safe Stopping Distance 2 Drivers Speeding Up Not to Allow Trucks to Change Lanes 2 Drivers Making Right Turns from Left Lane or Left Turns from Right Lane 1 Other Drivers Use of Turn Signals 1 Reckless Motorcyclists 1 Other Drivers Behavior Truck Drivers Road Etiquette 1

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238 Table 5-45. Other Factors Affecting Tr uck Trip Quality on Two-Lane Highways Issues Other Factors Affecting Truck Trip Quality on Two-Lane Highways Frequency Availability of Turning Maneuvers (left turns and U turns) 2 Brightness of Roadway Striping 1 Existence of Rumble strips Near the Center Line 1 Frequency of Small Towns on a R oute (on a delay perspective) 1 Properly Trimmed Trees/Foliage (adequate clearance and sight distance) 1 Roadway Condition Size of Street Signs (visibility and way finding) 1 Frequency of School Buses (that do not allow for other vehicles to pass) 2 Traffic signal Operations in Small Towns (signal responsiveness or coordination) 2 Ease of Maintaining a Consistent Speed 1 Traffic Condition Frequency of Speed Limit Changes 1 Drivers Improperly Cutting in or Pu lling Out in front of a Big Truck (despite the trucks low braking capability) 1 Other Drivers Tailgating Behavior 1 Other Drivers Behavior Recreational Vehicle (RV) Drivers Poor Driving Skills 1

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239 CHAPTER 6 CONCLUSIONS AND RECOMMENDATIONS This study investigated the re lative importance of traffic, roadway, and control factors on various transportation faci lities for the trucking community in terms of truck trip quality. This chapter provides conclusions on the truck trip quality determinants and service measures for each transportation facility and recommendations on the methodol ogies to develop truck LOS estimation models, based on the results of this study. 6.1 Conclusions The three issues important for the trucking co mmunity to evaluate quality of a truck trip were truck travel safety, trav el time, and physical and psychol ogical driving comfort. Truck drivers were more concerned about the driving comfort, while truck company managers were more concerned about travel time. Truck drivers at most truck companies are not very sensitive to travel time because they ar e usually given more than enough time to make deliveries on time and truck travel route and depa rture time are usually determined by the company managers. Thus, the managers are primarily responsible for any late deliveries from unexpected congestions. Independent truck drivers, however, ar e much more sensitive to travel time in that they make delivery appointments and schedule truck trips for their own business. Travel time is a critical concern for the manage rs and independent truck drivers. They typically schedule truck routes and manage their drivers to make deliverie s for customers and the service quality of truck companies evaluated by the customers, primarily based on on-time delivery performance. Travel safety was a very important issue for both driv ers and managers. Truck drivers are typically graded by their accident histor y and safe truck operation has a significant effect on overall trucking business, especially for insurance costs.

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240 The perceptions of both groups are important fo r evaluating truck trip Quality Of Service (QOS), but the perceptions of truck drivers shoul d be primarily addressed because they are the ones who drive on the roadway system. Managers may not be aware of all the situations that truck drivers encounter while traveling, or may no t be sensitive to them since they are not the ones behind the wheel. They certain ly want their drivers to trav el comfortably and safely, but their primary concern is that the deliv ery gets to its destination on time. 6.1.1 Quality of a Truck Trip on Freeways Truck drivers were most concerned about speed variance (or acceleration noise). That is, they were reluctant to experience a driving e nvironment where they have to accelerate or decelerate their truck often due to other drivers inconsiderate behavior and traffic congestion caused by increased traffic volume, construction activities, or truck travel rest rictions (i.e., truck route, lane, or time-of-day rest rictions). The importance leve l of pavement condition was as great as the speed variance for them. The prim ary concern of truck company managers was a travel time issue relative to th e ratio of traffic volume to the roadway capacity. The contributing factors included level of congesti on, construction activities, availabi lity of alternative routes, and number of lanes. They were also concerne d about the speed varian ce and pavement condition, but their importance was greater for truck driver s. Among all the listed factors, Traveler Information Systems (TIS) was perceived to be le ast important and least in need of improvement by both drivers and managers. The table 6-1 summarizes the relative importance of each of the main factors perceived to affect truck trip qu ality on freeways, by both truck drivers and truck company managers. The speed variance (or acceleration noise) complemented by pavement condition was identified as the potential truck LOS service m easure for trucks on freeways. Acceleration or speed variance reflects to a certain extent the psychological comfort of a trip, as more

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241 speed/acceleration variance may reflect more erratic dr iving behavior of other motorists. It also reflects to some extent capacity related issues which are a major concern for managers. The pavement condition reflects the physical comfort of the trip. It may also be a psychological concern, as drivers may worry more about damaging their equipment or the goods on rough pavement. Pavement condition on freeways was al so one of the several biggest concerns for truck company managers. Truck drivers per ceptions on the applicabil ity of these service measures were different by their earning methods, race, level of education, current truck driving time of day. 6.1.2 Quality of a Truck Trip on Arterials The perceptions of truck dr ivers on truck trip QOS determinants on arterials were summarized as maneuverability. The mane uverability concept included multiple factors influencing their ability to make turning mane uvers, change lanes, and avoid acceleration or deceleration activities. Truck drivers were al so concerned about the pavement condition to a great degree and the importance of curb radii and traffic signal coordinatio n, in particular, were emphasized for truck operations at intersections Some physical drivi ng deterrents such as placement of light poles or trees, level of bicycl e or pedestrian congestion, and improper stop bar position were perceived to be least impor tant among all the listed factors. Truck company managers had almost the same concerns as truck drivers identified, but their perceptions on the relative significance of each factor was so mewhat different from that of truck drivers. Travel time was a big issue fo r truck company managers and the importance of pavement condition was greater for them than for tr uck drivers. The importance of the existence of protected left turn signal was significant for truck company managers, but the importance of curb radii and traffic signal coordination was less for the managers than for the drivers. Even though the results on the managers perceptions on the relative importance of the truck trip QOS

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242 determinants are questionable due to the small sample size (6 responses), the perceptions of 33 managers on the relative importan ce of potential service measures indicated that ease of turning maneuvers, speed variance, and traffic density are more important than travel time or pavement quality issues. Ease of U turn maneuver, in particular, was perceived to be least important among all the listed potential perf ormance measures by both drivers and managers. The table 62 summarizes the relative importance of each of the main factors perceived to affect truck trip quality on arterials, by both truck drivers and truck company managers. Truck trip QOS on arterials mainly depends on how freely truck drivers can maneuver. However, the maneuverability on arterial is affected by multi-dimensional factors. Thus, it was not possible to identify one or tw o service measure(s) to adequate ly address the QOS on arterial facilities for trucks. The issu es that should be included to evaluate truck LOS on arterials include pavement condition, ease of turning mane uvers, acceleration variance, ease of changing lanes, and stop-and-go condition. The factors contri buting to those issues include other drivers behavior, pavement quality, level of congesti on, traffic signal coordination, existence of protected left turn signals, adequate curb ra dii, construction activit ies. Truck drivers perceptions on the importance of those factors varied by their earning methods, truck companys primary business types, maximum governed truc k speed, number of working hours per day, and types of goods carried. 6.1.3 Quality of a Truck Trip on Two-Lane Highways The two major concerns of truck drivers trav eling on two-lane highways were probability of being passed or followed and the widths and cond itions of travel lane and shoulder. In this context, other drivers behavior pavement and shoulder widths and surface quality, construction activities, level of congestion, a nd frequency of passing lanes were important to their perceptions on truck trip QOS on two-lane hi ghways. Lighting conditions at night time was also important

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243 to some degree. The widths and conditions of tr avel lane and shoulder we re also important from the managers perspectives, but the managers we re more concerned about the opportunity to pass other vehicles than the probability of being passed or followed by other vehicles. Level of congestion, other drivers behavi or, construction activit ies, and sight distance at horizontal curvatures were important to their perceptions. Roadway stri ping condition, in particular, was considered to be exceptionally important. Again, among all the listed factors, Traveler Information Systems (TIS) was perceived to be le ast important and least in need of improvement by the driver survey respondents. The table 63 summarizes the relative importance of each of the main factors perceived to affect truck trip quality on two-lane highways, by both truck drivers and truck company managers. The resu lts on the managers perceptions on the relative importance of the truck trip QOS determinants ar e questionable due to the small sample size (4 8 responses). However, the perceptions of 34 managers on the relative importance of potential service measures indicated that the widths a nd conditions of travel lane and shoulder and opportunities for passing other cars are the two ma jor concerns of truck company managers. The probability of being passed or followed by other faster vehicles was perceived to be least important in their perceptions. Percent-Time-Being-Followed (PTBF) and Percent-Time-Spent-Following (PTSF), complemented by travel lane and shoulder widths and pavement conditions, were identified as the potential truck LOS service measures for truc ks on two-lane highways. Percent-Time-SpentFollowing (PTSF) is a measure that generally reflects the level of congestion on a two-lane highway, which is definitely a concern for manage rs. However, the truck drivers appear to be more concerned with being followed rather than following. This is a reflection of the psychological comfort level of a driver. If a tr uck is leading a platoon of several vehicles, the

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244 driver will begin to worry about the actions that the following auto drivers may take in trying to get around the truck. Thus, they may be more concerned about what is going on behind them than what is happening in front of them. Ther efore, some combination of Percent-Time-BeingFollowed and Percent-Time-Spent-Following ma y adequately reflect the congestion and psychological concerns of trip quality. Th e lane and shoulder wi dth also reflect the psychological comfort level of the drivers, and pavement condition again relates to the physical (and possibly psychological) comfort of the trip. The shoulder width and condition, in particular, is important for the drivers to cope with an unexpected situation when a broke down occurs (e.g., tire blow-out) as discussed in the focus group se ssions. Truck drivers perceptions on the applicability of these service measures varied by their recruitment sources, gender, level of truck driving experience, percent of empt y truck trips, percent of late deliveries, race, annual income level, number of working days per week, curr ent truck driving time of day, types of goods carried, and truck types. 6.1.4 Improvement Priority of Various Tr ansportation Facilities for Trucks Based on the perceptions of 25 truck driver resp ondents, the order of the roadway types, in order from highest to lowest, identified as most in need of improvement was urban arterials, rural multilane highways, rural two-lane highways, a nd freeways. However, the difference in the needs of improvements among the first three facilities was fairly small. 6.1.5 Improvement Priority of the Factors on Each Transportation Facility for Trucks Improvement Priority Score (IPS) of each factor was extrac ted from Relative Importance Score (IPS) and Relative Satisfaction Score (RSS) by the method presented in chapter 3. The IPS concept was that the higher RIS and/or lower RSS of a factor are, the higher IPS of the factor is. The six most important factors to be considered for transportation serv ice improvement for each

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245 facility type for truck drivers are shown in Table 6-4. This result is based upon the average IPS of the factors. Passenger car drivers behavior knowledge a bout truck driving char acteristics and their road etiquette were perceived to be most in need of improvement for any transportation facility type. Level of traffic conges tion and frequency and timing of construction ac tivities were perceived to be in a considerably significant need of improvement also for any transportation facility type. Truck drivers had very negative feelings about various truck trav el restrictions (in terms of speed, lane, route, and/or driving time of day). Thus, they want to remove any restrictions currently implemente d on FIHS, strongly being opposed to any further restrictions. Traffic signal coordination along arterials and adequate curb radii at intersections were significantly in need of improvement for arteri al facilities, while s houlder width and condition and lighting condition was greatly in need of improvement for two-lane highway facilities. 6.1.6 Preference on Truck Driving Time of Day Most truck driver respondents preferred to drive in the morning time (between 6 AM and noon) and/or during late ni ght time (between midnight and 6 AM). The morning time, especially from 9 AM to noon, was the most preferred time of day for truck driving. The time period between 3 PM and midnight was considerably less preferred by the driver respondents. The late night time was best time of day for efficient truck operation from the tr uck company managers perspectives. Their pref erence on the late night time for truck driving was much greater than that for any other times of day. Most truck drivers and managers appear to agree that the late night time is good for efficient truck operation due to low traffic volum e, but the preference of the drivers on the morning time (between 6 AM and noon) was greater th an that for the late night time. This may indicate that quality of a truc k driving environment in the morn ing time is not bad enough for

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246 them to risk their physiological rhythm by doing the night shift. The drivers living with their family would also not want to miss spending time with their family by driving during the late night time. Truck drivers preference on truck driving ti mes of day varied by their earning methods, independence, companys primary business types, primary load types, hauling distance, percent of late delivery, number of working days per week, company fleet size, types of good carried, and truck types. 6.1.7 Relationships between Truck Drivers Ba ckgrounds and Their Perceptions on Truck Trip Quality Truck drivers perceptions on the app licability of each hypothetical truck LOS performance measure differed by various kinds of background characteristics. They include hauling distance, earning methods recruitment sources, truck company business types, primary load types, race, education level, truck type, current truck driving time of day, type of goods carried, etc. The most importa nt background features contribu ting to the perceptions were hauling distance, primary load types, earning me thods, and current truck driving time of day. These generally relate to whether a truck driver is a frequent freeway user or a city driver whose trips are mostly on arterials. Most frequent freeway users are long-haul drivers, TL drivers, drivers getting paid by the mile, or drivers trave ling during non-peak hours (e.g., late night time). These drivers showed more concern for freeway or two-lane highway hypothetical performance measures such as a consistently good ride qual ity, ease of obtaining useful travel conditions information, ease of driving at or above the posted speed limit, and probability of being passed or followed by faster vehicles. On th e other hand, most city drivers are short-haul drivers, LTL drivers, drivers getting paid by th e hour, or drivers trave ling during peak hours.

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247 They were less sensitive to those freeway or two-lane highway performance measures, but indicated more concerns with ease of U-turn ing maneuvers at inters ections along arterials. 6.1.8 Overall Effectiveness of Research Approach The focus group sessions were very effective in eliciting a number of factors affecting the perceptions of the trucking community on truck tr ip quality. Truck drivers had much to say, and they offered a lot of insight into truck drivi ng operations. Good input was obtained from the truck company managers, but with only one sess ion and three managers it was not as productive as the driver sessions. Transcripts from the a udio-recording of focus group discussions greatly facilitated the efficient summary of the studies. Survey studies were efficient in obtaining ge neral perceptions of the trucking community, but there was some difficulty co llecting survey data. Many surv ey respondents provided their valuable perceptions on the relative importance of each traffic, roadway, and/or control factor on truck trip quality. However, some respondents di d not complete the surveys, or did not answer the questions correctly, especially for the sections asking for th eir perceptions. Truck driver surveys during the Florida Truck Driving Compet ition (FTDC) were effective in obtaining a good number of surveys at one time (a total of 148 surveys), but many respondents completed only parts of the surveys due to the length of the survey (6 pages) and some drivers were probably not very willing to complete it, even though they were aske d to. In-field truck company manager surveys during the FTDC event were not very effici ent, yielding about 1.5 surveys per hour. Postage-paid mail back truc k driver surveys distributed at agricultural inspection stations were reasona bly efficient (overall response rate of 7.8%); however, these surveys are generally biased toward long-haul truck drivers, as they are mu ch more likely to have to stop at these stations (92% of the survey respondents were l ong-haul drivers). Postage-paid mail back truck company manager surveys, ba sed on the FTA membership directory were

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248 reasonably efficient (a response rate of 9%); but follow-up phone contacts did not help to improve the response rate. 6.2 Recommendations A major objective of this study was to identify appropriate se rvice measures to use for truck LOS determination. The intent is that the results of this study would lay the groundwork for a future study, or studies, to develop quant itative LOS estimation models based on these identified service measures. This secti on mainly provides recommendations on how to effectively develop these LOS models for each of the roadway facility types addressed in this study. Some considerations for transportation service improvement priority for the trucking community, as well as the trucking commun ity survey methods, are also offered. 6.2.1 Truck LOS Estimation Model Development This section will describe some specific research approaches that might be most effective or applicable for developing quantitative LOS m odels for each facility type, for the preferred service measures identified in this study. 6.2.1.1 Truck LOS on freeways Consistency in travel speed was identified as on e of the primary determinants of truck trip quality on a freeway. A previous study by Kim, et al. (2003) investigated the use of acceleration noise (i.e., standard deviation of acceleration) as a potential service measure. This study utilized simulation to develop a model for acceleration no ise based upon other easily measured traffic flow parameters, such as volume and speed. Th is work was confined to passenger cars, but it could easily be extended to trucks with some adjustments. Since many commercial trucks are now equipped with Global Positioning Systems (G PS), it may also be possible to obtain these data from truck companies, eliminating the need for on-the-road or driving simulator experiments. These data could then be used in combination with othe r traffic stream field

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249 measurements to develop a model based on fiel d data, or at a minimum validate the models developed from the simulation process. The AASHO (American Association of Stat e Highway Officials, now AASHTO) Road Test (Highway Research Board, 1962) developed the Present Serviceability Rating (PSR) to investigate users perceptions of pavement quality. A panel of raters actually rode in an automobile over a number of pavement sections a nd rated their ride experience on a scale from 0 to 5 (0 being Essentially Impassible and 5 be ing Excellent). It was found that about 95 percent of the information about the serviceabil ity of a pavement is contributed by the roughness of the surface profile. The Present Serviceabil ity Index (PSI) was deve loped as a function of multiple measures of pavement roughness (e.g., mean slope variance, surface rutting, surface cracking, and surface patching) using a multiple regression statistical technique. The AASHO Road Test rater opinions were ba sed on car ride dynamics, so it is unclear whether the levels of PSI are applicable to the cases of large trucks. The International Roughness I ndex (IRI) was developed by the World Bank in the 1980s (Sayers, et al., 1986) to establish uniformity of the physical measurement of roughness. The IRI is based on a filtered ratio (referred to as the average rectified slope) of a standard vehicles accumulated suspension motion (meters) divided by the distance traveled by the vehicle during the measurement (kilometers). That is, the IR I measures pavement roughness in terms of the number of meters per kilometer that a laser, mounted in a specialized van, jumps as it is driven across the interstate and expressway system. Thus, commonly recommended measurement units are m/km and the lower the IRI number, the sm oother the ride. The IRI has been shown to correlate well with vertical passe nger acceleration (a measure of ride quality) and tire load (a measure of controllability and safety). The IRI is now considered the international standard for

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250 comparing roughness measurements and widely used by Federal Highway Administration (FHWA) as a means of determining rehabilita tion needs and resource allocation for pavement condition. Many previous studies have show n that the users perceptions of pavement quality largely depend on the roughness of the roadway. Hveem (1960) stated that there is no doubt that mankind has long thought of road smoothness or roughness as being synonymous with pleasant or unpleasant, but the effect s of a given degree of roughness vary with the speed and characteristics of the vehicle a nd tolerance of the vehi cle driver or passenger. However, the relationship between physical m easurements of pavement roughness and the users perceptions of ride quality has not been adequately modele d. In studies by Shafizadeh and Mannering (2003 and 2006), selected participants were placed in real-world driving conditions and asked to rank the roughness of specific roadway segments. The st udy concluded that the users perceptions of roadway roughness is mostly cons istent with IRI and PSR, and al so correlated with type and speed of vehicle used, individuals age and gender, and interior vehicle noise level. However, no driver of a large truck pa rticipated in this study. There has been no research conduc ted to specifically investigate the re lationships between the perceptions of truck driver s on ride quality and the measur es of roadway roughness (e.g., IRI or PSI). However, given that many previous stud ies verified that user perceptions of roadway roughness (i.e., ride quality) can us ually be adequately addresse d by the measures of roadway roughness, the IRI and/or the PSI could be potentia lly referenced to estimate truck drivers satisfaction of pavement quality until experiments with truck drivers can be conducted. In-field driving experiments with a re presentative sample of truck drivers are required for the

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251 development of accurate models to estimate truck drivers perceptions of pavement quality from the measure of roadway roughness. It may be possible to ask truck drivers who ju st reach the destinati on about the quality of the trip and pavement condition a nd obtain the truck operational data from their truck companies. This may facilitate the development of a freew ay truck LOS model combining acceleration noise and pavement condition. However, this also requ ires research team to develop and coordinate the experiments with the truck companies beforehand. 6.2.1.2 Truck LOS on arterials Due to the number of variables identified in this study that impact truck trip quality on arterials, a composite model is necessary. This type of model development for arterials has recently been attempted, although specific to pass enger vehicles, by Flannery, et al. (2005) and Pecheux, et al. (2004), as well as in the cu rrently ongoing NCHRP 3-70 (Multimodal Arterial Level of Service) project. These previous st udies have utilized in-f ield driving and video simulation data collection methods One major challenge with the video simulation approach is being able to get accurate input on pavement c ondition. Hall, et al. (2004) incorporated the Kentucky Transportation Cabinet (KYTC)s inventory of pavement rideability ratings to evaluate large truck access routes between intermodal or other truck-traffic-ge nerating sites to the National Highway System (NHS) (i.e., connectors) However, the evaluation process was not based on the perceptions of truck drivers, and thus the level of contribution of pavement condition to truck trip quality on an access ro ute was arbitrary. If information about the importance level of pavement condition by truck type and travel speed on truck trip quality on arterials is available, it may be possible to develop a model combining pavement quality using the IRI or the PSI and other performance meas ures from video simulation data collection. However, in-field experiments are required to develop an accurate truck LOS model for arterials.

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252 It also may be possible for researchers to ask truc k drivers about their perceptions on trip quality and other measures at certain pl aces along their route (e.g., fuel station), while general traffic stream field measurements are collected at the same time. 6.2.1.3 Truck LOS on two-lane highways Three performance measures were identified that adequately address the truck trip quality on two-lane highways: Percent-Time-BeingFollowed (PTBF) and Percent-Time-SpentFollowing (PTSF); pavement condition; and trav el lane and shoulder width. PTBF and PTSF measures could be developed from microscopic traffic simulation or field observation. The measures may be determined by headway thres holds to define in which case a truck is considered to be following or being followed. Travel lane and shoulde r width information is available from the FDOT Roadway Characteri stics Inventory (RCI) database. Again, if information about importance level of pavement condition according to truck type and speed on truck trip quality on two-lane highways is ava ilable, it may be possible to develop a model combining pavement quality using the IRI or the PSI and PTBF, PTSF from video simulation, and lane and shoulder width from the RCI. 6.2.2 Transportation Service Improveme nt for the Trucking Community Given that urban arterials were identified as th e facility type most in need of improvement, access roads from the Florida Intrastate Highway System (FIHS) to hub facilities should be primarily addressed in the deve lopment of transportation improvement programs for the trucking community. For prioritizing transportation improv ement projects within each type of roadway facility, the use of an Improvement Priority Score (IPS) is recommended. So me general issues found to be important to the trucking comm unity from this study are as follows: The motoring publics attitude and knowledge about trucks is the primary concern for the trucking community, especially for truck driv ers. There is a n eed to publicize the importance of truck operations in the state of Florida through mass media to improve the

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253 motoring publics attitude about trucks on th e road. The motoring public also needs more education about how to mix with large trucks sa fely in a traffic stream. It may be possible to emphasize this topic in drivers licens e exams and some mandatory classes may be scheduled for the drivers to re juvenate their concerns on this topic when they obtain or renew their licenses. More emphasis on this to pic in beginning driver s education classes (such as in high schools) s hould also be considered. Implementation of truck travel restriction m easures requires a lot of caution because it significantly deteriorates the truck drivers trip satisfaction. To obtain greater acceptance of these measures from the truck drivers, th ey may need more education as to its overall benefits, as they appare ntly not aware of them. Shoulder width and condition on two-lane high ways, and curb radii on arterials are two major physical roadway concerns of the trucki ng community that need to be addressed by transportation service providers. Pavement condition is definitely important for truck trip quality, but most truck drivers are fairly satisfied with the pavement condition on Florida roadways. Thus, it is reasonable not to focus on this factor too much for tr ansportation improvement programs for trucks. However, it should definitely be incor porated into LOS models if possible. Access management (e.g., median closing) s hould be carefully planned to eliminate unnecessary turning movements of trucks due to the high risks associated with the maneuvers. The trucking community generally believes that night-time delivery is beneficial for them to a considerable degree, but it is not widely performed due to the lack of benefits to the other stakeholders (e.g., receiver s, customers, shippers). It is worth looking for ways to offer motivation or benefits to perform the ni ght-time delivery to the other stakeholders for more efficient truck operations. Frequency and security of rest areas and truck parking spac es (especially for overnight parking) are one of the important issues for long-haul truck drivers. These issues should be addressed adequately by transportation serv ice providers for a safe and convenient truck trip. 6.2.3 Trucking Community Surveys This study mainly used survey methods to i nvestigate the perceptions and opinions of the trucking community. Based on the experience, th e following strategies are recommended for future survey studies of the trucking community: Total length of a survey should be not more than 4 pages. The length of the sections asking for the perceptions of the trucking co mmunity should be not more than 3 pages.

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254 Survey questions should be easily understood and completed within ~15 minutes. If a pilot test of the questions is possible, it will help determine the proper length and complexity of the survey. Note that the pi lot test needs to be conducted with the same audience. With the truck driving audience, there appeared to be considerable variance in the ability of drivers to understand all of the questions, as well their dilig ence in filling out the survey (this was more so the case for the FTDC survey effort). Thus, in a future survey effort, to help determine the overall validity of the surv ey responses, the inclusion of a couple of dummy question should be considered. Th ese dummy questions would be questions that any respondent can ea sily answer correctly. Survey data collection as a part of an event (such as the FTDC) has advantages in collecting a large sample at one time, but may produce a considerable number of invalid surveys in that some participants will not fill it out diligently if they are generally unwilling to participate. Table 6-1. Truck Trip Quality of Service Determinants on Freeways Freeway QOS Truck Drivers Perceptions Truck Company Managers Perceptions Truck Trip QOS Determinants 1. Other Drivers Behavior 2. Pavement Condition 3. Level of Congestion 4. Truck Travel Restrictions 5. Construction Activities 1. Level of Congestion 2. Construction Activities 3. Alternative Routes 4. Other Drivers Behavior 5. Number of Lanes 6. Pavement Condition Primary Concern Driving Comfort (Physical and Psychological) Travel Time and Driving Safety Potential Service Measure(s) Speed Variance (or Acceleration Noise) and Pavement Condition

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255 Table 6-2. Truck Trip Quality of Service Determinants on Arterials Arterial QOS Truck Drivers Perceptions Truck Company Managers Perceptions* Truck Trip QOS Determinants 1. Other Drivers Behavior 2. Curb Radii for Right Turns 3. Level of Congestion 4. Traffic Signal Coordination 5. Pavement Condition 6. Construction Activities 1. Construction Activities 2. Pavement Condition 3. Level of Congestion 4. Protected Left Turn Signals 5. Curb Radii for Right Turns 6. Other Drivers Behavior 7. Traffic Signal Coordination Primary Concern Maneuverability Ma neuverability and Travel Time Potential Service Measure(s) Multiple Factors (Pavement Condition, Leftor Right-Turning Maneuvers, Speed Variance, Traffic Density) The sample size for the truck company manage r responses on Truck Trip QOS Determinants on arterials was low (6); thus, th e reliability of these responses is questionable. However, the sample size for their responses on the importance levels of Pote ntial Service Measure(s) was acceptable (33). Table 6-3. Truck Trip Quality of Serv ice Determinants on Two-Lane Highways Two-Lane Highway QOS Truck Drivers Perceptions Truck Company Managers Perceptions* Truck Trip QOS Determinants 1. Other Drivers Behavior 2. Pavement Condition 3. Shoulder Width and Condition 4. Lighting Conditions at Night 5. Construction Activities 6. Level of Congestion 7. Frequency of Passing Lanes 1. Roadway Striping Condition 2. Level of Congestion 3. Pavement Condition 4. Other Drivers Behavior 5. Shoulder Width and Condition 6. Construction Activities 7. Sight Distance at Horizontal Curvatures Primary Concern Driving Comfort (Physical and Psychological) and Travel Safety Travel Safety and Travel Time Potential Service Measure(s) Percent-Time-Being-Followed (PTBF), Percent-Time-Spent-Following (PTSF), Lane and Shoulder Width, and Pavement Condition The sample size for the truck company manage r responses on Truck Trip QOS Determinants on two-lane highways was low (4) ; thus, the reliability of these responses is questionable. However, the sample size for their responses on the importance levels of Potential Service Measures was acceptable (34).

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256 Table 6-4. Top Six Factors in the Need for th e Improvement on Each Roadway Type for Trucks Freeways Urban Arterials Two-Lane Highways 1. Improve Passenger Car Drivers Knowledge about Truck Driving Characteristics 2. Improve Passenger Car Drivers Road Etiquette 3. Remove Truck Lane Restrictions 4. Reduce Traffic Congestion 5. Do not implement lower Truck Speed Limit 6. Increase Governed Truck Speed Limit 1. Improve Passenger Car Drivers Road Etiquette 2. Improve Passenger Car Drivers Knowledge about Truck Driving Characteristics 3. Reduce Traffic Congestion 4. Improve Traffic Signal Coordination 5. Increase Curb Radii for Right Turns 6. Frequency and Timing of Construction Activities 1. Improve Passenger Car Drivers Knowledge about Truck Driving Characteristics 2. Improve Passenger Car Drivers Road Etiquette 3. Improve Shoulder Width and Condition 4. Frequency and Timing of Construction Activities 5. Improve Lighting Conditions at Night 6. Reduce Traffic Congestion

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257 APPENDIX A COOPERATION REQUEST LETTER SENT TO FTA

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259 APPENDIX B FOCUS GROUP INSTRUCTION

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261 APPENDIX C GUIDELINES FOR FOCUS GROUP PARTIC IPANT SELECTION SENT TO FTA

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265 APPENDIX D FOCUS GROUP MODERATORS GUIDE The purpose of this document is to provide an overv iew of what is expected in the oncoming focus group meetings of truck drivers and truck company ma nagers to the moderator, who will facilitate the discussions. The document includes the items or issues that should be explained or brought up in the meetings by the moderator so that the discussions can be organized to produce useful results for this research and the participants are also motivated to l ead the discussions in the meetings. The contents of this document are listed in a chronological order for each focus group meeting session. 1) Sign-in Form and Informed Consent Form Distribution (expected time frame = 5 minutes) Each participant is asked to provide his/her na me on the Sign-in Form, and fill out the Informed Consent Form before taking part in the focus group m eeting. The Informed Consent Form is required for the Institutional Review Board at the University of Fl orida (UFIRB) to ensure that the participants were aware of the risks and benefits of pa rticipating in this study and that th ey voluntarily agreed to participate in it 2) Welcome and Introductions (expected time frame = 5 minutes) Introduction of moderator and assistant(s). Express appreciation to participants for agreeing to participate and share their valuable experience and kn owledge about trucks operati ons in Florida. Selfintroductions of participants. 3) Overview of Study Backgrou nd, Objectives, and Benefits (expected time frame = 5 minutes) The background, objectives, and potential benefits of this study will be briefly described by the moderator. A separate hand-out addressing these issues will also be provided to each participant (Refer to Appendix E). 4) Focus Group Participants Background Survey (expected time frame = 10 minutes) Oneor two-page hand-outs to each truck driver will be distributed to gather information about participants personal characteristics and job duties, as well as truck, delivery, and cargo characteristics (Refer to Appendix F). Truck company operato rs/managers will be asked about business operation

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266 characteristics, fleet size, types of cargo hauled, primary origin and destination, etc (Refer to Appendix G). The moderator is required to ask the participan ts to fill out the forms and hand them in. 5) Explanation of Format and Scope of the Focus Group Session (expected time frame = 5 minutes) To obtain the perceptions or opinions of the par ticipants more efficiently and productively, the format and the scope of the focus group session will be explained to the participants. This will be included in the hand-out to each partic ipant as well (Refer to Appendix E). 6) Focus Group Questions (total expected time frame = 1 hour and 30 minutes) During the course of each focus group sessi on, several open-ended or subject-specific questions will be presented by the moderator to the participants. The participants will then discuss each topic amongst themselves and with th e moderator. A same set of issues will be introduced in both driver and manager focus groups, but their corresponding questions will be differently phrased for some issues. Each question should be written on a white board, or presented on an electronic slide, or the like, for all participants to easily see. The selected issues and questions are listed below in chronological order with the approxima te time assigned for discussion of each subject within a two-hour focus group meeting. Additional questions may also be asked about why the commented factors are important or the participants experience related to the factors. Truck Route and Departure Time Selecti on (expected time frame = 15 minutes) Who is responsible for selecting a travel route and departure time for your delivery? When selecting a travel route and departur e time for your delivery, what factors do you consider and what is their re lative overall significance? Transportation Service Improvement Prioriti es for Trucking Community (expected time frame = 10 minutes)

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267 What types of facilities (freeways, multi-lane highways, two-lane highways, or arterials) would you emphasize most to improve roadway and traffic conditions for better truck operations in the State of Florida? If youre in charge of policy at FDOT, what would be your top priorities for improving truck tip quality/travel conditions for commercial trucks? Factors affecting Truck Trip Quality (expected time frame = 50 minutes) What is important for the quality of a truck trip on freeways and how significant is each factor to your overall per ception of trip quality? What is important for the quality of a truck trip on multi-lane highways and how significant is each factor to your ove rall perception of trip quality? What is important for the quality of a truck trip on two-lane highways and how significant is each factor to your overall perception of trip quality? What is important for the quality of a truck trip on urban arterials and how significant is each factor to your overall perception of trip quality? What is important for the quality of a truck trip on hub facilities and how significant is each factor to your overall perception of trip quality? Truck Delivery Schedule Reliability (expected time frame = 15 minutes) How often has your delivery been late? What do you do to avoid a late delivery? What are the typical consequences for you/your company when a delivery is late? What do you think most affects a truck drivers ability to reach his/ her destination by the scheduled time? Ending questions Is there anything else important about truck operations on Floridas state roadway systems that you would like to mention? Total expected time = 2 hours

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268 APPENDIX E FOCUS GROUP PARTICIPANTS BA CKGROUND SURVEY RESULTS Table E-1. 1st Truck Driver Focus Group Participants Background Survey Results (November, 15th, 2005) Participants Backgrounds Truck Driver 1 Truck Driver 2 Truc k Driver 3 Truck Driver 4 Truck Driver 5 Company Name Con-way Southern Express Publix Supermarkets Watkins Motor Lines FedEx Ground Orlando Watkins Motor Lines Gender Male Male Male Male Male Age 40 49 40 49 50 59 50 59 40 49 Race Caucasian Caucasian Cau casian Caucasian Caucasian Truck Driving Job Experience 21 years 18 years 30 years 30 years 29 years Working Days Per Week 5 days 4 days 5 days 5 days 5 days Working Hours Per Day 10 hours 8 hours 9 hours 10 hours 9.5 hours Number of Nights Away From Home 0 night 0 night 0 night 0 night 0 night Earning Method(s) By the mile, or hour By the mile, hour, or salary By the mile By the mile By the mile, or hour Annual Income by Truck Driving $50,000 74,999 $50,000 74,999 $75,000 or more $75,000 or more $50,000 74,999 Company Type For-hire Privat e For-hire For-hire For-hire Company Fleet Size 10,000 trucks 600 trucks 4,000 trucks 5,000 trucks 4,000 trucks Primary Load Type LTL TL LTL Both TL & LTL LTL Geographic Coverage of Truck Driving Entire Florida and Other States Florida and Other States Part of Florida State Florida and Other States Florida and Other States One-way Delivery Distance 265 miles 160 miles 244 miles 280 miles 249 miles Route and Departure Time Selection Transportation, or Logistics Managers Myself Transportation, or Logistics Managers Transportation, or Logistics Managers Transportation, or Logistics Managers Truck Kind(s) Twin Trailer, 4-Axle Tractor Semitrailer 5-Axle Tractor Semitrailer 5-Axle Tractor Semitrailer, Rocky Mountain Double, Turnpike Double Twin Trailer, 3-, 4-, 5-Axle Tractor Semitrailer Twin Trailer, 4-Axle Tractor Semitrailer

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269 Table E-1. Continued Types of Goods Anything except livestock Mainly food Anything except livestock, coal or petroleum Household goods or stationary, auto parts, textiles, metals, manufactured goods, paper and allied products, furniture, wood products Anything except vehicles, livestock, coal or petroleum, waste and scrap, equipment, stone, clay, glass, and concrete products Table E-2. 1st Truck Company Manager Focus Group Pa rticipants Background Survey Results (November, 17th, 2005) Participants Backgrounds Truck Company Manager 1 Truck Company Manager 2 Truck Company Manager 3 Company Name Publix Supermarkets CTL Distribution Commercial Carrier Corp Gender Male Male Male Age 40 49 30 39 30 39 Truck Company Manager Job Experience 25 years 10 years 5 years Annual Income as a Manager $70,000 99,999 $70,000 99,999 $50,000 69,999 Company Type Private For-hire For-hire Company Fleet Size 850 trucks 432 trucks 1,200 trucks Primary Load Type TL TL TL Geographic Coverage of Trucking Business Florida and Other States Florida and Other States Florida and Other States Distribution of Truck Driving Distance of the Company Local: 50% Short-haul: 50% Long-haul: 0% Local: 50% Short-haul: 30% Long-haul: 20% Local: 10% Short-haul: 70% Long-haul: 20% Route and Departure Time Selection Facility Dispatcher Myself Myself Types of Goods Food, household goods or stationary Chemicals and allied products, coal or petroleum, hazardous materials Food, paper and allied products, stone, clay, glass, and concrete products Truck Kind(s) 5-Axle Tractor Semitrailer Twin Trailer, 5-Axle Tractor Semitrailer 5-Axle Tractor Semitrailer

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270 Table E-3. 2nd Truck Driver Focus Group Participants Background Survey Results (December, 8th, 2005) Participants Backgrounds Truck Driver 1 Truck Driver 2 Truck Driver 3 Truck Driver 4 Company Name Publix Supermarkets FedEx Ground Overnite Transportation (a UPS) TDT Gender Male Male Male Male Age 40 49 40 49 30 39 50 59 Race Caucasian Caucasian Caucasian Caucasian Truck Driving Job Experience 16 years 29 years 18 years 30 years Working Days Per Week 5 days 5 days 5 days 5 days Working Hours Per Day 12 hours 8 hours 7 hours 11 hours Number of Nights Away From Home 0 night 0 night 3 night 5 night Earning Method(s) By the mile, type of goods, or loading amount By the mile By the mile, or hour By the mile, or the drop Annual Income by Truck Driving $50,000 74,999 $75,000 or more $50,000 74,999 $25,000 34,999 Company Type Private For-hire For-hire For-hire Company Fleet Size 1,300 trucks 5,000 trucks 7,500 trucks or more 250 trucks Primary Load Type TL LTL LTL Both TL & LTL Geographic Coverage of Truck Driving Part of Florida State Florida and Other States Florida and Other States Florida and Other States One-way Delivery Distance 150 miles 1,112 miles 400 miles 1,350 miles Route and Departure Time Selection Transportation, or Logistics Managers Transportation, or Logistics Managers Transportation, or Logistics Managers Myself Types of Goods Food, household goods or stationary Small Packages Anything but livestock, coal or petroleum, vehicles, waste and scrap, and equipment Grains/Feed, household goods or stationary, metals, manufactured goods, wood products except furniture

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271 Table E-3. Continued Truck Kind(s) 5-Axle Tractor Semitrailer Twin Trailer or Doubles Twin Trailer, 4-Axle Tractor Semitrailer 5-Axle Tractor Semitrailer

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272 APPENDIX F TRUCK DRIVER FOCUS GROUP BACKGROUND SURVEY FORM

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273

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274

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275 APPENDIX G TRUCK COMPANY MANAGER FOCUS GROUP BACKGROUND SURVEY FORM

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276

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277

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278 APPENDIX H TRUCK DRIVER SURVEY FORM

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283

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284

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285 APPENDIX I TRUCK COMPANY MANAGER SURVEY FORM

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291

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292 APPENDIX J IMPROVEMENT PRIORITY SCORE (IPS) Table J-1. Improvement Priority Scores (IPS) Case Ranking RIS (1) RSS (1) IPS ( 42 +42) 1 7 1 42 2 6 1 30 3 5 1 20 4 7 2 17.5 5 4 1 12 5 6 2 12 7 7 3 9.33 8 5 2 7.5 9 3 1 6 9 6 3 6 11 7 4 5.25 12 4 2 4 13 5 3 3.33 14 6 4 3 15 7 5 2.8 16 2 1 2 17 3 2 1.5 18 4 3 1.33 19 5 4 1.25 20 6 5 1.2 21 7 6 1.17 22 1 1 0 22 2 2 0 22 3 3 0 22 4 4 0 22 5 5 0 22 6 6 0 22 7 7 0 29 6 7 .17 30 5 6 .2 31 4 5 .25 32 3 4 .33 33 2 3 .5 34 1 2 35 5 7 .8

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293 Table J-1. Continued 36 4 6 37 3 5 .33 38 2 4 39 4 7 .25 40 1 3 40 3 6 42 2 5 .5 43 3 7 .33 44 1 4 44 2 6 46 2 7 .5 47 1 5 48 1 6 49 1 7

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294 APPENDIX K POSTAGE-PAID TRUCK DRIVER SURVEY FORM

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298 APPENDIX L POSTAGE-PAID TRUCK COMPAN Y MANAGER SURVEY FORM

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302 APPENDIX M POSTAGE-PAID MANAGER SURVEY COVER LETTER

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303

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304 APPENDIX N SURVEY DATA FILTERING CRITERIA This document describes th e reasons why the survey da ta reduction strategy was necessary and how it was applied to survey resp onses for each section of the survey. For both driver and manager surveys, the identical survey da ta filtering criteria were used to determine the validity of the survey responses. Only the valid survey responses were used for data analysis. Section 1: Background of the Respondents Almost all the respondents completed the pa rticipant background section of the survey. A few participants did not fill out all the questions. It was required to check the validity of the survey responses for several questions such as percen t of empty trips, percent of late trips, etc. For instance, several truck driver participants in dicated that over 50 percent of their trips are empty, but those responses are not reasonably possi ble. Thus, they were excluded for survey data analyses. Section 2: Relative importance and sa tisfaction of each factor Relative importance and satisfaction of each fact or on each roadway type was asked in an interval rating scale. It was witnessed that many respondents did not answer all the questions in the section, or did not pay e nough attention to complete the section as directed. Some respondents indicated that all or most of the factors are equally important or satisfactory, not trying to give their opinions about the relative im portance or satisfaction of each factor among all the listed factors. Some other respondents only completed either the relative importance or the relative satisfaction section, or did not distinguish the relative importance scores from satisfaction scores, or presented the same scores for both the relative importance and satisfaction of all or most of the factors. Another group of respondents did not give relative importance or satisfaction scores for all the listed factors, creating some missing data. Considering these

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305 observations, following data screening criteria was used to distinguish valid survey responses for data analysis: Validity of survey responses for this section should be determined for each column of each roadway facility (e.g., Relative Importance on Freeways, Relative Satisfaction on 2-Lane Highways, etc). When the relative importance scores (or satisfaction scores) of all the listed factors present a small variance, the column data should not be considered for survey data analyses. At least, scores of 4 or more fact ors should be different from the mode of all the scores in that column. When there are more than 2 missing scores in a column, the data for that column should be considered invalid. When the relative importance score is identical to the satisfaction score for most of the listed factors, both column data should be di scarded. At least 4 im portance scores should be different from their corresponding satisfa ction scores for both column data to be determined to be valid. When there is any score(s) presented in oth er section without indi cating self-identifying factor, the data for that whol e column should be eliminated. Section 3: Relative importance of each category of factors Relative importance of each ca tegory of factors on each roadway type was asked in a ranking scale. A significant portion (more than 70%) of the particip ants did not respond to these questions. Some respondents answered them in an interval-rating scale. It is not clear whether they really regarded those ranki ng-scale questions as interval -rating scale questions. Thus, following criteria was used to pick out the valid survey responses for data analysis: Validity of survey responses for this sect ion should be determined for each question. Only the responses in the ranking scale (i.e., ranking the items from 1 to 4) should be considered valid. Responses with any missing data should be discarded.

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306 Section 4: Applicability of single performance measure Applicability of single performance measure to determine quality of a truck trip was asked in an interval-rating scale. Most participants completed this survey section as directed. However, there still were some respondents w ho did not answer the questions. Some other respondents indicated that all the listed performance measures are equally applicable. Following criteria were used to discern the valid survey responses for data analysis: Validity of survey responses for this section should be determined for each roadway type. When there are 2 or more missing scores within a roadway type, all da ta for that roadway type are considered invalid. When the same score is given to all factors fo r 2 or more roadway types, the data in the whole section should be eliminated. Section 5: Relative improvement need of each roadway facility type The relative improvement need of each roadwa y facility type was questioned in a ranking scale. Most participants did not respond to this question. Some respondents answered it in an interval-rating scale. It is not clear whether they really rega rded those ranking-scale questions as interval-rating scale questions. Valid surveys were determined by the criteria used in the section 3.

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307 LIST OF REFERENCES Alexander David G. and Moore David. (2003). Winter road management project, Idaho Technology Transfer Center, in conjunction with American Trucking Associations Foundation, Inc., on-line survey website: , (October 11, 2006) American Trucking Associations Foundation, Inc. (2005). American trucking trends 2004, Alexandria, V.A. American Trucking Associat ions Foundation, Inc. (1997). Motor carrier and freight movement operational characteristics in the Baltimore region, prepared for the Baltimore Metropolitan Council, Alexandria, V.A. Bureau of Transportation Statistics. (2004). 2002 Commodity flow survey, Washington, D.C. Center for Public Policy. (2001). A survey of the opinions of the driving public and drivers of large trucks on road safety issues: road safety survey, Virginia Commonwealth University, V.A. Conover W. J. (2001). Practical nonparametric statistics, 3rd Edition, John Wiley & Sons. Crum Michael R., Morrow Paula C., Olsgard Pa tricia, and Roke Philip J. (2001). Truck driving environments and their influen ce on driver fatigue and crash rates. Transportation Research Record. 1779, Transportation Research Board, D.C., 125133. Dunnett Charles W. (1980). Pairwise multiple comparisons in the unequal variance case. Journal of the American Statistical Association, Vol.75, No. 372, 796800. Field, A. P. (2005). Discovering statistics using SPSS, 2nd Edition, Sage Publications, London. Finnegan Clare, Finlay Hugh, and Mahony Margar et O. (2004). An in itial assessment of the potential for urban distri bution centres in Dublin. 83rd Annual Meeting of Transportation Research Board, Washington, D.C. Finnegan Clare, Finlay Hugh, Mahony Margar et O., and Sullivan Donal O. (2005). Urban freight in Dublin city centre: survey analysis and strategy evaluation. 84th Annual Meeting of Transportation Research Board, Washington, D.C. Flannery Aimee, Wochinger Kathryn, and Mar tin Angela. (2005). Driver assessment of service quality on urban streets. Transportation Research Record. 1920, Transportation Research Board, Washington, D.C., 2531.

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308 Florida Department of Transportation. (2003). Floridas strategic intermodal system plan, Tallahassee, F.L. Golob Thomas F. and Regan Amelia C. (2002) The perceived usefulness of different source of traffic information to trucking operations. Transportation Research Part E Vol. 38, 97116. Greene William H. (2000). Econometric analysis, 4th Edition, Prentice Hall. Hair Joseph F., Black Bill, Babin Barry, Anderson Rolph E., and Tatham Ronald L. (2005). Multivariate data analysis, 6th Edition, Prentice Hall. Hall Fred, Wakefield Sarah, and Kaisy Ahmed Al. (2001). Freeway quality of service: what really matters to drivers and passengers? Transportation Research Record. 1776, Transportation Research Board, Washington, D.C., 1723. Hall Lisa Aultman, Hill Michael L., and Agent Ken. (2004). Methodology for evaluating large truck access to in termodal and other facilities. Transportation Research Record. 1999, Transportation Research Board, Washington, D.C., 6168. Highway Research Board. (1962). The AASHO road test: report 5pavement research, Highway Research Board Special Report 61-E. Hostovsky, Charles and Hall, Fred L. (2004). F reeway quality of service: perceptions from tractor-trailer drivers. Transportation Research Record. 1852, Transportation Research Board, Washington, D.C., 1925. Hveem, F. N. (1960). Devices for recording and eva luating pavement roughness, Highway Research Board, Research Record 264. Kim Jin-Tae, Courage Kenneth G., Was hburn Scott S., and Bonyani Gina. (2003). Framework for investigation of level-of -service criteria and threshold on rural freeways. Transportation Research Record. 1852, Transportation Research Board, Washington, D.C., 239245. Koehne Jodi, Mannering Fred, and Hallenbeck Mark. (1997). Analysis of trucker and motorist opinions toward truck-lane restrictions. Transportation Research Record. 1560, Transportation Research Board, Washington, D.C., 7383. Larson Paul D. and Poist Richard F. (2004). I mproving response rates to mail surveys: a research note. Transportation Journal, Vol. 43, No. 4, 6774. Lawson, Catherine T. and Riis, Anne-Elizabeth. (2001). Were really asking for it: using surveys to engage the freight community. Transportation Research Record. 1763, Transportation Research Board, Washington, D.C., 13.

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309 Lawson, Catherine T., Kirk, Alan R., Stra thman, James G., and Riis, Anne-Elizabeth. (2002). Making contact: developing an e ffective methodology to survey the freight community. 81st Annual Meeting of Transportation Research Board, Washington, D.C. Levene H. (1960). Robust tests for the equality of variance, Contributions to Probability and Statistics, Stanford University Pr ess, I. Olkin, eds. Palo Alto, C.A. Loudon William R. (2000). Surveys of freight shippers and carriers: lessons learned, DKS Associates, ITE District 6 Meeting, San Diego, C.A. Martilla, John A. and James, John C. (1977) Importance-performance analysis. Journal of Marketing, Vol. 41, No. 1, American Marke ting Association, 7779. McCord Keith. (2006). UPS drivers discouraged from making left turns, Local News, KSL TV reporting, KSL website: , (December 22, 2006) Morris, Anne G., Kornhauser, Alain L., a nd Kay, Mark J. (1998). Urban freight mobility: collection of data on time, costs, and barriers related to moving product into the central business district. Transportation Research Record. 1613, Transportation Research Board, Washington, D.C., 2732. Ott R. Lyman and Longnecker Michael T. (2006). An introduction to statistical methods and data analysis, 6th Edition, Duxbury Press. Pecheux, Kelly Klaver, Flannery Aimee, Wochinger Kathryn, Rephlo Jennifer, and Lappin Jane. (2004). Automobile drivers perceptions of service quality on urban streets. Transportation Research Record. 1883, Transportation Research Board, Washington, D.C., 167175. Regan, Amelia C. and Golob, Thomas F. ( 1999). Freight operators perceptions of congestion problems and the application of advanced technologi es: results from a 1998 survey of 1,200 companies operating in California. Transportation Journal, Vol. 38, No. 3, 5767. Sayers, M., Gillespie, T., and Queiroz, C. (1986). The international road roughness experiment: a basis for establishing a standard scale for road roughness measurements. Transportation Research Record. 1084, Transportation Research Board, Washington, D.C., 7685. Shafizadeh Kevin and Mannering Fred. ( 2003). Acceptability of pavement roughness on urban highways by driving public. Transportation Research Record. 1860, Transportation Research Board, Washington, D.C., 187193. Shafizadeh Kevin and Mannering Fred. (2006). Statistical m odeling of user perceptions of infrastructure condit ion: application to the case of highway roughness. ASCE Journal of Transportation Engineering, 133140.

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310 Southwestern Pennsylvania Regi onal Planning Commission. (1996). Freight transportation issues in southwestern Pe nnsylvania: a survey of manufacturers and transportation service providers, Pittsburgh, P.A. Statistical Package for the Social Sciences (SPSS) Inc. (2006). SPSS for Windows version 15.0, Statistics Software, Chicago, I.L. Transportation Research Board. (2000). Highway capacity manual (HCM), National Academy of Sciences, Washington, D.C. U.S. Department of Transportation a nd Federal Highway Administration. (2002). Freight analysis framework, Washington, D.C. Veras Jose Holguin, Polimeni John, Cruz Brenda, Xu Ning, List George, Nordstrom Jeana, Haddock Jorge. (2005). Off-peak freight deliveries: challenges and stakeholders perceptions. 84th Annual Meeting of Trans portation Research Board, Washington, D.C. Washburn Scott S., Kirschner David S. (2006). Rural freeway level of service based on traveler perception. 85th Annual Meeting of Transportation Research Board, Washington, D.C. Washburn Scott S. (2002). Multimodal quality of service, part I: truck le vel of service, prepared for the Florida Department of Transportation, Tallahassee, F.L., Project Final Report, BC 354-28. Washburn Scott S., Ramlackhan Kirby, and McLeod Douglas S. (2004). Quality of service perceptions by rura l freeway travelers: an explanatory analysis. 83rd Annual Meeting of Transportation Research Board, Washington, D.C. Zhang Li, and Prevedouros Panos D. (2005). U ser perceptions of signalized intersection level of service. 84th Annual Meeting of Transpo rtation Research Board, Washington, D.C.

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311 BIOGRAPHICAL SKETCH As an architect, architectural engineer, and transportation engineer, Byungkon (Cody) Ko has experienced a wide range of disciplines thro ughout his life. Inspired by his mother, who had been an architectural agent, he started studyi ng architectural design a nd engineering at Dankook University in Korea. At that time, he dealt w ith a number of architectura l projects as an active member of Artlier Compe, a study group intend ed to discover and materialize what people want and need through various contempor ary architectural design competitions. He continued his career at an architectural consulting company afte r graduation. He had worked as an architectural engineer and arch itect, managing construc tion sites, designing buildings, and drawing construction details. While he was gaining the real field experiences, he got interested in the origin, structure, and condition of traffic network that he realized architectural planning and infrastructure are stro ngly associated with. He also witnessed and inquired many serious traffic operation and mana gement problems consistently brought up in Seoul, Korea. All such concerns have grown furthe r to result in a motivation for him to get into the field of transportation. He earned his Masters and Ph.D degree in transporta tion engineering at the University of Florida. He had worked as a teaching assistan t for Transportation E ngineering course, and played a major role in conducting several FH WAor FDOT-funded tr ansportation research projects under the supervision of a couple of faculty members. He has mostly involved investigating the effectiveness of ITS safety count ermeasures such as flex ible traffic separators, variable message signs, pedestri an countdown signals on overall tra ffic safety and operation. He had been a key person in all aspects of the resear ch studies, from the inst allation of surveillance cameras in the field to the statistical analyses of the collected data. His dissertation for Ph.D. degree is about the identification of preferred performance measures for the assessment of truck

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312 level of service. The study required him to conduct focus group studies and surveys studies to elicit perceptions of Florida tr ucking community, truck drivers a nd truck company managers. He designed focus group and survey questionnaires and analyzed the data qualitatively and quantitatively to complete the study.