<%BANNER%>

Simulation-Based Approach to Estimate the Capacity of a Temporary Freeway Work Zone Lane Closure


PAGE 1

SIMULATION-BASED APPROACH TO ESTIMATE THE CAPACITY OF A TEMPORARY FREEWAY WORK ZONE LANE CLOSURE By DIEGO F. ARGUEA A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ENGINEERING UNIVERSITY OF FLORIDA 2006

PAGE 2

This document is dedicated to my parents and sister

PAGE 3

iii ACKNOWLEDGMENTS This section is written in appreciation of all thos e involved in the research process and the writing of this document. I thank the Univ ersity of Florida and the Department of Civil and Coastal Engineering for the opportunity t o participate in a Transportation Project and produce unique research. I thank my co mmittee, comprised of Dr. Lily Elefteriadou, Associate Professor, Committee Chair, and primary advisor; Dr. Scott Washburn, Associate Professor; and Dr. Sivaramakris hnan Srinivasan, Assistant Professor. I thank them for the advice, guidance, and feedback throughout the research and writing of the report. I would like also to th ank McTrans and those involved with software development. I thank them for providing i nsight into the details of the problems and solutions within the algorithms, fundamental to the effective use of the software packages. I thank the group of master’s and doctor al candidates that provided me with the technical support and guidance when needed. Fi nally, I thank my friends, family, and loved ones for the emotional support and encouragem ent throughout this endeavor.

PAGE 4

iv TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................... ................................................... ..........iii LIST OF TABLES..................................... ................................................... ....................vii LIST OF FIGURES.................................... ................................................... ..................viii ABSTRACT........................................... ................................................... .........................ix CHAPTER 1 INTRODUCTION..................................... ................................................... ................1 Background......................................... ................................................... .......................1 Problem Statement.................................. ................................................... ...................3 Research Objective................................. ................................................... ...................3 2 LITERATURE REVIEW................................ ................................................... ..........5 Work Zone Capacity in the Highway Capacity Manual ( HCM2000)..........................5 Current FDOT Methodology........................... ................................................... ..........7 Work Zone Capacity in the Literature............... ................................................... ........8 Work Zone Analysis Software........................ ................................................... .........13 Early and Late-Merge Maneuvers Upstream of a Work Z one...................................14 Other Freeway Work Zone Literature................. ................................................... ....18 Safety............................................. ................................................... ...................18 Traffic Diversion.................................. ................................................... ............19 Delay and Queuing.................................. ................................................... .........20 Summary and Conclusions............................ ................................................... ..........22 3 METHODOLOGY...................................... ................................................... ............24 Simulator Selection................................ ................................................... ..................25 Challenges with Previous Versions of CORSIM/FRESIM. .......................................26 Resolved Challenges with Current Versions of CORSIM /FRESIM..........................26 Modeling of Work Zones with CORSIM 5.1............. ................................................28 Operating Conditions............................... ................................................... .........28 Simulation of a Work Zone.......................... ................................................... ....29 Lane Closure Location.............................. ................................................... .......29

PAGE 5

v Results of Preliminary Analyses.................... ................................................... ..33 Simulation Scenarios............................... ................................................... ................33 Simulated Test Section............................. ................................................... ........33 Input Variables.................................... ................................................... .............34 Simulated Test Section Setup—Input Fixed Values.... .......................................37 Required Number of Simulation Runs................. ...............................................39 Total Number of Simulation Scenarios............... ................................................39 Output Values...................................... ................................................... ....................40 4 MODEL DEVELOPMENT................................ ................................................... .....41 Data Analysis...................................... ................................................... .....................41 2-to-1 Lane Closure Model Development.............. .............................................42 3-to-2 Lane Closure Model Development.............. .............................................54 3-to-1 Lane Closure Model Development.............. .............................................54 Final Models....................................... ................................................... .....................54 Variable Explanations and Example of Model Usage... ......................................55 Capacity Estimation Models for each Lane Closure Co nfiguration....................58 2-to-1 lane closure configuration.................. ...............................................58 3-to-2 lane closure configuration.................. ...............................................59 3-to-1 lane closure configuration.................. ...............................................60 Discussion of Results.............................. ................................................... .........61 Effects of Model Variables on Capacity............. .........................................62 Model Application by the FDOT...................... ...........................................65 5 CONCLUSIONS AND RECOMMENDATIONS.................. ...................................68 Conclusions........................................ ................................................... ......................68 Recommendations.................................... ................................................... ................70 Calibration........................................ ................................................... ................70 Future Research and Applications................... ................................................... .71 APPENDIX A MODEL DEVELOPMENT RELATIONSHIPS FOR A 3-to-2 LANE CLOSURE CONFIGURATION...................................... ................................................... ..........74 B MODEL DEVELOPMENT RELATIONSHIPS FOR A 3-TO-1 LANE CLOSURE CONFIGURATION.............................. ..................................................8 1 C SAMPLE OUTPUT FILES FROM CORSIM 5.1 AND CORSIM 6. 0.....................89 Sample Output from CORSIM 5.1...................... ................................................... ....89 Sample Output from CORSIM 6.0...................... ................................................... ....96

PAGE 6

vi D STATISTICA OUTPUT SCREENSHOTS FOR EACH LANE CLOSU RE CONFIGURATION...................................... ................................................... ........105 2-to-1 Lane Closure Configuration.................. ................................................... ......105 3-to-2 Lane Closure Configuration.................. ................................................... ......106 3-to-1 Lane Closure Configuration.................. ................................................... ......106 E MODEL USAGE EXAMPLE: SAMPLE CAPACITY CALCULATIONS FOR EACH LANE CLOSURE CONFIGURATION.................... ...................................107 F TRAFVU SCREENSHOTS FOR EACH LANE CLOSURE CONFIGURATION...................................... ................................................... ........112 LIST OF REFERENCES................................. ................................................... .............114 BIOGRAPHICAL SKETCH................................ ................................................... ........117

PAGE 7

vii LIST OF TABLES Table page 3 1. Delay Values for Combinations of Lane Closur es and Lane Distributions (Ten Simulation Runs)................................... ................................................... ................30 3 2. Average Speeds per Vehicle (Ten Simulation R uns)............................................... 31 3 3. Average Speed Values for Different Combinati ons of Lane Closures and Lane Distributions...................................... ................................................... ....................32 3 4. Effects of Rubbernecking Factor on Capacity through Work Zone Lane Closure..37 3 5. Variation of Input Parameters............... ................................................... .................40

PAGE 8

viii LIST OF FIGURES Figure page 3 1. Sketch of the freeway network used in data c ollection.......................................... ..33 3 2. Relationship between work zone flow and work zone length..................................35 4 1. Relationship between work zone capacity and upstream warning sign distance.....43 4 2. Relationship between work zone capacity and truck presence in traffic stream......44 4 3. Relationship between work zone capacity and truck presence in traffic stream......44 4 4. Relationship between work zone capacity and lane changes in link (6,7)...............45 4 5. Relationship between the number of lane chan ges in link (6,7) and the length of link (6,7)......................................... ................................................... .......................46 4 6. Relationship between work zone capacity and the average speed per vehicle in lanes one and two of link (5,6).................... ................................................... ..........47 4 7. Relationship between work zone capacity and the average speed per vehicle in lanes one and two of link (6,7).................... ................................................... ..........48 4 8. Relationship between work zone capacity and the vehicular distributions on lane one of all links................................... ................................................... ....................49 4 9. Relationship between vehicular lane distribu tions in lanes one and two of link (6,7) and the location of the upstream warning sign ................................................50 4 10. Relationship between work zone capacity and the interaction of lane distributions in link (6,7) and upstream sign dista nce...........................................51 4 11. Relationship between the work zone capacity and the interaction of the speeds in lane 1 of link (6,7) and the location of the ups tream warning sign...................52 4 12. Relationship between the speeds in lane 1 a nd lane 2 of link (5,6)........................53 4 13. Relationship between the speeds in lane 1 a nd lane 2 of link (6,7)........................53

PAGE 9

ix Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Engineerin g SIMULATION-BASED APPROACH TO ESTIMATE THE CAPACITY OF A TEMPORARY FREEWAY WORK ZONE LANE CLOSURE By Diego F. Arguea August 2006 Chair: Lily Elefteriadou Major Department: Civil and Coastal Engineering The Florida Department of Transportation (FDOT) is interested in updating its methodologies for estimating capacities on freeway work zones in Florida. The current methods have not been modified since 1995, and the FDOT is particularly interested in new ways to facilitate the scheduling and managing of lane closures. This thesis proposes new simulation-based models for estimating the capacity of a temporary freeway work zone lane closure. Some of the factor s considered in model development include the location of the upstream warning sign, the presence of trucks, the presence of law enforcement and/or heavy equipment, and the len gth of the work zone. In addition to these inputs, the average speeds per vehicle and th e vehicular lane distributions for specific network links were considered in model dev elopment. A large matrix of scenarios was created so that the effects of all co mbinations of factors could be observed. Data were collected from simulation of these scenar ios using the software package CORSIM 5.1. Three lane closure configurations—2-to -1, 3-to-2, and 3-to-1—were

PAGE 10

x simulated and one model for estimating capacity was developed for each. All models for each lane closure configuration consider the input factors named previously as well as average speeds per vehicle and lane distributions o f vehicles upstream of the work zone lane closure. The final models show the effects of each of these factors on the throughput capacity of a freeway lane closure. A higher fract ion of vehicles in the to-be closed lane(s) prior to the work zone leads to a significa nt decrease in capacity. Likewise, higher speeds in the to-be closed lane(s) also lead to a capacity decrease. The result of this simulation modeling offers valuable insights i nto the relative capacities under different geometric configurations and traffic stre am scenarios. Future research is recommended to calibrate the models to actual field conditions.

PAGE 11

1 CHAPTER 1 INTRODUCTION This section presents a brief description of existi ng problems and current issues facing transportation agencies. A background is gi ven on specific problem areas and the efforts attempted to increase efficient traffic str eam flow through a work zone lane closure. The problem statement is then provided, f ollowed by a concise statement of objectives for this research. Background Many state transportation agencies are experiencing growing congestion and traffic delays in work zones on rural interstate highways. This congestion results in unproductive and wasteful delays for both motorists and commercial vehicles. It also creates hazardous conditions in which vehicles stop ped in the queues are being approached by vehicles upstream at very high speeds The delays also result in driver frustration, making some drivers willing to take un safe risks in an effort to bypass delays. The Florida Department of Transportation (FDOT) is currently interested in updating its existing methodologies for estimating capacity values through a lane closure. These capacity values through work zones are import ant so that queues and thus delays can be accurately estimated as well. The level of operation of a facility can be assessed from these queue lengths and delay values. The cur rent methods have not been updated since 1995, and the FDOT is particularly interested in an updated method that will facilitate the scheduling and managing of short-ter m work zone lane closures on freeways. The development of this updated capacity estimation procedure will form a

PAGE 12

2 part of a decision matrix that the FDOT is developi ng to assist engineers and contractors in selecting the proper tools to evaluate lane clos ures. The need to maintain adequate traffic flow through short-term interstate work zones is vital on today’s heavily-traveled freeways. Num erous states have policies that provide guidance for when short-term lane closures can be i nstituted. These policies are related to maximum allowable traffic flows, vehicle delays, and queue lengths. Generally, these threshold limits are defined on a state by state ba sis as a function of traffic stream characteristics, highway geometry, work zone locati on, type of construction activities, and work zone configuration (Sarasua, 2004). Limited research by State Departments of Transporta tion—Nebraska and Indiana, for example—has been conducted involving the identi fication and evaluation of alternative strategies designed to control traffic speeds and merging operations in advance of lane closures (McCoy et al ., 1999). In addition, work has been done in the fields of early merging and late merging strategies : models have been developed to predict delays, queue lengths, and lane capacities using many rural interstate areas of the United States as observation sites for data gatheri ng (Beacher et al. 2005). The early merge encourages vehicles to merge into the through lane at locations far upstream of the lane closure. This can be achieved by signs or phy sical barriers. The late merge concept is designed to encourage drivers to use all lanes a pproaching a lane closure and then alternate their entry into the through lane, guided by static signs in addition to normal work zone traffic control. Although some states ha ve put these into practice, only a handful of short-term field studies have formally e valuated their effectiveness. There is

PAGE 13

3 little information available on when the early or l ate merge should be used, however, and a limited understanding of the factors that influen ce their performance. The FDOT requires an updated method that will facil itate the scheduling and managing of the lane closures and considers additio nal operational factors. Some of the same factors used in the current methodology will b e considered as well as new factors that may also have an effect on capacity reduction. Problem Statement The existing procedure used by the FDOT applies an obstruction factor based on lateral clearance and travel lane width, and a work zone factor based on work zone length to the base capacity to estimate a restricted capac ity. The procedure was developed in 1995 and does not account for operating characteris tics of the facility. It is also limited in that the restricted capacities are estimated for 2, 4, and 6-lane two-way facilities that are converted into one-way facilities. The updated cap acity model will consider several additional operating factors in addition to those c onsidered in the current methodology. Furthermore, the updated model will estimate restri cted capacities for one-way freeway facilities with lane reductions. Research Objective The objective of this research is to develop an ana lytical model to estimate the capacity of a temporary freeway work zone based on various geometric and traffic factors. One factor of particular interest is the vehicular lane distributions at different distances upstream of the lane closure. The effect of lane distributions of vehicles upstream of a temporary freeway work zone on the ca pacity of the work zone has not been previously investigated. This relationship is important for selecting an optimal traffic management strategy to implement in order t o maximize traffic flow and

PAGE 14

4 passenger safety through the work zone. Another ne w factor that will be considered is the average travel speed of vehicles by lane, also upstream of the work zone. This factor will serve to compare capacity values at different speeds, potentially leading to speedcontrol strategies to maximize throughput. The lan e distributions’ and upstream speeds’ relationship to the work zone environment and early merge and late merge implementation can begin to answer questions regard ing which strategy may be preferred for a given set of environmental and geometric cond itions. The model(s) developed will be based on the work zo ne environment and geometry, the percentage of large trucks present in the traffic stream, and the presence of other conditions that may affect capacity through t he lane closure. The relationships between the lane distributions and performance meas ures through the lane closure will also be developed to enhance the traffic management strategy selection process.

PAGE 15

5 CHAPTER 2 LITERATURE REVIEW An extensive literature review was conducted to ide ntify and evaluate existing research involving freeway work zone lane closures. Specific focus was given to capacity models developed for estimating vehicular flow through said lane closures. This chapter presents several angles of work zone capaci ty research ranging from existing capacity models to their implementation as part of different types of traffic management strategies. The first section discusses the treatm ent of work zone capacity in the Highway Capacity Manual (HCM 2000). The next secti on presents a review of the current FDOT methodology and its limitations, follo wed by a review of the literature on capacity and its definition for work zones. The fo urth section reviews the software available for work zone analysis, since many comput er models have used capacity as a key input parameter to help quantify queue length a nd delay and to calculate delay costs. Next, literature on freeway merging and general tra ffic management strategies is reviewed. Then, a section is presented outlining p revious research on queuing and delay estimation; both being important in identifying add itional factors that may affect capacity. The last section includes a brief summar y of the findings and recommendations from the literature. Work Zone Capacity in the Highway Capacity Manual ( HCM2000) The HCM 2000 defines capacity as “the maximum susta inable flow rate at which vehicles or persons reasonably can be expected to t raverse a point or uniform segment of a lane or roadway during a specified time period un der given roadway, geometric, traffic,

PAGE 16

6 environmental, and control conditions; usually expr essed as vehicles per hour, passenger cars per hour, or persons per hour.” The HCM 2000 (Chapter 22, Freeway Facilities) recommends that a value of 1600 pc/h/ln be used as the base capacity value for shortterm freeway work zones, regardless of the lane clo sure configuration. It is stated that this base value may be higher or lower when adjustm ents are applied in accordance to the specific work zone’s prevailing conditions. The in tensity of work activity—characterized by the number of workers, types of machinery, and p roximity of travel lanes to work under way—can have an effect on the capacity, incre asing or reducing the base value by up to ten percent. Also, the HCM 2000 states that the effect of heavy vehicles should be considered, as truck presence leads to reduction of capacity. Another element reducing the base capacity value is the presence of ramps. T he HCM 2000 recommends that to minimize the impact of ramp presence on capacity, r amps should be located at least 1,500 ft. upstream from the beginning of the full closure If that cannot be done, and the ramp is within the taper or the work zone itself, then e ither the ramp volume should be added to the mainline volume to be served, or the capacity o f the work zone should be decreased by the ramp volume (up to a maximum of half of the capacity of one lane). The HCM 2000 provides the following equation (Equation 22-2 HCM 2000) for estimating capacity at work zones, which considers reductions due to the three elements discussed above: ca = (1,600 + I – R) fHV N (Eq. 2-1) where ca = adjusted mainline capacity (veh/h) fHV = adjustment for heavy vehicles; defined in HCM Eq uation 22-1

PAGE 17

7 I = adjustment factor for type, intensity, and loca tion of the work activity (ranges from -10% to +10% of base capacity, or -160 to +160 pc/h/ln) R = adjustment for ramps, as described in the prece ding paragraph N = number of lanes open through the short-term wor k zone An additional factor discussed in the HCM 2000, whi ch would decrease capacity and can be considered, is the lane width. It is st ated that capacity may decrease by 9-14% for lane widths of 10-11 ft. Note that this factor is not included in the capacity estimation equation, nor does the HCM discuss potential intera ctions between the various factors affecting capacity. Current FDOT Methodology The Florida Department of Transportation is interes ted in updating its methodology for estimating restricted capacity through a tempor ary work zone lane closure. Their procedure was developed in 1995 and does not consid er operating characteristics of the traffic stream in its reduction estimate. Rather, geometric conditions form the basis of the method. The procedure is limited to the following lane reduction configurations: 2-lane, 2-way facility converted to 2-way, 1-lane 4-lane, 2-way facility converted to 1-way, 1-lane 6-lane, 2-way facility converted to 1-way, 2-lane The base capacities, respectively, for the three co nfigurations listed above, are 1400, 1800, and 3600 vehicles per hour. Capacity r eduction factors are then applied to these base values so that an estimate of restricted capacity may be obtained. The obstruction factor is obtained from a table and is based on the width of the travel lane and the lateral clearance to the travel lane. A latera l clearance of 6 feet and a lane width of 12 feet results in a reduction factor of 1.00, or no r eduction. A lateral clearance of 0 feet and

PAGE 18

8 a lane width of 9 feet results in a maximum reducti on factor of 0.65. The other reduction factor considered in the method is a work zone fact or that is also obtained from a table. This reduction factor is based on the length of the work zone and ranges from 0.98 to 0.50 for work zone lengths of 200 feet through 6000 feet, respectively. Work Zone Capacity in the Literature There are several articles in the literature on lan e-closures in freeway work zones. Krammes and Lopez (1994) presented recommendations on estimating the capacities of short-term freeway work zone lane closures. Their r esearch served as the basis for the HCM 2000 methodology. The study consisted of analy zing lane closures in Texas between 1987 and 1991. The data collected represen t over 45 hours of capacity counts at 33 different freeway work zones with short-term lan e closures. Five different lane closure configurations were analyzed, and data were only used from time periods during which traffic was queued in all lanes upstream of t he work zone area. Capacity counts were taken only at the upstream end of the activity area (i.e., the beginning of the bottleneck). The results of their study showed an average short-term work-zone lane closure capacity value of 1600 pcphpl, and it was r ecommended that this value be used as the starting base value when analyzing these freewa y segments. It was also recommended that this value be adjusted for the effects of heav y vehicle presence, intensity of the work zone, and the presence of entrance or exit ramps ne ar the beginning of the lane closure. The following equation, Equation 2-2, estimates cap acity in a lane closure, taking into consideration the effects of work zone activity int ensity, number of open lanes, and the presence of ramps and heavy vehicle in the traffic stream.

PAGE 19

9 C = (1600 + I R) H N (Eq. 2-2) where C = estimated work zone capacity (vph) I = adjustment for type and intensity of work activ ity (pcphpl) suggested in the research R = adjustment for presence of ramps (pcphpl) sugge sted in the research H = heavy vehicle adjustment factor given in the HC M N= number of lanes open through the work zone Research by Maze et al. (1999) evaluated traffic flow behavior at rural int erstate highway work zones, and estimated the traffic carry ing capacity of work zone lane closures. Traffic performance data were collected at an Iowa interstate highway work zone using data collection trailers, constructed ex clusively for this project. The trailers use a pneumatic mast to hoist video cameras 30 feet above the pavement’s surface where the cameras collected video of traffic operations. Traffic performance data were collected at one work zone on Interstate Highway 80 where two lanes are reduced to one lane. Through analysis of these data, a work zone lane closure capacity from 1,374 to 1,630 passenger cars per hour was estimated. Additional research was completed by Maze et al. (2000) considering the capacities of work zones in rural Iowa. The paper discusses t he procedure for developing an estimate for vehicular capacity through rural inter state work zones in Iowa. The following field data were collected during the summ er of 1998 on Interstate Highway 80 between U.S. 61 and Interstate Highway 74: Traffic flow characteristics—speed, density, and vo lume—at the end of the lane closure taper

PAGE 20

10 Traffic flow characteristics upstream from the lane closure (500 feet) The length of the queues throughout congested condi tions. This is a measure of storage and the difference in queue length from one time interval to the next is the speed that the queue grows or is discharged. One aspect of particular interest to the research w as the observation of the rate at which the queue increases or decreases. Field obse rvation found that backward moving queues were forming at speeds as high as 40 mph. W ith oncoming, unsuspecting traffic arriving at 65 to 70 mph, this creates unsafe relat ive speeds of 100 mph, a problem for rural Iowa’s interstate traffic. It was concluded in the report that the capacities in rural Iowa for work zone lane closures varied from 1,400 to 1,600 passenger cars. This capacity estimation assumed a passenger car equival ency (PCE) value of 1.5 for heavy vehicles. Kim et al. (2001) conducted further research on the capacity of work zones. The study objectives were to investigate various factor s that contribute to capacity reduction in work zones and to suggest a new methodology to e stimate the work zone capacity. The new capacity estimation model is based on traff ic and geometric data collected at 12 freeway work zone sites with four lanes in one dire ction. Traffic data were collected mainly after the peak hour during daylight and nigh t (Maryland State Highway Administration (SHA) has a policy that lanes cannot be closed during the peak-hour.) Multiple-regression analysis was used to develop a model to predict work zone capacity as a function of several key independent factors su ch as the number of closed lanes, the proportion of heavy vehicles, grade, and the intens ity of work activity. The proposed model was compared with other existing capacity mod els, including the Krammes and Lopez model discussed above, and showed improved pe rformance for all of the validation data. The following equation estimates c apacity through a lane closure, and

PAGE 21

11 considers additional factors such as lateral distan ce to the open travel lanes, work zone length, and the location of the closed lanes (left or right or even middle). Capacity = 1857 – 168.1 NUMCL – 37.0 LOCCL – 9. 0 HV + 92.7 LD 34.3 WL – 106.1 WIH – 2.3*WG H V (Eq. 2-3) where NUMCL = Number of closed lanes LOCCL = Location of closed lanes (which lanes are c losed) HV = Proportion of heavy vehicles LD = Lateral distance to the open travel lanes WL = Work zone length WIH = Intensity of heavy work zone activity WG HV = Work zone grade Proportion of heavy veh icles According to the above model, Kim et al. suggests that work zone length has an effect on capacity in the following manner: a long work zone length will likely have more intense work activity, thus reducing capacity. How ever, there is already a term in the model, WIH, that considers work zone intensity. It is unclear then why there is an individual term for work zone length, and not an in teraction term with intensity. Sarasua et al. (2004) conducted a study in South Carolina to dete rmine the number of vehicles per lane per hour that can pass through short-term, interstate work zone lane closures, with minimum acceptable levels of delay. After review of other states’ policies, the methodology was developed based on a 12-month d ata collection period during 20012002 from 22 work zone sites along South Carolina’s interstate system. Heavy vehicles were considered in the analysis, implementing the s oftware Satflo2 to develop PCEs

PAGE 22

12 based on recorded time headways. Sarasua’s paper p resents a summary of the data collection procedures and data analysis methods, as well as the final form of the work zone capacity model. The research recommended a bas e capacity value of 1460 pcphpl. A report by Benekohal and Chitturi (2004) describes a methodology for estimating both operating speeds and capacity at interstate wo rk zones. Data were collected at 11 work zones in Illinois with time-coded video record ing equipment. Headways, speeds, and travel times were among the performance measure s recorded. The following speedflow relationship was developed from the data to es tablish the lower part (congested part) of the speed-flow curve: q = 145.68 U 0.6857 (Eq. 2-4) where: q = flow in passenger cars per hour per lane (pcphp l) U = speed in mph (input speed must be lower than th e speed at capacity) The free flow part of the curve is based on informa tion from the HCM 2000 and on field data collected in work zones. The authors st ate that the capacity model is based on the principle that work zone operating factors (suc h as work intensity, lane width, lateral clearance, etc.) cause reductions in the “operating speed”. Operating speed in a work zone is defined as the speed at which the vehicles would travel through the work activity area after reducing their speed due to work intensi ty, lane width, lateral clearance, and other factors. The adjusted capacity is estimated as follows: Cadj = CU0 fHV PF (Eq. 2-5) where Cadj = adjusted capacity (vphpl)

PAGE 23

13 CU0 = capacity at operating speed U0 fHV = heavy vehicle factor PF = platooning factor (which accounts for the unde rutilization of available capacity, and is a function of drivers’ aggressiven ess, traffic volume, and work zone operations) The model was validated for a two-to-one lane closu re, but the authors recommended additional data collection from work zo nes with different lane closure configurations to further verify the validity of th eir methodology. Work Zone Analysis Software Most computer models, such as Queue and User Cost Evaluation of Work Zones (QUEWZ), have used capacity as a key input paramete r to help quantify queue length and delay, and to calculate delay costs. Memmott a nd Dudek (1984) developed QUEWZ to estimate user costs incurred due to lane closure s. The software is designed to evaluate work zones on freeways, but is also adaptable to di fferent types of highways (Associated Press, 1989). The model analyzes traffic flow thro ugh lane closures, and helps plan and schedule freeway work-zone operations by estimating queue lengths and the additional road user costs. The costs are calculated as a fun ction of the capacity through work zones, average speeds, delay through the lane closu re section, queue delay, changes in vehicle running costs and total user costs. Since its development, QUEWZ has undergone two major modifications. One of these is the ability to determine acceptable schedules for alternative lane closure configuratio ns—crossover or partial lane closure— based on motorist-specified maximum acceptable queu e or delay. The second of these improvements is the development of the algorithm th at can consider natural road user

PAGE 24

14 diversion away from the freeway work zone to a more desirable, unspecified, alternate route (Associated Press, 1989). Another popular software package is QuickZone 2.0, which was released in February 2005 in its full version (Federal Highway Administration, 2000). This software is an enhanced version of QuickZone, an Excel-based software tool for estimating queues and delays in work zones. The maximum allowable qu eues and delays are calculated as part of the procedure in optimizing a staging/phasi ng plan and developing a traffic mitigation strategy. As a result, lane closure sch edules are recommended to minimize user costs. This is a quick and easy method, with a user-friendly, concise spreadsheet setup. Within the software, however, the PCE facto r is fixed at 2.3 for all heavy vehicles, and the capacity of the work zone is fixed at 1200 pcphpl. This PCE value—2.3—is higher than the value reported in the HCM for basic freeway segments (Chapter 23) for level terrain, which is 1.5. This 1.5 value is the same one that is applied to the heavy vehicle adjustment factor for short-term freeway wo rk zones in Chapter 22. The capacity value, fixed at 1200 pcphpl, is also quite conserva tive. As a result, delays estimated using this software would typically be higher than those estimated using the HCM 2000 analysis. Early and Late-Merge Maneuvers Upstream of a Work Z one This section discusses types of merge strategies th at have been developed to improve work zone operations. Examples of such str ategies include “early merge” and “late merge”. These can be implemented in the field using physical barriers or doublelane markings, or even with the presence of a law e nforcement vehicle. Variations of these include the dynamic early merge (used in Indi ana, known as the Indiana Lane Merge) and dynamic late merge. The dynamic early m erge is intended to provide

PAGE 25

15 warning and merge signs at variable distances upstr eam of the back of the queue. The distance is dependent upon the queue length, which is sensed by sonic detectors and enforced with flashing do not pass signs. The dynamic late merge uses the late merge strategy only when congestion is present, otherwise conventional merging is used. The Nebraska Department of Roads (NDOR) refers to conve ntional merging as NDOR Merging. Another merging strategy, called Zip merg ing, is primarily used in Europe and was developed in the Netherlands. With this strate gy, each driver does not change lanes until a fixed distance from the lane closure, alter nating between those in the through lane and the closed lane. Technology has further allowe d for improvements in merging and work zone safety with the creation of "Smart" Work Zones. These are capable of detecting congestion and providing real-time adviso ry information to travelers encouraging them to divert to an alternate route. The remainder of this section discusses literature related to the relationship between thes e strategies and capacity of the work zone. McCoy et al. (1999) identified twelve alternative strategies to control traffic speeds and merging operations in advance of lane closures. Field evaluations of the NDOR Merge and two alternatives, the Indiana Lane Merge and Late Merge were conducted. Based on the data collected, a benefit-cost analysi s showed the cost-effectiveness of four alternative traffic control strategies relative to the NDOR Merge. The four alternatives evaluated were: (1) the Indiana Lane Merge, (2) Lat e Merge, (3) Enhanced Late Merge, and (4) “Smart” Work Zone. The NDOR Merge was foun d to be the most cost-effective merge control strategy for directional average dail y traffic values below 16,000 to 20,500 vehicles, depending on the percentage of trucks. Th e Late Merge, Enhanced Late Merge,

PAGE 26

16 and “Smart” Work Zone were the most cost-effective alternatives at higher traffic volumes. An attempt to evaluate the effects that late merger s have on work zones is reported by Maze and Kamyab (1999) in their Work Zone Simulation Model During the summer of 1998, traffic flow data were collected at merge areas of work zone lane closures on freeways in rural Iowa. Using video image processi ng technology, the merge areas were observed from the point of the flashing merge arrow board to the point where the bottleneck begins—the site of construction. Virtua l detectors were used to collect traffic flow rates, speeds, and headways at the two ends of the merge area. Travel times were also obtained by the noted vehicles’ arrival and de parture times. These data were used to develop a microscopic simulation model specifically designed to examine the effects that slow-moving vehicles and late mergers have on delay and average speed. The model was developed for a work zone with a two-to-one configu ration—two lanes reduced to a single lane (Maze and Kamyab, 1999). The model can estimate delay, as well as the length and dissipation time of the queue. The autho rs report that the length of the queue is overestimated, because the model places 97 percent of vehicles in the through lane, rather than distributing them more evenly over both throug h and merge lanes. For that reason, queue length estimates are not included in the mode l and further data collection and model enhancements are recommended before accurate queue length estimates can be obtained. In a study by Walters and Cooner (2001), it is repo rted that stress levels are reduced in 50% of drivers when bottleneck and work zone improvements are made. Researchers tested the late merge concept, original ly developed in Pennsylvania, at a

PAGE 27

17 work zone on Interstate 30 in Dallas, Texas. The r eport indicates that the Late Merge concept is feasible on an urban freeway where three lanes are reduced to two (Walters and Cooner, 2001). Further testing of this concept and other innovative merge strategies such as Early and Zip Merging is recommended to det ermine the most efficient, safe, and least stressful method of encouraging merging at la ne closures. The late merge strategy was also assessed by Beache r et al. (2005) in a field test conducted over several months. Conducted on a prim ary route in Tappahannock, Virginia, a 2-to-1 lane closure was analyzed and th e results compared with those of traditional work zone lane closure strategies. Alt hough an increase in throughput was observed, the increase was not statistically signif icant. Similarly, time in queue decreased, but the decrease was not statistically s ignificant (Beacher et al. 2005). The report concludes that despite the lack of statistic al significance, more drivers were present in the closed lane, indicating a positive response to the late merge signs. The authors indicate potential statistical biases (such as driv er population and site-specific characteristics) may have had error-inducing effect s on the analysis. In conjunction with the above field evaluation, the late merge con cept was evaluated by comparing it to traditional traffic control using a full factorial analysis. Results of the computer simulations showed that the late merge produced a s tatistically significant increase in throughput volume versus the traditional merge for the 3-to-1 lane closure configuration across all combinations of analysis factors. Altho ugh the 2-to-1 and 3-to-2 configurations did not show significant improvement in throughput overall, it was found that as the percentage of heavy vehicles increased, the late merge did foster higher throughput volumes than traditional traffic control The simulation results indicated that

PAGE 28

18 the late merge may not provide as much of a benefit as previous studies had indicated, and that application of the late merge may be more appropriate in situations where heavy vehicles comprise more than 20 percent of the traff ic stream (Beacher et al. 2005). Other Freeway Work Zone Literature This section summarizes literature review findings related to other aspects of work zone analysis, including safety, traffic diversion, and delay and queuing estimation. Safety Generally, crash rates are higher in work zones tha n on stretches of highway under normal operation, and there are several articles in the literature assessing safety around work zones. For example, Pal and Sinha (1996) deve loped a model that systematically selects appropriate lane closure strategies based o n predicted crash rates. Each lane closure strategy was evaluated through consideratio n of the additional travel time, additional vehicle operating cost, safety, traffic control cost, and contractors’ needs. Opinion surveys of the subcontractors at each of th e project sites were conducted which identify four subcomponents involved in their perce ived need: worker safety, equipment safety, work productivity, and work quality. The d ata used were collected from 17 Interstate 4R projects in Indiana. Information obt ained from the INDOT included type of lane closure strategy used, duration of closure, le ngth of section closed, and traffic data: average daily traffic, hourly variation in volume, directional splits, vehicle mix, and project costs. Also, the number of crashes was obt ained for several years during the construction activities at each site as well as for normal operating conditions at the same sites. Pal and Sinha implemented the analytic hier archy approach to synthesize the study results. Computer software was developed that can be used to select an appropriate lane closure strategy based upon the described parameter s. The user-required inputs are work

PAGE 29

19 zone length, traffic volume, duration of the projec t, crash rate under normal conditions, and total project cost. The software applies regre ssion models to estimate the user-travel time and vehicle operating cost, traffic control co st, and expected number of crashes. This procedure is recommended for selecting between a partial or crossover lane closure with statistically sufficient accuracy (Pal and Sin ha, 1996). Traffic Diversion Ullman (1996) explored how natural diversion affect s traffic volumes at the exit and entrance ramps upstream of temporary work zone lane closures on high volume, urban Texas freeways. Data collection was scheduled to begin before the start time of the lane closure and continued through the time when th e lane closure was removed and the queues on the freeway were completely dissipated. T hese field studies were limited to urban freeways with frontage roads, and of primary interest was observation of traffic operations at the two facilities before, during, an d after the work activity. Data were collected and studies constrained to within the mid day off-peak period (9:00am to 4:00pm), as lane closures are prohibited by law dur ing peak traffic periods in Texas. The following performance measures were obtained from t he data collection activities: Changes in volumes on the freeway, frontage road, a nd ramp volumes hour by hour during lane closure Freeway and frontage road travel times Propagation of queuing on the freeway upstream of t he lane closure over time Ullman discusses further the concept of natural div ersion as well as the requirements for a motorist to make a conscious dec ision in avoiding the congestion. The results of the study show that queue stabilization can occur because flow conditions

PAGE 30

20 within the queue are not uniform and tend to change as a function of the distance from the beginning of the lane closure bottleneck. Ullm an indicates that these changes can be explained by shock wave theory within a traffic str eam, and shows that the stabilization results are consistent with this theory. Thus sign ificant amounts of diversion at temporary closures can have extensive effects upstr eam of the bottleneck. This queue stabilization results in lower user delay values. Then, additional costs of usage can be estimated using “using regular input-output or shoc k-wave analysis based on historical traffic volumes.” Another important result is that these temporary lane closures do not only affect the entrance and exit ramps immediately upstream of the closure, but can extend significantly further than previous models h ave predicted. Ullman recommends that the potential effects of diversion on alternat ive routes should be considered a significant distance upstream of the temporary work zone (Ullman, 1996). Delay and Queuing A large part of selecting an appropriate traffic ma nagement strategy is work-zone related traffic delay. A study conducted by Chien and Chowdhurry (2002) indicates that delays are always underestimated when using determi nistic queuing theory. Therefore, despite the costs associated with many simulation r uns, the report recommends simulation as a viable alternative, when combined w ith queuing theory. The authors developed a methodology that approximates delays by combining CORSIM simulation data and deterministic queuing while considering va rious geometric conditions and timevarying traffic distribution. The traffic flow dis tribution over time and the work zone capacity are the two major inputs to the model. Th e queuing delay is then calculated from the estimated queue lengths of the previous ti me period. Delay values from work zone traffic operations on a segment of I-80 in New Jersey were predicted using

PAGE 31

21 deterministic queuing, CORSIM simulations, and the proposed model. Because the model is dependent on the accuracy of the CORSIM de lay curve, extensive calibration and validation of CORSIM may be required. Ullman and Dudek (2003) describe a new theoretical method that more accurately predicts the lengths of queues that develop under a temporary work zone lane closure. The authors state that the queues and delays that d evelop upstream of closures in urban areas are much shorter than those estimated using h istorical traffic volume data. Rather than propagating, these queues often stabilize upst ream over the duration of the lane closure (Ullman and Dudek, 2003). The new formulat ion is based on a traditional macroscopic perspective of traffic flow on a sectio n in which flow, speed, and density are known. A new, permeable pipe analogy is presented to represent the work zone’s creation of a stimulus for diversion. The mathemat ical components of the model include the following in its algorithm: A shock wave theory to model the propagation of the traffic queue An energy model of traffic flow that illustrates th e reduction in speed and its effect on natural diversion tendencies A mathematical analogy of urban roadway section as fluid flow through a section of permeable pipe This macroscopic model predicts queue stabilization at some point, so overestimation of queue lengths does not occur. Ho wever, Ullman and Dudek recommend that more work is required to further com prehend the stimuli that affect permeability of a corridor, and to develop a model that can estimate what this level of permeability may be for a given set of conditions. Chitturi and Benekohal (2005) performed a study on the effects of narrowing lanes and reduced lateral clearances on the free-flow spe eds (FFS) of cars and heavy vehicles

PAGE 32

22 in work zone areas. The findings report that the r eductions in FFS of vehicles in work zones due to narrow lanes are higher than the reduc tions given in the HCM for normal freeway sections, although the reduction due to nar row lateral clearance was comparable. Because of the wider dimensions of heavy vehicles, the reduction in FFS of heavy vehicles is greater than that of passenger cars. A s a result, heavy vehicles are affected more adversely than passenger cars, and it is recom mended that the speed reductions due to narrow lanes should take into account the percen tage of heavy vehicles in the traffic stream. The reductions for passenger cars and heav y vehicles have not been quantified separately because of the limited data for heavy ve hicles. Until such data become available, it is recommended that 10, 7, 4.4 and 2. 1 mph be used for speed reduction in work zones for lane widths of 10, 10.5, 11 and 11.5 ft respectively (Chitturi and Benekohal, 2005). Summary and Conclusions A review of the literature illustrates many ways of developing a model that estimates capacity through a temporary work zone. No two procedures are alike, differing in the ways that data are collected and a nalyzed as well as in the selection of factors that affect capacity reduction. The follow ing is a summary of those work-zone capacity-reducing factors that have been included i n existing models: number of closed lanes heavy vehicle presence grade of roadway segment intensity of work activity merge strategies such as late merge and early merge lane widths presence and location of ramps proximity of travel lanes to work zone activity

PAGE 33

23 Work zone capacity base values obtained around the country have varied since the introduction of Krammes and Lopez’s Texas-based rec ommendation of 1600 pcphpl (which is also used in the HCM 2000). The Iowa-bas ed study by Maze produced a model that recommended base values ranging from 1374 to 1 630 pcphpl, depending on the location within the state. Sarasua’s model estimat es a value of 1460 pcphpl for South Carolina, and the QuickZone 2.0 software implements a conservative 1200 pcphpl in its analyses. The current FDOT procedure only consider s geometric factors in its capacity reduction model and should be updated with factors that consider operational characteristics of the traffic stream.

PAGE 34

24 CHAPTER 3 METHODOLOGY At the onset of this research, the goal was to loca te 4 different freeway segments with temporary lane closures. Two of these were to be lane closures on two-lane segments reduced to one lane and the remaining two were to be three-lane segments reduced to two lanes (from this point on, these wil l be referred to as a 2-to-1 and 3-to-2 lane closures, respectively). The data were to be collected during daylight hours via video recording devices installed at key locations throughout and upstream of the work zone. Several sites were located, but complication s quickly arose. The status of the different projects (percent completed) were not kno wn exactly, so coordinating with the project managers to set up data-collection equipmen t was not possible. In addition, contractors have been urged to move toward night co nstruction on the incentive of higher pay if freeway delays are minimized. Nighttime lan e closures do not experience the same volumes of traffic as during the peak hours of the day, so breakdown, a required condition for capacity estimation, is typically not observed. As a result of these obstacles to field data collec tion, computer simulation of the lane closure incidents was selected as the next bes t tool for collecting the data. Simulation modeling cannot replace field data colle ction; it can, however, offer insights into the relative capacities under different geomet ric configurations and traffic stream scenarios. A large matrix of scenarios was thus cr eated that considered many of the factors identified from previous research. Each sc enario was input into the simulator and run 15 times to ensure that the mean error was with in the tolerance limit. Data such as

PAGE 35

25 speeds, vehicle lane distributions, headways, and v olumes were gathered from the output files and combined with the input factors to develo p significant relationships between these variables and the capacity through the work z one lane closure. Simulator Selection The software package CORSIM was selected for use in the study for several reasons. This software is available to the Univers ity of Florida through Mc Trans allowing for a high level of software support in un derstanding the algorithms. In addition, CORSIM has the ability to simulate freeway sections with i ts integrated package FRESIM, and the 5.1 edition has been updated with an impro ved FRESIM engine (Owen et al., 2000). The following are the principal improvement s that were made over past versions of CORSIM: Errors in FRESIM collision avoidance were corrected Destination assignment and leader determination wer e eliminated Changes were made to the logic that deals with vehi cles crossing interface nodes to improve the car following between networks Errors in processing truck restriction lanes were c orrected An error in the way vehicle counts on Record Type 5 3 were converted into entry volumes was corrected. In addition to these improvements, FRESIM allows for the analysis of incidents on freeways as either lane closures, lane drops, or ev en a shoulder incident, which can be simulated by the addition of a rubbernecking factor to the length of the segment affected. Calibration of a FRESIM network is possible using techniques such as rubber necking and car-following sensitivity factors, allowing for a r ealistic representation of real-world conditions.

PAGE 36

26 Challenges with Previous Versions of CORSIM/FRESIM Despite the obvious advantages, several problems ar ose when considering CORSIM as the software package of choice. Literatu re from 1995 explains that FRESIM was unreliable when simulating lane closures, as it did not account for slow-moving vehicles that severely impacted the queue lengths i n the field. According to Dixon et al. (1995), the large queues observed in the field were due to the existence of one or two vehicles in a data set that traveled inexplicably s low through the work zone—much slower than the distribution of speeds in a simulat ion—and thus caused a queue buildup that did not appear in the simulator. As a result, FRESIM underestimated the delay because these vehicles did not exist in the simulat ion runs. Therefore, the behavior of vehicles at the lane closure was not replicating ac tual conditions (Dixon et al. 1995). The 1995 report used the software FRESIM version 4. 5, and since then, improvements such as those named previously have led to the vers ion 5.1 release (McTrans). Resolved Challenges with Current Versions of CORSIM /FRESIM After several initial simulation trials, it was det ermined that even CORSIM 5.1 has several processing problems with the FRESIM outputs These problems with FRESIM were forwarded to the software development team, an d the developers ran the same *.trf files so that the same outputs could be obtained an d evaluated. It was concluded that these are indeed problems with CORSIM 5.1 and that the newest version, CORSIM 6.0, corrects all of these issues. Although not current ly commercially available, the University has obtained a Beta copy of the newest software for testing purposes o nly. CORSIM 5.1 will be used for all analyses in this re port.

PAGE 37

27 The first problem is an inconsistency in the volume s of vehicles when reported by link and by lane The software developers quickly corrected this i ssue, identifying that the correct outputs from 5.1 were those by lane Therefore, only values of vehicles by lane will be extracted from version 5.1. A second issue with CORSIM 5.1 involves the output from the data stations. A data station is p laced on several of the links of a freeway and headway and speed data are collected at a speci fied point from the upstream node (Note: this is not a detector, but a data station. Its only function is to collect speed and headway statistics at a point of interest). The ou tput files, however, report what seems to be an incorrect distribution of headway values. Th ere are too many values in the 0.4 to 1.2 second-range, which is not realistic for freewa y operating conditions. This second issue with vehicle headways and distributions, is a result of model input driver-type parameters. Because no field data were available f or calibration, these parameters are set to the default values for all simulation runs. Acc ording to the software developers, the default values for the car-following parameters are set up to maximize throughput in any scenario; as a result, the distribution may seem un realistically skewed (McTrans). This will effectively lead to higher flow rates and thus higher capacity values through the work zone, and this issue is discussed in model developm ent results. The third issue is related to the headway values reported by the data stations The mean headways reported for each link should be specific and different by lane. However, for each data station, the headways are equal by lane. This issue was also re solved quickly. There is an error in output processing in version 5.1 and the headways b y lane per link should be calculated from the average volume given by lane per link. Thus, the data collected for headways by lane was calculated and not taken directly from the incorrect output.

PAGE 38

28 CORSIM 5.1—with the described corrections—will ther efore be used for all analyses. Data were collected accurately from the 5.1 output based upon the corrections from the software developers. Modeling of Work Zones with CORSIM 5.1 This section presents the preliminary analyses need ed to create appropriate scenarios. The first section defines the operating conditions of the network simulated. The way in which CORSIM can simulate a work zone is described second, followed by a detailed justification of closing only the rightmos t lanes. The last sub-section summarizes the results, including the number of run s required per scenario, and the way in which the work zone will be simulated. Operating Conditions A test network was created in order to evaluate whe ther a lane drop or incident approach should be used for the study. With 15% tr ucks present on the freeway, the breakdown volume for a 2-to-1 lane closure was foun d to be 1900 veh/hr. The breakdown volume, as defined for this study, is the minimum volume that will cause freeflow speeds to be reduced by 30% or more at the lin k immediately upstream of the lane closure. Furthermore, this decrease in speed leads to queue formation upstream of the lane closure location (for the remainder of the doc ument, the location of the lane closure will be referred to as the bottleneck). As a resul t, the discharge of this queue into the work zone lane closure is causing the facility to o perate at capacity. This flow, 1900 veh/hr, is used throughout the following experiment s. Another consideration in operating conditions relates to the driving behavior of truck s. Throughout all experiments and simulation runs, trucks will be biased to traveling on the rightmost lane of the freeway segment. FRESIM provides three choices for truck behavior: not bias ed or restricted to

PAGE 39

29 any lanes, biased to a set of lanes, and restricted to a set of lanes. The lanes to which a truck is biased can be specified on the on-screen i nterface within FRESIM. For all 2-to-1, 3-to-2 and 3-to-1 lane closures, trucks will be bia sed to traveling in the rightmost lane or lanes. Simulation of a Work Zone There is no explicit simulation of a work zone in FRESIM ; instead, there are two techniques that allow FRESIM to approximate a work zone lane closure, and both a re built-in to the user-friendly interface. The first of these is identified as a lane add/drop. The options allow up to three lane additions or dro ps to occur within the same link. So, to simulate a right-lane closure, the rightmost lan e would be dropped at a point specified at a distance from the upstream node, and then it w ould be added at another specified point designated again by a distance from the upstr eam node. The second technique that can be used to simulate a lane closure is identifie d as an incident. The user can create multiple incidents during different times of the si mulation on the same link. Such incidents include capacity reduction due to a shoul der incident (requires a rubbernecking factor) and/or blockage at a point of incident. Ea ch of these can occur simultaneously and on several lanes if desired. Both techniques r equire an upstream distance for a warning sign, signaling that a lane closure is appr oaching. It should also be noted that neither technique has an input for a taper prior to the lane closure. The incident technique was used in all analyses for this study a nd the justification is given in the Results of Preliminary Analyses section. Lane Closure Location The next step was to evaluate whether closing the r ight lane produced the same results as closing the left lane. The value that w as selected as a performance measure

PAGE 40

30 was network-wide average delay. The results of the first experiment comparing the delay values between lane closure techniques as well as l eft and right closures are presented in the following table, Table 3 1. Table 3 1. Delay Values for Combinations of Lane Closures and Lane Distributions (Ten Simulation Runs) Trucks biased to rightmost lane Results based on 10 simulation runs All flows 1900 veh/hr Lane distribution Trucks Incident Lane Drop L% / R% % Delay (veh-hr) Delay (veh-hr) 60/40 0 6.066 5.991 40/60 0 6.092 6.118 60/40 15 68.703 75.899 Right Lane Closure 40/60 15 61.552 65.217 40/60 0 6.028 5.967 60/40 0 6.216 6.288 40/60 15 56.306 47.353 Left Lane Closure 60/40 15 60.312 42.463 As can be seen from Table 3 1 above, the experime nt was run for both the lane drop and incident techniques. In order to accept t hat closing the left lane produces the same results as closing the right lane, the first v alue of delay in the incident column for Right Lane Closure should match the first value of delay in the incident column for Left Lane Closure, and so on. The values are similar be tween right and left lane closures for 0% truck presence, but differ greatly when truck pr esence is increased to 15%. Also, the

PAGE 41

31 values between the incident technique and lane drop technique show no consistency and no intuitive reasoning can explain the differences between the numbers. Because of these discrepancies, it is not possible to determine whet her closing the right lane will produce the same results as closing the left lane by using network-wide average delay as a performance measure. This value does not describe what is happening per vehicle, which causes an inconsistent result that is based upon ho w many vehicles enter the system, which is dependent upon flow and breakdown conditio ns. Therefore, a different performance measure was considered that does look a t the value per vehicle. Average speed per vehicle was considered and it was found t hat 9.71 runs are required for an error tolerance of 15% (see below for calculation): Table 3 2. Average Speeds per Vehicle (Ten Simul ation Runs) Run # Speed (mph) 1 36.62 2 29.80 3 32.02 4 54.82 5 54.48 6 51.10 7 48.14 8 42.30 9 32.24 10 33.58 Calculation of required number of simulation runs: Sample Size, n = 10 Sample Mean, MN = 41.51 Sample Std. Dev., SN = 9.899 Error (15%), E = 0.15 MN = 6.227 95% Confidence Interval: +/(1.96 SN) / (n2) = +/(6.135)

PAGE 42

32 Required number of runs for 15% error tolerance: N = 1.962 SN 2 / E2 = 9.709 Thus 10 runs are used to compare the values of spee d. The results are displayed below in Table 3 3. Table 3 3. Average Speed Values for Different Co mbinations of Lane Closures and Lane Distributions Trucks biased to rightmost lane Results of Ten Simulation Runs All flows 1900 veh/hr Lane distribution Trucks Incident Lane Drop L% / R% % Speed (mph) Speed (mph) 60/40 0 61.483 61.490 40/60 0 61.481 61.478 60/40 15 41.289 40.015 Right Lane Closure 40/60 15 42.773 42.503 40/60 0 61.536 61.520 60/40 0 61.410 61.403 40/60 15 45.397 46.725 Left Lane Closure 60/40 15 44.195 49.084 As seen in Table 3 3 above, the values of average speed per vehicle between left and right lane closures as well as between incident and lane drop techniques are very close and within the 15% tolerance error.

PAGE 43

33 Results of Preliminary Analyses From these results (10 runs based upon 15% error to lerance), it is concluded that any simulation scenario analysis need not be perfor med on both a left and right lane closure, but only on one, as the other will produce the same result. In addition, because the two techniques of simulating a lane closure pro duce almost identical values within 15% error, the technique which offers more options in the simulation is selected. Therefore, the use of an incident will be used in a ll simulation runs throughout the report, and the lane drop technique will not be used. This decision is based on the versatility of the incident technique, allowing for the effects of a rubberneck to be simultaneously implemented with a lane closure. Simulation Scenarios This section will outline the network schematic, th e input variables and fixed values, the number of required simulation runs, and the simulated data that will be collected. Simulated Test Section Figure 3 1 below shows the simulated test segment that is analyzed. 150 ft. mi. mi. 0.5, 1.0, 2.0 mi. 150 ft. 0.5, 1.0, 1.5 mi. 1.0 –3.5 miles23 45678 Sign location Figure 3 1. Sketch of the freeway network used i n data collection

PAGE 44

34 There are a total of nine nodes (2-8 displayed). T he feeder node is located 0.5 miles upstream of node 2. The following is a discu ssion of the function and characteristics of each link: Link (2,3) – 150 feet in length; created to verify headways values being collected by the data station (located halfway between nodes 2 and 3) Link (3,4) – Length is variable from 1 to 3.5 miles ; created to give vehicles adequate time for discretionary lane changes a far distance upstream of the work zone; variable distance is due to variability in li nks (6,7) and (7,8) (see below). Link (4,5) – 150 feet in length; created to verify headway values being collected by the data station (located halfway between nodes 4 a nd 5) Link (5,6) – Always 0.5 miles in length; created to observe the driver behavior prior to the work zone warning sign. Link (6,7) – Length is variable from 0.5 to 1.5 mil es; this is the distance from the work zone to the upstream warning sign. The changi ng of this distance is one reason for the variability in the length of Link (3 ,4). The overall network length is constant, so Link (3,4) is either lengthened or sho rtened when Link (6,7) is either shortened or lengthened, respectively. Link (7,8) – Length is variable from 0.5 to 2.0 mil es; this is the link in which the lane closure is in place. The changing of this dis tance is the other reason for the variability in the length of Link (3,4). The overa ll network length is constant, so Link (3,4) is either lengthened or shortened when L ink (7,8) is either shortened or lengthened, respectively. There is also a data sta tion placed halfway between nodes 7 and 8, in order to verify headway data on that li nk. Input Variables The variables selected for the model development ar e listed below and their values and limitations are described in detail following t he list: Lane Configurations – 2/1, 3/2, 3/1 Volume Distributions (percentages) o (2/1 closure) – 50/50, 40/60, 30/70 (left/right) o (3/2 and 3/1 closure) – 20/40/40, 30/30/40, 30/40/3 0 (left/middle/right) Length of Work Zone – 0.5 mi, 1.0 mi, 2.0 mi Distance of Sign Upstream of Work Zone – 0.5 mi, 1. 0 mi, 1.5 mi Presence of trucks (percentage) – 0%, 10%, 20% Rubbernecking factor (percentage) – 0%, 15%, 25%

PAGE 45

35 The input volume distributions were determined by c onsidering reasonable operating conditions for a free-flowing freeway net work. For example, a 20/80 input distribution was not used because it is unlikely th at such a distribution would be observed in the field. The maximum length of the work zones is limited by the FDOT Design Standards for 2006. These state that for any facil ity where the speed limit is greater than 55 mph, the length of the work zone shall not excee d a length of 2 miles (Design Standards, Index 600, Sheet 2 of 10). Also, the wa rning sign placement upstream of the work zone is to be at a distance no less than 0.5 m iles for facilities where the posted speed limit is 45 mph or more (Design Standards, In dex 600, Sheet 4 of 10). The analysis of the effect of the work zone length on the work zone capacity showed no significant relationship between the two variables. Figure 3 2 below illustrates the relationship between the work zone capacity and the length of the work zone lane closure from the simulated data. Work Zone Capacity vs. Work Zone Lengthy = 0.322x + 1590.6 R2 = 0.0000004 1000 1200 1400 1600 1800 2000 2200 2400 2600 00.511.522.5 Work Zone Length (mi)Work Zone Capacity (veh/hr/ln) Figure 3 2. Relationship between work zone flow and work zone length

PAGE 46

36 From Figure 3 2 above, there exists no relationsh ip between the length of a work zone and the capacity throughput. As the length of the work zone increases, no significant variation in vehicular flow exists thro ugh the lane closure. This variable will therefore not be included in any of simulation runs for all lane closure configurations. The presence of trucks ranges from zero to twenty p ercent, again limited by the consideration of reasonable operating conditions. Similarly, the rubbernecking factor ranges from zero to twenty-five percent, and will b e used to model any type of incident on the shoulder or the presence of law-enforcement vehicles or general road work equipment. Because there is no literature on the e ffect of the rubbernecking factor, several simulation runs were made to identify and u nderstand the way this factor affects the capacity of the roadway. To view the effect of the rubberneck factor on a fr eeway work zone lane closure segment, the capacity of that segment is compared t o the rubberneck factor used. The scenario tested is a 2-to-1 lane closure with a flo w rate of 2200 veh/hr distributed 40% in the through (left) lane and 60% in the closed (righ t) lane. The length of the work zone is 0.5 miles, truck presence is at 20%, and the advanc e warning sign is located at 0.5 miles upstream of the bottleneck. Table 3 4 below illu strates the effects of the rubbernecking factor on the capacity through a work zone lane clo sure:

PAGE 47

37 Table 3 4. Effects of Rubbernecking Factor on Ca pacity through Work Zone Lane Closure Trucks biased to rightmost lane Results of Five Simulation Runs All flows 2200 veh/hr Rubberneck Factor (%) Average Headways through Work Zone (5 simulation runs) Average of headways Capacity (veh/hr/ln) 0 1.65, 1.64, 1.64, 1.64, 1.64 1.64 2192.5 5 2.01, 1.98, 2.09, 2.02, 2.11 2.04 1763.0 15 2.24, 2.20, 2.25, 2.25, 2.26 2.24 1607.1 25 2.61, 2.51, 2.47, 2.61, 2.47 2.53 1420.7 35 2.79, 2.76, 2.78, 2.78, 2.79 2.78 1295.0 45 2.98, 2.99, 2.98, 2.97, 2.96 2.98 1210.0 The results indicate that an increase in the rubber necking factor leads to a decrease in capacity. The capacity through the work zone is calculated by dividing 3600 (seconds/hour) by the headway value (seconds/vehicl e) in the previous column. This relationship is not linear with values greater than 25%. In addition, the percentage of rubbernecking does not reduce the capacity by the s ame percent. For example, the 5% rubbernecking factor reduces capacity by nearly 20% whereas a 25% rubbernecking factor has only a slightly increased effect on capa city reduction. After this point, increasing the rubbernecking factor does not have a s large an effect and it is for these reasons that 25% will be the maximum rubbernecking factor applied in this study. Simulated Test Section Setup—Input Fixed Values As limited by the FDOT Design Standards, the free-f low speed that will be used throughout all analyses will be 55 mph through the work zone; this value cannot be lower than 10 mph less than the mainline free-flow travel speed (Design Standards, Index 600,

PAGE 48

38 Sheet 3 of 10). Because the facility being modeled can be a state highway or freeway facility, a free flow speed for mainline traffic of 65 mph will be used throughout the analysis. A value of 2400 vehicles per hour per non-closed la ne will be implemented as the fixed flow rate. This is the flow rate at which br eakdown occurs—speed reduction of at least 30% immediately upstream to the lane closure— with nothing other than passenger cars present in the traffic stream. This base case did not include any trucks and had a rubbernecking factor of zero percent. The traffic entering the system was distributed equally between lanes—50/50—and the upstream sign w as placed at 1.5 miles upstream of the beginning of the bottleneck. This flow rate of 2400 vehicles per hour per nonclosed lane will cause a queue to form and the disc harge causes the downstream link to operate at capacity. Therefore, for 2-to-1 and 3-t o-1 lane closure configurations, the flow rate will be fixed at 2400 veh/hr. For the 3-to-2 lane closure, the flow rate will be 4800 veh/hr. The relationships that are shown in CHAPTER 4: MODE L DEVELOPMENT do not appear to have any values of capacity between 2 000 and 2200 veh/hr/ln. Each point plotted is the average of 15 simulation runs. Ther efore, the average of the runs do not have values of capacity between 2000 and 2200, but the full dataset does include values between these numbers. This apparent absence of da ta is a result of the type of breakdown that is occurring based on the characteri stics of the traffic stream for specific scenarios. All values of capacity above 2200 veh/h r/ln have only passenger cars in the traffic stream and both the rubbernecking factor an d truck percentage is at zero. The type of breakdown that occurs in this case is different than if any other factor is present. With

PAGE 49

39 only passenger cars in the traffic stream, queues a re building and recovering throughout the simulation time period. There is a shockwave p resent that travels upstream, causing breakdown conditions to occur and dissipate through out the traffic stream. Therefore, because free-flow speed is reduced by at least 30%, capacity is being observed downstream through the work zone. Once a rubbernec king factor or a presence of trucks is introduced into the traffic stream, however, cap acity through the work zone is reduced due to a breakdown in speeds that is greater than 5 0%, causing a permanent queue o form that does not recover within the simulation time pe riod. As a result, there is a constant discharge of vehicles into the work zone, and capac ity conditions are also observed, but the values are lower than the passenger-car only tr affic stream. Required Number of Simulation Runs As calculated previously, 10 simulation runs per sc enario are required for a 15% error tolerance in the sample mean. In order to ob tain a higher number of data points from the data collection (from simulation) and thus further increase final model precision, 15 runs per scenario will be simulated. Total Number of Simulation Scenarios There will be four variable input values per lane c losure configuration, each with three levels of variation. For thoroughness, every effect that each variable has on the other will be analyzed, and thus a total of 243 dif ferent scenarios will be analyzed, each simulated 15 times: 4 input values, 3 levels Nscen = 34 = 81 (per lane closure configuration) Table 3 5 below illustrates the three levels of v ariation for each variable for all lane closure configurations. Because there are two other configurations—3/2 and 3/1— the total number of scenarios is 243.

PAGE 50

40 Table 3 5. Variation of Input Parameters Variable Level 1 Level 2 Level 3 units lane configuration 2-to-1 3-to-2 3-to1 total-to-open lanes in work zone 50 / 50 40 / 60 30 / 70 left % / right % lane distributions 20 / 40 / 40 30 / 30 / 40 30 / 40 / 30 left % / middle % / right % upstream sign distance 0.5 1 1.5 miles truck % 0 10 20 percentage rubber % 0 15 25 percentage As a result, the 243 files created will each be sim ulated for 15 runs, giving a total of 3,645 output files, or data points for model develo pment. Output Values In addition to the variables described in the previ ous section, data from the simulations will be collected to model the lane dis tributions and their effect on work zone capacity. The following values will be taken from the output files and used for model development: Volumes by lane through link (7,8) Vehicle lane distributions through all links Speeds by lane through all links Number of lane changes through all links TRAFVU screen shots for each of the 3 different lan e closure configurations are provided in APPENDIX F.

PAGE 51

41 CHAPTER 4 MODEL DEVELOPMENT The software package STATISTICA (Release 7) was used in developing the relationships between selected performance measures and the vehicular capacity through the temporary lane closure. This section summarize s the process of data analysis and model development for each of the three lane closur e configurations. First, the relationships between collected data and capacity w ill be outlined and identified. This leads into the development of the models, using General Linear Regression techniques as provided by the software. The final models consist of a combination of highlycorrelated, statistically significant parameters th at are obtainable from field data collection. As a result, the models are practical and easy to use. Finally, an example outlining the steps in using each of the models wil l be provided for each respective lane closure configuration. Data Analysis The network created for simulation has been designe d so that field data may be input into the appropriate location on the freeway segment. For this reason, many links were created upstream of the lane closure in order to collect lane distribution data significant distances upstream of the first lane cl osure warning sign. For the development of the models, however, only those lane distributio ns from links (4,5), (5,6), and (6,7) are considered. In CORSIM 5.1, lane distributions upstream of the warning sign are not affected by the work zone. Therefore, the simulate d behavior of vehicles just upstream of the location of the warning sign is the same as tha t of vehicles three to four miles

PAGE 52

42 upstream in CORSIM 5.1 (this is not expected in the field, which is the r eason for the existence of more upstream links not used in modeli ng). When simulated vehicles pass the sign, they react and merge based on existing qu eues, gaps, travel speeds, and driver aggression level. Because the warning sign is loca ted at Node 6 for all simulation scenarios, the parameters for lane distributions, s peeds, and lane changes will only be estimated for the aforementioned links in the model development. In addition, the lane distributions that were input into the simulation are not used in model development. Rather, the actual lane distrib ution data collected from links (4,5), (5,6), and (6,7) were used. The program distribute s the vehicles into lanes as designated by the input percentages. However, immediately upo n entry into the system, vehicles begin to make discretionary lane changes that alter the input lane distribution percentages. The degree to which vehicles make the se lane changes can be controlled by modification of the driver behavior parameters with in the software. The lack of field data made any modifications irrelevant and thus the defa ult values for driver types were used. These numeric default values range from not aggress ive to very aggressive for ten driver types, and 10% of each type comprise the traffic st ream (the user cannot modify this). For that reason, observed lane distributions are used in model development. 2-to-1 Lane Closure Model Development For this test configuration, a two-lane freeway seg ment is reduced to one lane with the rightmost lane closed. The following input var iables and performance measures were plotted against the value of capacity through the l ane closure and are displayed in the following order in this section: Input Variables Upstream sign distance

PAGE 53

43 Truck percentage Rubbernecking factor Performance Measures (from simulated data) Capacity through lane closure (shown plotted agains t all other data) Lane changes per link Speeds per vehicle Actual lane distributions Figure 4 1 below shows the relationship between t he work zone capacity and the location of the upstream warning sign. Work Zone Capacity vs. Upstream Sign Locationy = 9.4476x + 1581.5 R2 = 0.0002 1000 1200 1400 1600 1800 2000 2200 2400 2600 00.20.40.60.811.21.41.6 Upstream Sign Location (mi)Work Zone Capacity (veh/hr/ln) Figure 4 1. Relationship between work zone capac ity and upstream warning sign distance As the distance of upstream warning increases, ther e is a small increase in capacity. This relationship, though immediately not apparent, becomes more significant when

PAGE 54

44 viewed in combination with the lane distributions o f link (6,7) as an interaction variable. This is discussed later in this section with Figure 4 9. The following two figures (4 2, 4 3) show the e ffects of an increase in the truck percentage and an increase in the rubbernecking fac tor on the throughput capacity. Work Zone Capacity vs. Truck percentagey = -14.651x + 1737.5 R2 = 0.1552 1000 1200 1400 1600 1800 2000 2200 2400 2600 0510152025 Truck PercentageWork Zone Capacity (veh/hr/ln) Figure 4 2. Relationship between work zone capac ity and truck presence in traffic stream Work Zone Capacity vs. Rubbernecking Factory = -24.407x + 1916.4 R2 = 0.6821 1000 1200 1400 1600 1800 2000 2200 2400 2600 051015202530 Rubbernecking Factor (%)Work Zone Capacity (veh/hr/ln) Figure 4 3. Relationship between work zone capac ity and truck presence in traffic stream

PAGE 55

45 Both figures above show that an increasing percenta ge of trucks in the traffic stream or an increasing rubbernecking factor leads directly to a decrease in capacity through the lane closure. These variables are expe cted to be significant factors in the final models. The relationship between the number of lane changes in link (6,7) and the throughput capacity is shown below in Figure 4 4. Work Zone Capacity vs. Lane Changes in (6,7)y = 6.8601x 221.27 R2 = 0.5495 1000 1200 1400 1600 1800 2000 2200 2400 2600 050100150200250300350 Lane Changes in (6,7)Work Zone Capacity (veh/ln/hr) Figure 4 4. Relationship between work zone capac ity and lane changes in link (6,7) There is a positive correlation indicating that an increasing number of lane changes leads to a higher value of capacity. Although cons idered in model development, this variable was ultimately not included in the final m odels. This variable cannot be collected easily in the field. In addition, the ef fect of this variable is captured by other included model parameters that are more important a nd could not be excluded. For

PAGE 56

46 example, as shown below in Figure 4 5, the number of lane changes is correlated to the length of link (6,7). Lane Changes in Link (6,7) vs. Length of Link (6,7)y = 22.861x + 241.31 R2 = 0.0809 0 50 100 150 200 250 300 350 00.20.40.60.811.21.41.6 Length of Link (6,7) (mi.) Number of Lane Changes in Link (6,7) Figure 4 5. Relationship between the number of l ane changes in link (6,7) and the length of link (6,7) The number of lane changes did not show significanc e when included in the model with other parameters that were more important (the effect of the number of lane changes is captured by the percentage of vehicles traveling on lane 1 and lane 2, represented as vehicle lane distributions in the final models). The distance of the link (6, 7)—which also captures the effect of the lane changes—however, is included in the final models represented by the variable upstream sign distance Figure 4 6 illustrates the relationship between t he speeds of vehicles in all lanes in the link upstream of the placement of the warning s ign. At this point, the vehicles have not seen and thus have not reacted to any type of w arning or work zone ahead.

PAGE 57

47 Work Zone Capacity vs. Speed in link (5,6)1000 1200 1400 1600 1800 2000 2200 2400 010203040506070 Speed in link (5,6) (mph)Work Zone Capacity (veh/hr/ln) Lane 1 Lane 2 Linear (Lane 2) Linear (Lane 1) Figure 4 6. Relationship between work zone capac ity and the average speed per vehicle in lanes one and two of link (5,6) Both lanes show an increase in capacity with increa sing link speeds. This variable will thus be considered in model development. The speeds in the link immediately downstream of th e lane closure warning sign were also considered, and their relationship to the throughput capacity is shown below in Figure 4 7.

PAGE 58

48 Work Zone Capacity vs. Speed in Link (6,7)y = 26.629x + 1302.7 R2 = 0.7737 y = 10.489x + 1273.1 R2 = 0.506 1000 1200 1400 1600 1800 2000 2200 2400 2600 010203040506070 Speed in link (6,7) (mph)Work Zone Capacity (veh/hr/ln) Lane 1 Lane 2 Linear (Lane 2) Linear (Lane 1) Figure 4 7. Relationship between work zone capac ity and the average speed per vehicle in lanes one and two of link (6,7) There is a strong relationship between these variab les, which were also considered in the final model development. An increase in spe ed in lane 1 (closed lane) does not increase capacity as much as a higher speed in lane 2 (through lane). This is because a higher speed in lane 1 implies less congestion and thus smoother merging into the through lane. In this case of less congestion, the re is a steady flow of vehicles traveling in lane 2 which increases the capacity more signifi cantly with an increase in traffic stream speed. Another important relationship that was identified is that of the distribution of vehicles in links upstream and immediately downstre am of the work zone warning sign. These relationships are important if an agency want s to implement a particular traffic management strategy. If higher capacities are a re sult of lower percentages of merging

PAGE 59

49 vehicles, for example, then an early merge strategy is an effective option. Figure 4 8 below shows the effect that the vehicular lane dist ributions have on work zone capacity. Work Zone Capacity vs. Lane Distributionsy = -1412.8x + 2073.4 R2 = 0.3943 1000 1200 1400 1600 1800 2000 2200 2400 2600 00.10.20.30.40.50.60.7 Lane Distributions of Lane 1 (closed lane)Work Zone Capacity (veh/hr/ln) link (4,5) link (5,6) link (6,7) Linear (link (6,7)) Linear (link (4,5)) Linear (link (5,6)) Figure 4 8. Relationship between work zone capac ity and the vehicular distributions on lane one of all links The relationship is similar for links (4,5) and (5, 6), and therefore only link (5,6) will be considered in the model development. The e ffects of the vehicular lane distributions immediately before and after a work z one sign will thus be considered in the final models. In model development, there was some interaction be tween the distribution of vehicles in link (6,7) and the sign distance (this is also the length of link (6.7)). With an increasing sign distance, a higher fraction of the traffic stream is present in the through lane (lane 1) while a lower fraction is in the clos ed lane. Longer warning distances

PAGE 60

50 upstream of a lane closure allow vehicles more time and space to merge into the through lane. This relationship is illustrated below. Lane Distributions of Link (6,7) vs. Upstream Sign Location0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 00.20.40.60.811.21.41.6 Upstream Sign Location (mi) Lane Distributions of Link (6,7) Lane 1 Lane 2 Linear (Lane 1) Linear (Lane 2) Figure 4 9. Relationship between vehicular lane distributions in lanes one and two of link (6,7) and the location of the upstream warning sign The interaction of these two terms—lane distributio ns of link (6,7) and upstream sign distance—were plotted against capacity to veri fy that a relationship existed. These results are illustrated below.

PAGE 61

51 Work Zone Capacity vs. Lane 1 Distribution in Link (6,7) and Sign Distancey = -1194.5x + 1977.7 R2 = 0.3722 0 500 1000 1500 2000 2500 00.10.20.30.40.50.60.7 Lane 1 Distribution (6,7) Sign Distance (mi)Work Zone Capacity (veh/hr/ln) Figure 4 10. Relationship between work zone capa city and the interaction of lane distributions in link (6,7) and upstream sign dista nce As a result, the net effect of increasing sign dist ance and lane distribution is negative for lane one. This factor is included in the final model. Another interaction was observed between the locati on of upstream warning sign and the average speeds of vehicles in link (6,7). Their combined effect on capacity is shown below in Figure 4 – 11.

PAGE 62

52 Work Zone Capacity vs. Speeds in Lane 1 (6,7) and S ign Distancey = 4.8857x + 1416.9 R2 = 0.2341 0 500 1000 1500 2000 2500 020406080100120 Speeds in Lane 1 (6,7) Sign Distance (mph*mi)Work Zone Capacity (veh/hr/ln) Figure 4 11. Relationship between the work zone capacity and the interaction of the speeds in lane 1 of link (6,7) and the location of the upstream warning sign The results indicate that there is a relationship b etween these variables, and the interaction of speeds and sign distance is consider ed in the final model. Another important interaction between variables was observed between the average speeds in lane 1 and lane 2 of both links (5,6) and (6,7). As shown in the following figures, the two lanes’ speeds are highly dependent on each other; as a result, both lanes’ speeds cannot be used together in the model. There fore, a polynomial regression—of order 2—was performed on each link. The resulting equations predict the speeds of lane 2 for each link from the lane 1 speed data. These relationships are shown in the following figures, Figure 4 – 12 and .4 – 13.

PAGE 63

53 Speeds in Link (5,6)y = 0.0188x2 0.3209x + 10.532 R2 = 0.9893 R = 0.9946 0 10 20 30 40 50 60 70 010203040506070 Lane 1 (mph)Lane 2 (mph) Figure 4 12. Relationship between the speeds in lane 1 and lane 2 of link (5,6) Speeds in Link (6,7)y = 0.0143x2 0.5816x + 9.2131 R2 = 0.8541 R = 0.9242 0 10 20 30 40 50 010203040506070 Lane 1 (mph)Lane 2 (mph) Figure 4 13. Relationship between the speeds in lane 1 and lane 2 of link (6,7)

PAGE 64

54 From the results above, the polynomial expressions were used to calculate the predicted values of the speeds in lane 2, and those predicted values were used in model development when both lanes were considered. 3-to-2 Lane Closure Model Development For this test segment, a three-lane freeway segment is reduced to two lanes with the rightmost lane closed. The input variables and per formance measures discussed in the 2to-1 model development were plotted against the val ue of capacity through the lane closure. The graphical representations of these re lationships are shown in APPENDIX A: Model Development Relationships for a 3-to-2 Lane C losure Configuration. The progression of relationships follows that of the 2to-1 lane closure configuration and the same variables will be considered in the final mode l development. 3-to-1 Lane Closure Model Development For this test segment, a three-lane freeway segment is reduced to one lane with the two rightmost lanes closed. The graphical represen tations of these relationships are shown in APPENDIX B: Model Development Relationships for a 3-to-1 Lane C losure Configuration. Final Models This section presents the final models for each of the three lane closure configurations, with all variables within a 0.05 le vel of significance. The models are based upon the relationships described in previous sections, with work zone capacity through each lane closure as the dependent variable One model that can be used to predict capacity was developed for each of the thre e lane closure configurations studied. The values of vehicle speeds and distributions in t he closed lanes are the primary inputs into the models. Sign distance, truck percentage, and rubbernecking factor also

PAGE 65

55 contribute to the final value of work zone capacity Speeds and lane distributions are performance measures that can be controlled in a wo rk zone. Signs or physical barriers can encourage or require lane merges, maintaining t raffic stream speeds and/or vehicle travel lanes. Therefore, having both of these fact ors present in the models provides a view of the effect of different types of management strategies to be imposed through the work zone. Variable Explanations and Example of Model Usage The variables used in the developed models are defi ned in detail below. Their limitations in range, based on collected data, and an example of capacity calculations for each of the lane closure configurations are reporte d in APPENDIX E. Capacity This is the dependent variable and represents th e maximum number of vehicles that travel through the work zone lane clo sure given specific input values for the model parameters. This traffic stream condition is a result of a drop in free-flow speed of at least 30% in the link immediately upstream of th e work zone. This value is given in units of veh/hr/lane, and for the 3-to-2 lane closu re configuration, this value is the average of both open lanes. Intercept This is the value that is being adjusted by othe r parameters in each of the models. By itself, it is not an estimate of the ba se capacity of any of the lane configurations because inputting zeros for all othe r parameters is not reasonable. The unit of this variable is veh/hr/lane. SignDist This variable represents the upstream distance o f the work zone warning sign. Because this variable is always the same len gth as link (6,7), some interactions have been noted and accounted for in the model deve lopment. This variable is input into the model as miles.

PAGE 66

56 Truck% This variable represents the percentage of heavy vehicles in the traffic stream. It is input into the model as a whole numb er (e.g. 20 for 20%). Rubber% This variable represents the degree to which cap acity is reduced due to any additional factors within the lane closure. A higher rubbernecking factor leads to a decrease in capacity throughput, and it is input in to the model as a whole number (e.g. 20 for 20%). The value of this variable should be cho sen carefully, since field data has not yet been acquired to properly calibrate its effect. With a zero percent rubbernecking factor, no additional events are causing capacity r eduction other than the geometry of the work zone and factors discussed previously. Howeve r, it may be possible that there is a presence of law enforcement or heavy construction e quipment and workers, which would lead to additional decrease in capacity as drivers react to the more hazardous driving conditions. Hence a rubbernecking factor ranging f rom 5 to 25 percent may be used to simulate this additional reduction in capacity. SpdLan1(5,6 ) This variable represents the speed of vehicles i n lanes 1 of link (5,6) (lane 1 being the rightmost lane). The units of this variable are in mph and should be input into the model as such. SpdLan1(5,6) SignDist This variable is an interaction term between the speed of vehicles in lane 1 of link (5,6) and the upstream d istance of the work zone warning sign. This interaction is a result of the following logic : scenarios that have shorter sign distances have higher likelihoods of producing queu es extending beyond the warning sign into link (5,6). If queues in a particular la ne extend into link (5,6) then the average speeds per vehicle are thus affected (reduced) at t his link.

PAGE 67

57 CalcSpdLan2(5,6) There is a strong correlation between the actual speeds of lane 1 and lane 2 for link (5,6). This variable is a po lynomial regression of order 2 that estimates the speeds in lane 2 of link (5,6) using the speeds in lane 1 of link (5,6) as an input. SpdLan1(6,7), SpdLan2(6,7) These variables represent the speeds in lanes 1 and 2 of link (6,7) (lane 1 being the rightmost lane). T he units of these variables are in mph and should be input into the models as such. SpdLan1(6,7) SignDist, SpdLan2(6,7) Sign Dist These variables are interaction terms between the speed of vehicles in lanes 1 and 2 of link (6,7) and the upstream distance of the work zone warning sign. T his interaction is a result of scenarios that have shorter sign distances have higher likeli hoods of producing queues extending beyond the warning sign into the upstream link (5,6 ). If queues in a particular lane extend into link (5,6) then the average speeds per vehicle are thus affected (reduced) at this link. CalcSpdLan2(6,7) There is a strong correlation between the actual speeds of lane 1 and lane 2 for link (6,7). This variable is a po lynomial regression of order 2 that estimates the speeds in lane 2 of link (6,7) using the speeds in lane 1 of link (6,7) as an input. CalcSpdLan3(6,7) There is a strong correlation between the actual speeds of lane 2 and lane 3 for link (6,7). This variable is a po lynomial regression of order 2 that estimates the speeds in lane 3 of link (6,7) using the speeds in lane 2 of link (6,7) as an input.

PAGE 68

58 DistrLan1(6,7) DistrLan2(6,7) These variables represent the fraction (percent divided by 100) of vehicles present in lane 1 of li nk (6,7). For example, if 10% of vehicles are traveling in lane 1, the input value w ould be 0.10 into the model for this variable. DistLan1(6,7) SignDist DistLan2(6,7) SignDist These variables are interaction terms between the distribution of vehic les in lanes 1 and 2, respectively, of link (6,7) and the upstream distance of the work zo ne warning sign. The interaction is a result of the relative ease for vehicles to merge i nto the non-closed lane, given specific link lengths. A larger sign distance creates more space for vehicles to merge into the through lane(s). The sign distance is input in uni ts of miles, and the lane distributions as a decimal ( e.g. 0.1, 0.5, etc. ). Capacity Estimation Models for each Lane Closure Co nfiguration The following sections present the final models for each lane closure configuration. For actual STATISTICA output screen shots (includin g p-stats and t-stats ), please see APPENDIX D. 2-to-1 lane closure configuration The following model estimates the capacity for a 2to-1 lane closure configuration (Equation 4-1). The dependent variable, Capacity2to1 represents the number of vehicles per hour per lane that travel through the open lane given a set of input values. The model is shown below:

PAGE 69

59 Capacity2to1 = 1623.02 + (740.88) SignDist + (-14.23) Truck% + (-22.83) Rubber% + (-409.76) DistrLan1(6,7) SignDist + (-13.513) SpdLan1(6,7) SignDist + (26.69) CalcSpdLan2(6,7) (Eq. 4-1) The adjusted R2 value for the relationship in Equation 4-1 is 0.971 The variable CalcSpdLan2(6,7) is calculated from the input variable SpdLan1(6,7) : CalcSpdLan2(6,7) = 0.0143 ( SpdLan1(6,7) )2 – 0.5816 SpdLan1(6,7) + 9.213 (Eq. 4-2) The R value for the relationship in Equation 4-2 is 0.9242. 3-to-2 lane closure configuration The following model estimates the capacity for a 3to-2 lane closure configuration (Equation 4-3). The dependent variable, Capacity3to2Avg represents the average number of vehicles per hour per lane that travel th rough the work zone, given a set of input values. To calculate the total number of veh icles per hour through the work zone, the Capacit3to2Avg value should be multiplied by two. The model is s hown below:

PAGE 70

60 Capacity3to2Avg = 1595.84 + (711.49) SignDist + (-5.87) Truck% + (-17.88) Rubber% + (-1211.94) DistrLan1(6,7) SignDist + (-10.58) SpdLan1(5,6) SignDist + (9.30) CalcSpdLan2(5,6) (Eq. 4-3) The adjusted R2 value for the relationship in Equation 4-3 is 0.954 The variable CalcSpdLan2(5,6) is calculated from the input variable SpdLan1(5,6) : CalcSpdLan2(6,7) = 0.0188 ( SpdLan1(5,6) )2 – 0.3209 SpdLan1(5,6) + 10.532 (Eq. 4-4) The R value for the relationship in Equation 4-4 is 0.9946. 3-to-1 lane closure configuration The following model estimates the capacity for a 3to-1 lane closure configuration (Equation 4-5). The dependent variable, Capacity3to1 represents the average number of vehicles per hour per lane that travel through Lane 3, given a set of input values. The model is shown below.

PAGE 71

61 Capacity3to1 = 1665.42 + (763.56) SignDist + (-10.12) Truck% + (-20.07) Rubber% + (-1698.76) DistrLan1(6,7) SignDist + (-626.50) DistrLan2(6,7) SignDist + (-13.513) SpdLan2(6,7) SignDist + (26.84) CalcSpdLan3(6,7) (Eq. 4-5) The adjusted R2 value for the relationship in Equation 4-5 is 0.976 The variable CalcSpdLan3(6,7) is calculated from the input variable SpdLan2(6,7) : CalcSpdLan3(6,7) = 0.0136 ( SpdLan2(5,6) )2 – 0.5144 SpdLan2(5,6) + 8.108 (Eq. 4-6) Discussion of Results Three models were developed for each of the three l ane closure configurations on a temporary freeway work zone. The parameters select ed are easily visualized and can be efficiently collected from the field. The first se ction below discusses the effect of the three models’ variables on the capacity throughput of the work zone lane closure. The second section describes the way in which the model s can be applied by the FDOT and the assumptions that must be made in using the capa city estimates.

PAGE 72

62 Effects of Model Variables on Capacity The relationship of the link (5,6) speeds to capaci ty is important because the vehicles in the traffic stream at this point have n ot yet seen the work zone warning sign, which is located at node 6. The speeds in lane 1 ( actual) and 2 (predicted) of link (5,6) was modeled for the 3-to-2 lane closure configurati on. Lane 2 is the first open through lane (merging from right to left) of the work zone and lane 1 is the lane immediately to the right of that (the lane from which vehicles are merging). The model parameters show that for link (5,6), higher speeds in the to-be thr ough lane lead to an increase in capacity and thus lower speeds lead to lower values of capac ity. Higher speeds imply that the queue created at the lane closure does not extend u pstream of the warning sign and vehicles are merging and passing through the work z one smoothly. Higher speeds on the to-be closed lane, however, lead directly to a decr ease in capacity. This relationship is also a function of queue length growth on the throu gh lane. Depending on the level of congestion (defined by the other user inputs), a qu eue may also form on the merging lane. Because vehicles are merging into the through lane once they pass the warning sign, the queue will grow much faster on the through lane tha n on the merging lane. This may cause vehicles upstream in the to-be through lane t o slow down (lower speeds lead to a lower capacity) while vehicles in the to-be closed lane are maintaining free flow speed. Thus, high speeds in the to-be closed lane lead to quick queue buildup in the through lane and the congestion leads to a lower capacity value. Lower speeds in the to-be closed lane imply that the queue has extended beyond the warnin g sign and all lanes are congested, thus also leading to a reduction in capacity by bot h speed parameters. The 3-to-2 lane closure configuration was the only one that was mod eled using the speeds in link (5,6). This is because the work zone has two open through lanes, and the vehicular behavior

PAGE 73

63 was more correlated to that of vehicles before they first encounter the work zone warning sign. Vehicles can make discretionary lane changes in this work zone configuration, as they can in the upstream link (5,6). The other two lane closure configurations, 2-to-1 and 3-to-1, are modeled using the speeds in link (6,7). This is the link after the vehicles have seen the warning sign and are required to make a me rging maneuver as soon as possible. The speeds in link (6,7) were more closely correlat ed to the behavior of vehicles in the work zones where only one through lane was open. T he parameter estimates suggest that the same trend exists that was observed in the upst ream link (5,6). Higher speeds in the to-be closed lanes lead to a decrease in capacity, while higher speeds in the to-be open lanes lead to an increase in work zone capacity. T he speeds in the lanes considered are correlated (see Figure 4 – 13), and speeds as high as 50 mph on the to-be closed lane show speeds of less than 20 mph on the to-be open l ane. Therefore, high speeds of the vehicles in the to-be closed lane are merging into a congested to-be through lane in which the speeds are not high, thus overall reducing capa city through the work zone. Capacity is lower because of the creation of a shockwave on the through lane every time a rapidly moving vehicle slows down after merging into the th rough. This does not lead to a constant queue discharge on the through lane. When vehicle speeds are higher than 50 mph on the to-be closed lane, however, vehicle spee ds on the through lane are also higher, indicating a much smoother merging pattern and thus overall increasing capacity (with the increasing speeds in the to-be closed lan e). The second relationship developed is between work z one capacity and the vehicular lane distributions upstream of the work zone. For all lane closure configurations, the model parameters show that for link (6,7), a larger fraction of vehicles in the to-be closed

PAGE 74

64 lane(s) leads to a decrease in capacity. This effe ct is a result of the quantity of merging maneuvers occurring after the warning sign. A high er fraction of vehicles in the to-be closed lane(s) leads to a higher required number of merging maneuvers. A lower number of merging maneuvers is a result of a higher fracti on of vehicles in the to-be through lane(s)—and thus a lower fraction in the to-be clos ed lane(s)—which leads directly to a steady traffic stream moving more efficiently throu gh the work zone. A higher number of merging maneuvers leads to an increase in the am ount of shockwaves present in the tobe through lane; these shockwaves disrupt the stead y discharge of the queue into the work zone and reduce the work zone capacity. In addition to the speed and lane distribution rela tionships, the distance of the warning sign, the presence of trucks, and a rubbern ecking factor also contribute an effect to capacity. The distance of the upstream warning sign increases the work zone capacity with an increasing distance. This variable has som e interaction with the lane distributions and speed values, and is thus implemented in the mo dels as an interaction term. The increasing effect that warning sign distance has on capacity, however, is not greater than the decreasing effect of the lane distribution fact or or of the speed factor. Thus the interaction of the two terms leads to a net decreas ing effect on capacity, as seen in the models’ negative interaction term coefficients. As the models’ parameters illustrate, a higher percentage of trucks present in the traffic stream leads to a decrease in capacity. The greatest decrease in capacity due to truck pres ence occurs for the 2-to-1 lane configuration; this is a result of the simulation s cenarios having been set up with trucks biased to travel in the rightmost lane. Because th ere are only two travel lanes in this configuration, the truck presence has a strong effe ct. The smallest decrease in capacity is

PAGE 75

65 observed with the 3-to-2 lane closure configuration Again, because trucks are biased to the rightmost lane, all trucks entering link (6,7) will be present in lane 1. However, because there are three travel lanes and two of the m are open, the effect of truck presence is not as pronounced as with the 2-to-1 or even 3-t o-1 configurations. A rubbernecking factor ranging from 0 to 25% also reduces the capac ity through the work zone in all three lane closure configurations. The reduction is achi eved by the software by increasing time headways between vehicles traveling through the wor k zone, and the factor is included to simulate any additional reason for a capacity reduc tion. Model Application by the FDOT The current method employed by the FDOT considers o nly the geometric characteristics of a potential work zone in order t o estimate the capacity. The new models developed consider some geometric characteri stics as well as traffic operating conditions. These models are intended to be used a s tools to estimate the capacity of a work zone lane closure, given a set of geometric an d traffic operating inputs with the work zone already in place. To use the models effe ctively, the geometric and operational conditions specified in capacity estimation should be implemented and regulated in the field. The models developed for the new methodolog y require the input of some geometric factors that are obtainable by engineers before the work zone is built, based on the collection of field data. These are listed bel ow: Distance of the upstream warning sign Truck percentage – this value can be obtained for d ifferent times of day Rubbernecking factor – this value is a result of th e quantity of workers and/or heavy equipment present throughout the work zone

PAGE 76

66 The new models estimate a capacity value based on a combination of the above factors and additional factors that are not obtaina ble before the work is built. These are listed below: Vehicle lane distributions upstream of the work zon e lane closure Average speed per vehicle upstream of the work zone lane closure Because the current FDOT methodology uses only geom etric factors, these can be specified and a value for capacity is estimated bef ore the work zone is built. With the new models, the engineers should obtain the data fo r truck percentages and decide on the location of the upstream warning sign and rubbernec king factors. When inputting the values for the lane distributions and speeds, howev er, the user should be aware of several issues. First, the speeds and lane distributions a re the values for the work zone once it is already in place. These are not the values for the regular operating conditions observed with no work zones in place. Second, the models we re developed based on simulated data that ensured breakdown and the creation of a b ottleneck at the location of the lane closure. Therefore, the capacity estimates resulti ng from the models are for operating conditions where a queue is formed at the start of the lane closure on the through lane(s). Finally, the lane distributions and speed values in put into the models for capacity estimation must be maintained when constructing the work zone. Otherwise, the capacity values estimated by the models will not be observed in the field operations. For example, if a Lane 1 average speed of 30 mph is used when es timating capacity, the speed for that link of actual roadway should be enforced at 30 mph for the capacity estimate to replicated in the field. Speed limit signs and law enforcement can help to encourage the desired traffic stream behavior. The same holds tr ue for the lane distributions inputs. If

PAGE 77

67 the models are applied with a Lane 1 vehicular dist ribution of 10%, for example, then that value should be enforced in the field as well. Signs encouraging early merging or even the use of additional barriers can help achiev e the desired distribution of vehicles.

PAGE 78

68 CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS This section identifies the primary results and con clusions from the analyses presented in this thesis. The effects of the model factors on work zone capacity are summarized and discussed. In addition, the reasons for the differences in capacity estimation between models and between lane closure configurations are outlined. The models developed are only one step toward a full un derstanding of the effects of specific factors on work zone capacity. For this reason, re commendations for further investigation are also presented following the conc lusions. Conclusions There are several factors that were identified to h ave significant effects on the capacity of a temporary freeway work zone lane clos ure. The percentage of trucks present in the traffic stream and the rubbernecking factor both decrease the capacity of the lane closure. The rubbernecking factor has a g reater effect on capacity reduction than the presence of trucks—represented by the larger (m ore negative) coefficients for this factor. Therefore, when considering lane closure s chedules during peak hour traffic (high volumes of vehicles), times of day (or night) shoul d be avoided when high percentages of trucks in the traffic stream are present. In addit ion, consideration should be given to the quantity of workers and presence of heavy equipment and/or law enforcement vehicles in the work zone. A large presence of a combination o f these factors will contribute to a high rubbernecking factor, thus reducing capacity f urther. To be able to correlate a

PAGE 79

69 specific rubbernecking factor with a level of worke r/equipment presence, field data for this variable should be collected and analyzed. The average of speeds of vehicles directly upstream of the work zone warning sign had varying effects by lane. As noted in CHAPTER 4 : MODEL DEVELOPMENT, the speeds of lane 2 are correlated to those in lane 1, and the speeds in lane 3 are correlated to those in lane 2 for congested conditions. A higher capacity is observed with higher speeds in the to-be through lanes, and a lower capa city with higher speeds in the to-be closed lanes. As a result, in order to increase th e efficiency of a work zone lane closure, higher speeds—in the range of 25 to 45 mph—should b e maintained (upstream and downstream of the warning sign) through the lanes t hat are not closed. The model results also show the effect of vehicle l ane distributions downstream of the location of the warning sign. There is a stron g reduction in capacity if a high fraction of vehicles are present in the to-be closed lane(s) This is an important conclusion when considering traffic management upstream of a lane c losure on a freeway. A work zone will operate much more smoothly—and thus have a gre ater capacity—if vehicles are encouraged (or required) to merge into the through lane at a greater distance upstream from the location of the warning sign. The location of the upstream warning sign increases capacity throughput with an increasing warning distance for all lane closure co nfigurations. In all models, this variable is included both as a stand-alone variable and as an interaction term. As an interaction term, however, it’s positive effect is not greater than the negative effect of the variable with which it’s interacting, so the net ef fect of the variables together is a decrease in capacity. These interaction variables included the speeds of vehicles in link

PAGE 80

70 (5,6) (upstream of the warning sign), the speeds of vehicles in link (6,7), and the lane distributions of vehicles in link (6,7). As a resu lt, a lane closure configuration will have greater capacity throughput if advance warning is g iven to the vehicles in the traffic stream. Recommendations Strong relationships between work zone capacity and upstream speeds and upstream lane distributions were identified and pre sented in model form in this report. The relative differences in capacity provided by di fferent input traffic stream scenarios are valuable but do not represent actual field capa cities. Presented in this section are recommendations to calibrate the existing models an d to conduct further research in the direction of early merge traffic management strateg ies. Calibration Between lane closure configurations, there are expe cted differences in capacity results for the same input values into the models ( see APPENDIX E). The variables have the same effect on capacity (increase or decrease), but the relative effect (value of coefficient) of each factor is different. This rel ative difference is valuable information and can be used for capacity comparisons for differ ent inputs. Even more valuable is the ability to estimate actual capacity values for the different lane closure configurations. In order to achieve actual capacity estimates, however field data for each scenario must be collected to accurately calibrate the models so tha t it replicates actual traffic stream conditions. The way in which vehicles behave on a freeway and w ith a lane closure present is likely not the same as the default values of a simu lator for this behavior. As a result, field data for vehicular lane changing behavior and drivi ng speeds is fundamental to model

PAGE 81

71 calibration. Car-following parameters can also be adjusted within the software, so this data will also have some impact on the actual capac ity values. In addition to driver behavior, the rubbernecking f actor requires calibration. For each field data collection scenario, information sh ould be collected regarding the presence of additional obstructions or congestion w ithin the work zone (degree of presence of law enforcement, heavy equipment, and w orkers). The data can be modeled and a table can be developed that correlates a defi nite rubbernecking factor with a specified scenario of field conditions. If there a re many variables that affect the rubbernecking factor, then a full model can be used in which the user specifies the inputs and a rubbernecking factor is estimated. These fac tors will contribute to a reduction in capacity represented by the rubbernecking factor in the models. Thus, the field data will lead to a rubbernecking range defined to specific f ield conditions. Without calibration, the output values reported by the models will be approximately 10 to 20 percent too high, as typical work zone base capacities range from 1400 to 1700 passenger cars per hour per lane. The default driver behavior values are the primary cause for this overestimate. These default values applied by the software CORSIM .1 are chosen to maximize the flow through a ny facility, and any modification will lead to a decrease in the output values (Schne ll and Aktan, 2001). Future Research and Applications The effects of traffic stream speeds and vehicle la ne distributions on capacity can be considered in the selection of work zone traffic management strategies for lane closures. With these results, further research is recommended with each of the scenarios in order to determine their effectiveness as recomm endations for management strategies. The models give relative capacity estimates for dif ferent inputs, so it is a simple task to

PAGE 82

72 identify the optimal flow conditions that maximize the capacity throughput. However, it is a more difficult task to implement these optimiz ed conditions in the field. An important work zone consideration that is ignored b y the models is safety, for example. High speeds may be ideal but not feasible. A high fraction of vehicles in the through lane will increase capacity, but this also leads to more lane changes far distances upstream of the work zone and thus more of a possibility of acc idents due to the higher operating speeds at that location. In addition to safety con cerns, there also exist implementation difficulties with enforcement of specific condition s. If a predicted capacity is estimated based on a desired traffic stream speed of 30 mph, for example, then some kind of enforcement is required to ensure that this is the speed occurring in the field. The same difficulty exists with the enforcement of a desired lane distribution, when estimating a capacity value for a lane configuration. The results of the lane distribution effects on cap acity imply that an early merge strategy is effective in increasing the flow of the traffic stream through a work zone. However, this may only be effective (or possible) w hen there are few trucks, a low rubbernecking factor, and a sign location greater t han 0.5 miles, for example. The effectiveness of an early merge has been investigat ed in some states, and future research will allow a clearer understanding of when to use t his strategy. The goal of using an early merge strategy is to pla ce as many vehicles as possible into the through lane far distances upstream of the work zone. The possibility to model this scenario with CORSIM 5.1 is limited; the work zone sign leads vehicles to merge, but the bottleneck is still created at the point of the lane closure, as would be expected in actual conditions. With CORSIM 6.0 (not released i n its full version until the end of

PAGE 83

73 April 2006), at the point of an incident, vehicles begin merging very aggressively at the location of the incident warning sign. This way, t he bottleneck forms around the sign location, not at the location of the lane closure. The aggressive merging movements and the creation of the bottleneck around the warning s ign is intended to maximize the throughput capacity in the work zone (see APPENDIX C for CORSIM 5.1 and 6.0 output comparisons for the same input file). Therefore, e ven though this behavior is not expected in field conditions, CORSIM 6.0 can be use d to vary the locations of the bottleneck formations upstream of the work zone, ef fectively simulating an early merge scenario. CORSIM 6.0 places over 95% of the vehicle s in the through lane just before the lane closure location, whereas CORSIM 5.1, dependin g on the flow rate and truck percentage, places anywhere from 50% to 90% in the through lane (see APPENDIX C for output data from each version). Because the go al of the research, in part, is to verify the relationship between the upstream distance of t he warning sign and the work-zone throughput capacity, the algorithm used in CORSIM 6 .0 would skew these results. For future early merge research, however, this may prov e to be a valuable tool. As stated previously in this thesis, simulation mod eling cannot provide as much information as field data collection. The relative capacities provided by the models, however, provide valuable information to a user int erested in optimizing the efficiency of a lane closure operation. The speeds and lane dist ributions upstream of a warning sign have an effect on capacity throughput and this info rmation can be used in scheduling and managing a temporary freeway lane closure.

PAGE 84

74 APPENDIX A MODEL DEVELOPMENT RELATIONSHIPS FOR A 3-to-2 LANE C LOSURE CONFIGURATION Work Zone Capacity vs. Upstream Sign Locationy = 27.64x + 1523 R2 = 0.0019 1000 1200 1400 1600 1800 2000 2200 2400 00.20.40.60.811.21.41.6 Upstream Sign Location (mi)Work Zone Capacity (veh/hr/ln)

PAGE 85

75 Work Zone Capacity vs. Truck Percentagey = -10.44x + 1655 R2 = 0.1085 1000 1200 1400 1600 1800 2000 2200 2400 0510152025 Truck Percentage Work Zone Capacity (veh/hr/ln) Work Zone Capacity vs. Rubbernecking Factory = -22.082x + 1845.1 R2 = 0.7689 1000 1200 1400 1600 1800 2000 2200 2400 051015202530 Rubbernecking Factor (%)Work Zone Capacity (veh/hr/ln)

PAGE 86

76 Work Zone Capacity vs. Lane Changes in (6,7)y = 0.6724x + 1115.5 R2 = 0.5246 1000 1200 1400 1600 1800 2000 2200 2400 0200400600800100012001400 Lane Changes in (6,7)Work Zone Capacity (veh/hr/ln) Lane Changes in Link (6,7) vs. Length of Link (6,7)y = 436.49x + 210.61 R2 = 0.4089 0 200 400 600 800 1000 1200 1400 00.20.40.60.811.21.41.6 Length of Link (6,7) (mi)Number of Lane Changes in Link (6,7)

PAGE 87

77 Work Zone Capacity vs. Speed in link (5,6)1000 1200 1400 1600 1800 2000 2200 2400 010203040506070 Speed in link (5,6) (mph)Work Zone Capacity (veh/hr/ln) Lane 1 Lane 2 Lane 3 Linear (Lane 2) Linear (Lane 1) Linear (Lane 3) Work Zone Capacity vs. Speed in link (6,7)y = 31.454x + 1266.4 R2 = 0.6739 y = 11.057x + 1266.3 R2 = 0.5726 y = 39.235x + 1025.9 R2 = 0.8329 1000 1200 1400 1600 1800 2000 2200 2400 2600 010203040506070 Speed in link (6,7) (mph)Work Zone Capacity (veh/hr/ln) Lane 1 Lane 2 Lane 3 Linear (Lane 2) Linear (Lane 1) Linear (Lane 3)

PAGE 88

78 Work Zone Capacity vs. Lane Distributionsy = -2189.4x + 2121.2 R2 = 0.3092 1000 1200 1400 1600 1800 2000 2200 2400 00.050.10.150.20.250.30.350.4 Lane Distributions of Lane 1 (closed lane)Work Zone Capacity (veh/hr/ln) Link (6,7) Link (5,6) Link (4,5) Linear (Link (6,7)) Linear (Link (4,5)) Linear (Link (5,6)) Lane Distributions of Link (6,7) vs. Upstream Sign Location0 0.1 0.2 0.3 0.4 0.5 0.6 00.20.40.60.811.21.41.6 Upstream Sign Location (mi) Lane Distributions of Link (6,7) Lane 1 Lane 2 Lane 3 Linear (Lane 3) Linear (Lane 2) Linear (Lane 1)

PAGE 89

79 Work Zone Capacity vs. Lane 1 Distribution (6,7) an d Sign Distancey = -971.7x + 1711.6 R2 = 0.192 0 500 1000 1500 2000 2500 00.10.20.30.40.5 Lane 1 Distribution (6,7) Sign Distance (mi)Work Zone Capacity (veh/hr/ln) Work Zone Capacity vs. Speed in Lane 1 (6,7) and Si gn Distancey = 5.0755x + 1309.3 R2 = 0.2835 0 500 1000 1500 2000 2500 020406080100120 Speed in Lane 1 (6,7) Sign Distance (mph*mi)Work Zone Capacity (veh/hr/ln)

PAGE 90

80 Speeds in Link (5,6)y = 0.0087x2 + 0.4493x + 0.6699 R2 = 0.9937 R = 0.9968 0 10 20 30 40 50 60 70 010203040506070 Lane 1 (mph)Lane 2 (mph) Speeds in Link (6,7)y = 0.0082x2 0.1876x + 5.9044 R2 = 0.9378 R = 0.9684 0 5 10 15 20 25 30 35 010203040506070 Lane 1 (mph)Lane 2 (mph)

PAGE 91

81 APPENDIX B MODEL DEVELOPMENT RELATIONSHIPS FOR A 3-TO-1 LANE C LOSURE CONFIGURATION Work Zone Capacity vs. Upstream Sign Locationy = 12.236x + 1579.5 R2 = 0.0003 1000 1200 1400 1600 1800 2000 2200 2400 260000.20.40.60.811.21.41.6Upstream Sign Location (mi)Work Zone Capacity (veh/hr/ln)

PAGE 92

82 Work Zone Capacity vs. Truck Percentagey = -14.614x + 1737.8 R2 = 0.1542 1000 1200 1400 1600 1800 2000 2200 2400 2600 0510152025 Truck PercentageWork Zone Capacity (veh/hr/ln) Work Zone Capacity vs. Rubbernecking Factory = -24.29x + 1915.6 R2 = 0.6745 1000 1200 1400 1600 1800 2000 2200 2400 2600 051015202530 Rubbernecking Factor (%)Work Zone Capacity (veh/hr/ln)

PAGE 93

83 Work Zone Capacity vs. Lane Changes in (6,7)y = 2.709x + 173.85 R2 = 0.3703 1000 1200 1400 1600 1800 2000 2200 2400 2600 0100200300400500600700 Lane Changes in (6,7)Work Zone Capacity (veh/hr/ln) Lane Changes in Link (6,7) vs. Length of Link (6,7)y = 107.96x + 415.42 R2 = 0.417 0 100 200 300 400 500 600 700 00.20.40.60.811.21.41.6 Length of Link (6,7) (mi)Number of Lane Changes in Link (6,7)

PAGE 94

84 Work Zone Capacity vs. Speed in link (5,6)1000 1200 1400 1600 1800 2000 2200 2400 2600 010203040506070 Speed in link (5,6) (mph)Work Zone Capacity (veh/hr/ln) Lane1 Lane 2 Lane 3 Linear (Lane 3) Linear (Lane 2) Linear (Lane1) Work Zone Capacity vs. Speed in link (6,7)y = 25.667x + 1308.4 R2 = 0.7924 y = 10.211x + 1283.4 R2 = 0.5011 y = 3.7751x + 1420.7 R2 = 0.0912 1000 1200 1400 1600 1800 2000 2200 2400 2600 010203040506070 Speed in link (6,7) (mph)Work Zone Capacity (veh/hr/ln) Lane 1 Lane 2 Lane 3 Linear (Lane 3) Linear (Lane 2) Linear (Lane 1)

PAGE 95

85 Work Zone Capacity vs. Lane Distributionsy = -1440.1x + 2142.4 R2 = 0.4941000 1200 1400 1600 1800 2000 2200 2400 2600 00.10.20.30.40.50.60.70.8Lane Distributions of Lanes 1 and 2 (closed lanes)Work Zone Capacity (veh/hr/ln) Link (4,5) Link (5,6) Link (6,7) Linear (Link (6,7)) Linear (Link (5,6)) Linear (Link (4,5)) Lane Distributions of Link (6,7) vs. Upstream Sign Location-0.2 0 0.2 0.4 0.6 0.8 1 00.20.40.60.811.21.41.6 Upstream Sign Location (mi) Lane Distributions of Link (6,7) Lane 1 Lane 2 Lane 3 Linear (Lane 3) Linear (Lane 2) Linear (Lane 1)

PAGE 96

86 Work Zone Capacity vs. Lane 1 Distribution (6,7) an d Sign Distancey = -4618.4x + 1724 R2 = 0.1779 0 500 1000 1500 2000 2500 00.020.040.060.080.1 Lane 1 Distribution (6,7) Sign Distance (mi)Work Zone Capacity (veh/hr/ln) Work Zone Capacity vs. Lane 2 Distribution (6,7) an d Sign Distancey = -1157.8x + 1964.8 R2 = 0.3614 0 500 1000 1500 2000 2500 00.10.20.30.40.50.60.7 Lane 2 Distribution (6,7) Sign Distance (mi)Work Zone Flow (veh/hr/ln)

PAGE 97

87 Work Zone Capacity vs. Speed in Lane 2 (6,7) and Si gn Distancey = 4.7924x + 1420.5 R2 = 0.23180 500 1000 1500 2000 2500020406080100120 Speed in Lane 2 (6,7) Sign Distance (mi)Work Zone Capacity (veh/hr/ln) Speeds in Link (5,6)y = 0.0529x2 3.8355x + 93.881 R2 = 0.9475 R = 0.97340 10 20 30 40 50 60 70010203040506070 Lane 2 (mph)Lane 3 (mph)

PAGE 98

88 Speeds in Link (6,7)y = 0.0136x2 0.5144x + 8.1082 R2 = 0.8497 R = 0.9218 0 10 20 30 40 50 010203040506070 Lane 2 (mph)Lane 3 (mph)

PAGE 99

89 APPENDIX C SAMPLE OUTPUT FILES FROM CORSIM 5.1 AND CORSIM 6.0 The outputs shown below illustrate the differences between two identical input scenarios simulated by two different versions of CO RSIM. The two versions being compared are the current one being used in this rep ort, CORSIM 5.1, and a Beta version to be released in April 2006, CORSIM 6.0. Sample Output from CORSIM 5.1 On the following pages is a sample output for the f ollowing scenario (base case): 2-to-1 Lane Closure Work Zone Length at 0.5 miles Input Lane Distributions at 50/50 Sign Distance at 0.5 miles Truck Percentage at 0% Rubbernecking Factor at 0%

PAGE 100

90 INPUT FILE NAME: S:\Students\Diego_Arguea\Research\ CORSIM\2 to 1 La RUN DATE : 01/16/06 TTTTTTTTTTT RRRRRRRRR AAAAAAA FFFFFFFFFFF TTTTTTTTTTT RRRRRRRRRR AAAAAAAAA FFFFFFFFFFF TTTTTTTTTTT RRRRRRRRRRR AAAAAAAAAAA FFFFFFFFFFF TTT RRR RRR AAA AAA FFF TTT RRR RRR AAA AAA FFF TTT RRRRRRRRRRR AAAAAAAAAAA FFFFFFF TTT RRRRRRRRRR AAAAAAAAAAA FFFFFFF TTT RRR RRR AAA AAA FFF TTT RRR RRR AAA AAA FFF TTT RRR RRR AAA AAA FFF TTT RRR RRR AAA AAA FFF TTT RRR RRR AAA AAA FFF VERSION 5.1 (BUILD #301) RELEASE DATE FEBRUARY 2003 TRAF SIMULATION MODEL DEVELOPED FOR U. S. DEPARTMENT OF TRANSPORTATION FEDERAL HIGHWAY ADMINISTRATION FHWA OFFICE OF OPERATIONS RESEARCH, DEVELOPMENT AND TECHNOLOGY 1 CARD FILE L IST 0SEQ.# :----+----1----+----2----+----3----+----4---+----5----+----6----+----7----+----8 1 : 10 1 02005 0 1 2 : 1 0 0 20 7981 0000 0 8 700 7781 7581 2 3 : 900 3 4 : 60 4 5 : 0 0 0 0 0 0 0 0 0 0 0 5 6 : 6 7 8 26400 2 1 19 7 : 7 8 9 26400 2 1 19 8 : 8 98002 26400 2 1 19 9 :8001 1 2 0 2 1 19 10 : 5 6 7 26400 2 1 19 11 : 4 5 6 1500 2 1 19 12 : 1 2 3 26400 2 1 19 13 : 2 3 4 1500 2 1 19 14 : 3 4 5181800 2 1 19 15 : 6 7 0 0 0 11065 1 1 100 20 16 : 7 8 0 0 0 11055 1320 100 20 17 : 8 9 0 0 0 11065 1 1 100 20 18 :8001 1 0 0 0 11065 1 1 20 19 : 5 6 0 0 0 11065 1 1 100 20 20 : 4 5 0 0 0 11065 1 1 75 100 20 21 : 1 2 0 0 0 11065 1 1 100 20 22 : 2 3 0 0 0 11065 1 1 75 100 20 23 : 3 4 0 0 0 11065 1 1 100 20 24 : 6 7 8 100 25 25 : 7 8 9 100 25 26 : 8 98002 100 25 27 :8001 1 2 100 25 28 : 5 6 7 100 25 29 : 4 5 6 100 25 30 : 1 2 3 100 25 31 : 2 3 4 100 25 32 : 3 4 5 100 25 33 : 7 8 2 0 264 0 099999 0 2640 29 34 :8001 12400 0 0 100 50 50 50 35 : 0 170 36 :8002 35000 0 195 37 :8001 0 0 195 38 : 9 33280 0 195 39 : 7 28000 0 195

PAGE 101

91 40 : 8 30640 0 195 41 : 6 25360 0 195 42 : 5 22720 0 195 43 : 1 1600 0 195 44 : 4 22570 0 195 45 : 2 4240 0 195 46 : 3 4390 0 195 47 : 1 0 0 210 0SEQ.# :----+----1----+----2----+----3----+----4---+----5----+----6----+----7----+----8 TRAF SIMULATION MODEL DEVELOPED FOR U. S. DEPARTMENT OF TRANSPORTATION FEDERAL HIGHWAY ADMINISTRATION FHWA OFFICE OF OPERATIONS RESEARCH, DEVELOPMENT AND TECHNOLOGY 1 0 DATE = 10/10/2005 0 USER = 0 AG ENCY = RUN CONTROL DATA VALUE RUN PARAMETERS AND OPTIONS 0 0 RUN IDENTIFICATION NUMBER 1 RUN TYPE CODE = ( 1 2, 3) TO RUN (SIMULATION, ASSIGNMENT, BOTH) (-1 ,-2,-3) TO CHECK (SIMULATION, ASSIGNMENT, BOTH) ONL Y 0 FRESIM OFFLINE INCI DENT DETECTION CODE = (0, 1) IF OFFLINE INCIDENT DE TECTION (IS NOT, IS) BEING PERFORMED FRESIM ENVIRONMENTA L OPTIONS --------------------------0 FUEL/EMISSION RATE TABLES ARE NOT PRINTED 0 SIMULATION: PERFOR MED ENVIRONMENTAL MEAS URES: CALCULATED RATE TABLES: EMBEDD ED TRAJECTORY FILE: NOT WRITTEN 0 INITIALIZATION CODE (0,1) = (DO NOT, DO) FORCE FULL INITIALIZATION PER IOD 0 INPUT UNITS CODE = (0,1) IF INPUT IS IN (ENGLISH, METRIC) UNITS 0 OUTPUT UNITS CODE = (0,1,2,3) IF OUTPUT IS IN (SAME AS INPUT, ENGLISH, METRIC, BOTH) UNITS 700 CLOCK TIME AT START OF SIMULATION (HHMM) 7581 RANDOM NUMBER SEED 900 DURATION (SEC) OF T IME PERIOD NO. 1 60 LENGTH OF A TIME IN TERVAL, SECONDS 10 FRESIM TIME STEP DU RATION IN TENTHS-OF-A-SECOND 20 MAXIMUM INITIALIZAT ION TIME, NUMBER OF TIME INTERVALS 0 NUMBER OF TIME INTE RVALS BETWEEN SUCCESSIVE STANDARD OUTPUTS 0 TIME INTERMEDIATE O UTPUT WILL BEGIN AT INTERVALS OF 0 SECS. FOR 0 SECS. FOR MICROSCOPIC MODELS 1************************************************** *************************************************** ***************** ************* TI ME PERIOD 1 FRESIM DATA *************************************************** *************************************************** ***************** ************ 1 FRESIM LINK CHARACTERISTICS -----------------------------------AUXILIARY LANE ----------ONE ----TWO --THREE -G T T T T R R IGHT FREE Y NO. Y Y Y THRU CURV A PAVE TRUCK LAN E OF FLOW QUEUE P LNGTH THRU P LNGTH P LNGTH P LNGTH DEST RADIUS D SUPER MENT RESTRAINT SEP PAIR SPEED HDWY LINK E (FT) LANES E ID (FT) E ID (FT) E ID (FT) NODE (FT) E ELEV CODE CODE LANE 1 2 (MPH) (SEC) LINK NAME -----------------------------------------------------------------------------------( 6, 7) F 2640 2 8 0 0 0 1* 1 1 65 1.0 ( 7, 8) F 2640 2 9 0 0 0 1* 0 55* 1.0 ( 8, 9) F 2640 2 8002 0 0 0 1* 1 1 65 1.0 (8001, 1) F 0 2 2 0 0 0 1* 1 1 65 1.0 ( 5, 6) F 2640 2 7 0 0 0 1* 1 1 65 1.0 ( 4, 5) F 150 2 6 0 0 0 1* 1 1 65 1.0 ( 1, 2) F 2640 2 3 0 0 0 1* 1 1 65 1.0 ( 2, 3) F 150 2 4 0 0 0 1* 1 1 65 1.0 ( 3, 4) F 18180 2 5 0 0 0 1* 1 1 65 1.0 TOTAL LINKS: 10 INDICATES THAT THE DEFAULT VALUE IS USED

PAGE 102

92 LINK TYPE CODE AUXILIARY LANE TYPE CODE PAVEMENT CODE T RUCK RESTRAINT CODE F FREEWAY LINK A ACCELERATIO N LANE 1 DRY CONCRETE 0 TRUCKS ARE UNRESTRICTED R RAMP LINK D DECELERATIO N LANE 2 WET CONCRETE 1 TRUCKS ARE BIASED TO F FULL AUXILI ARY LANE 3 DRY ASPHALT CERTAIN LANE(S) 4 WET ASPHALT 2 TRUCKS ARE RESTRICTED TO CERTAIN LANES(S) CAR FOLLO WING SENSITIVITY MULTIPLIERS LI NK ID MULTIPLIER -----------------------(NO MODIFIERS WERE PROVIDED MULTIPLIERS FOR ALL LINKS ARE DEFAULTED TO 1.0) ANTICIP ATORY LANE CHANGE PARAMETERS LINK I D MIN AVE SPEED DISTANCE ------------------------------FRESIM TURNING MOVEMENTS ----------------------------------MAIN-L INE TRAFFIC ------------------EXITING TRAFF IC ---------DOWNSTREAM NODE NO. OF THE MAIN-LIN E DOWNSTREAM NODE LINK RECEIVING LINK PERCENTAGE NO. OF THE OFF-RAMP PERCENTAGE --------------------------------------------------------------------------( 6, 7) 8 100.0 ( 7, 8) 9 100.0 ( 8, 9) 8002 100.0 (8001, 1) 2 100.0 ( 5, 6) 7 100.0 ( 4, 5) 6 100.0 ( 1, 2) 3 100.0 ( 2, 3) 4 100.0 ( 3, 4) 5 100.0 FRESIM INCIDENT DATA ---------------------INCIDENT CODE DISTANCE LENGTH ---BY LANE (*) ---FROM OF RUBBE R WARN. 1 1 UPSTREAM ROADWAY TIME OF INCIDENT NECK SIGN LINK 1 2 3 4 5 6 7 8 9 0 1 NODE AFFECTED ONSET DURATION FACTO R POSITION -------------------------------------------------( 7, 8) 2 0 0 0 0 0 0 0 0 0 0 0.0 2640.0 0 **** 0. 0 2640.0 INCIDENT CODES: 0 NORMAL SPEED 1 REDUCED TRAFFIC CAPACITY DUE TO RUBBER NECKING 2 BLOCKAGE FRESIM LINK VOLUME -------------------FLOW RATE PERCENT PERCENT PERCENT HOV LINK (VEH/HOUR) TRUCKS CARPOOL LANE VIOLAT ORS ----------------------------------------------------(8001, 1) 2400 0 0 1.00 1 FRESIM LANE ALIGNMENT TABLE ---------------------------DISTANC E FROM UPSTREAM FEEDING LANE NUMBER LINK UPST. NO DE --------------------------------------------REASON LINK TYPE (FT) 1 2 3 4 5 6 7 8 9 10 11 CODE ---------------------------------------------------------TABLE OF FREEWAY WARNING SIGNS ------------------------------WARNING SIGN OBJECTIVE DISTANCE BETWEEN -----------------------------DISTANCE BETWEEN THRU EXITING THE WARNING SIGN LINK LINK THE WARNING SIGN TRAFFIC TRAFFIC

PAGE 103

93 TYPE OF WARNING SIGN AND UPSTREAM OFFRAMP CONTAINING WITH AND ITS VACATES MOVES TO WARNING SIGN LINK NODE (FT) NODE INCIDENT LANE DROP OBJECTIVE (FT) LANE(S) LANE(S) ---------------------------------------------------------------------------------------------INCIDENT ( 5, 6) 2640.0 ( 7, 8) 2640.0 1 FRES IM ORIGIN DESTINATION TRIP TABLE --------------------------------------FOR EACH ORIGIN NODE, TABLE PROVIDES LISTING OF P AIRS OF DATA : DESTINATION/ FRACTION OF ENTRY VOLUM E TRAVELING TO DESTINATION ORIGIN NODE (8001) 9/ 1.000 THE GRAVITY MODEL ACCURACY THRESHOLD IS 5.0E-0 2 GRAVITY MODEL RESULTS --------------------ENTRY VOL/DEST 9 8001 2400.0 2400.0 SUM VOL 2400.0 DEST VOL 2400.0 FREE FLOW SPEED PERCENTAGES -------------------------DRIVER TYPE: 1 2 3 4 5 6 7 8 9 10 --------------------PERCENTAGE: 88 91 94 97 99 101 103 106 109 112 MAXIM UM ACCELERATION TABLE ------------------------PERFORMANCE 0 10 20 30 40 50 60 70 80 90 100 110 INDEX FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/ SEC FT/SEC ---------------------------------------------------------------1 8.00 9.00 6.00 5.00 5.00 5.00 4.00 3.00 2.00 2.00 1 .00 1.00 2 6.00 12.00 10.00 8.00 7.00 6.00 4.00 4.00 4.00 2.00 2 .00 2.00 3 4.69 5.35 4.94 3.47 3.09 2.61 2.14 1.70 1.27 0.86 0 .46 0.06 4 2.81 2.42 2.15 2.04 1.74 1.42 1.12 0.83 0.56 0.30 0 .04 -0.23 5 2.76 2.37 1.81 1.56 1.25 0.97 0.73 0.52 0.32 0.14 -0 .05 -0.23 6 2.45 2.14 1.42 1.12 0.85 0.63 0.44 0.29 0.14 0.00 -0 .14 -0.27 7 7.47 5.33 3.17 2.66 2.29 1.65 1.40 0.95 0.75 0.50 -0 .33 -0.35 GRADE CORREC TION FACTORS FOR ACCELERATION (USED BY FRESIM ONLY) -------------------------------------------------------------PERFORMANCE 0 10 20 30 40 50 60 70 80 90 100 110 INDEX FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/ SEC FT/SEC ---------------------------------------------------------------1 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0 .31 0.31 2 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0 .31 0.31 3 0.21 0.21 0.21 0.22 0.23 0.25 0.26 0.27 0.28 0.28 0 .30 0.31 4 0.16 0.15 0.19 0.22 0.24 0.25 0.27 0.28 0.29 0.31 0 .31 0.31 5 0.18 0.17 0.20 0.23 0.25 0.27 0.28 0.30 0.31 0.31 0 .31 0.31 6 0.18 0.18 0.22 0.25 0.27 0.29 0.30 0.30 0.31 0.31 0 .31 0.31 7 0.27 0.27 0.27 0.27 0.27 0.29 0.29 0.30 0.30 0.30 0 .30 0.30 GRADE CORRECTI ON FACTORS FOR FUEL CONSUMPTION (USED BY FRESIM ONL Y) ----------------------------------------------------------------PERFORMANCE 0 10 20 30 40 50 60 70 80 90 100 110 INDEX FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/ SEC FT/SEC ---------------------------------------------------------------1 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0 .31 0.31 2 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0 .31 0.31 3 0.26 0.26 0.26 0.28 0.29 0.30 0.30 0.30 0.30 0.30 0 .30 0.30 4 0.11 0.11 0.23 0.27 0.28 0.29 0.30 0.30 0.30 0.30 0 .30 0.30 5 0.16 0.16 0.26 0.28 0.29 0.30 0.30 0.30 0.30 0.30 0 .30 0.30 6 0.20 0.20 0.28 0.29 0.30 0.30 0.30 0.30 0.31 0.31 0 .31 0.31 7 0.27 0.27 0.27 0.27 0.27 0.29 0.29 0.30 0.30 0.30 0 .30 0.30 1 IN ITIALIZATION STATISTICS TIME INTERVAL SUBNETWORK PR IOR CONTENT CURRENT CONTENT PERCENT NUMBER TYPE (VEHICLES) (VEHICLES) DIFFERENCE 1 FRESIM 0 40 10000 2 FRESIM 40 80 100 3 FRESIM 80 119 48 4 FRESIM 119 159 33 5 FRESIM 159 199 25 6 FRESIM 199 228 14 7 FRESIM 228 243 6 8 FRESIM 243 238 2 EQUILIBRIUM ATTAINED ALL EXI STING SUBNETWORKS REACHED EQUILIBRIUM

PAGE 104

94 1 CUMULATI VE FRESIM STATISTICS AT TIME 7 15 0 ---------------------------------------------------LINK STATISTICS VEH-MIN/ SECONDS/VEHICLE VEH-MILE ---------------------------VEHICLES LANE CURR AVG VEHVEHTOTAL MOVE DELAY VOLUME DENSITY SPEED LINK LINK IN OUT CHNG CONT CONT MILES MIN TIME TIME TIME M/T TOTAL DELAY VEH/LN/H R VEH/LN-MILE MILE/HR TYPE ------------------------------------------------------------------------------------------( 6, 7) 597 561 306 62 39.7 291.9 5 96.1 61.3 28.0 33.3 0.46 2.04 1.11 1167.6 39.7 29.38 FRWY ( 7, 8) 561 562 0 22 23.2 280.4 3 48.5 37.3 32.8 4.5 0.88 1.24 0.15 1121.5 23.2 48.28 FRWY ( 8, 9) 562 566 267 12 18.3 281.3 2 74.6 29.3 27.7 1.5 0.95 0.98 0.05 1125.2 18.3 61.48 FRWY ( 5, 6) 603 597 126 23 19.4 299.5 2 91.1 29.2 27.8 1.4 0.95 0.97 0.05 1197.9 19.4 61.74 FRWY ( 4, 5) 604 603 8 2 1.1 17.1 16.6 1.7 1.6 0.1 0.95 0.97 0.04 1205.7 19.5 61.89 FRWY ( 1, 2) 599 598 98 20 19.0 301.4 2 84.3 28.3 27.8 0.5 0.98 0.94 0.02 1205.7 19.0 63.60 FRWY ( 2, 3) 598 597 6 1 1.1 17.0 16.2 1.6 1.6 0.0 0.97 0.95 0.03 1194.4 19.0 62.84 FRWY ( 3, 4) 597 604 827 129 133.2 2060.6 19 97.5 200.3 191.2 9.1 0.95 0.97 0.04 1196.9 19.3 61.89 FRWY NETW ORK STATISTICS VEHICLE-MILES = 3549.1, VEHICLE-MIN UTES = 3824.8, MOVING/TOTAL TRIP TIME = 0.872, AVERAGE CONTENT = 255.0, CURRENT CONTENT = 271.0, SPEED(MPH) = 55.68, TOTAL DELAY (VEH-MIN) = 491.04, TRA VEL TIME (MIN)/VEH-MILE = 1.08, DELAY TIME (MIN)/ VEH-MILE = 0.14 L INK STATISTICS BY LANE (SOME STAT ISTICS APPLY TO HOV LANES ONLY) SEC./VEHICLE SEC./PERSON ---------------------------------VEHICLES CURR VOLUME VOLUME OF TOTAL MOVE DELAY TOTAL MOVE DELAY SPEED LINK LANE TYPE IN OUT CONT VEH/HR VIOLATORS TIME TIME TIME TIME TIME TIME MILES/HR ----------------------------------------------------------------( 6, 7) 1 SOV ----5 322.1 ----29.2 28.3 1.0 22.5 21.7 0.7 61.57 ( 6, 7) 2 SOV ----57 2012.4 ----66.4 27.7 38.7 51.1 21.3 29.8 27.10 ( 7, 8) 1 SOV ----0 0.0 ----0.0 0.0 0.0 0.0 0.0 0.0 0.00 ( 7, 8) 2 SOV ----22 2242.9 ----37.3 32.9 4.4 28.7 25.3 3.4 48.28 ( 8, 9) 1 SOV ----4 796.8 ----27.9 26.7 1.2 21.5 20.5 0.9 64.48 ( 8, 9) 2 SOV ----8 1453.7 ----30.0 28.5 1.6 23.1 21.9 1.2 59.95 ( 5, 6) 1 SOV ----13 1216.5 ----29.0 27.7 1.3 22.3 21.3 1.0 62.03 ( 5, 6) 2 SOV ----10 1179.4 ----29.3 27.9 1.4 22.5 21.4 1.1 61.44 ( 4, 5) 1 SOV ----1 1238.5 ----1.7 1.6 0.1 1.3 1.2 0.1 61.95 ( 4, 5) 2 SOV ----1 1172.9 ----1.7 1.6 0.1 1.3 1.2 0.1 61.83 ( 1, 2) 1 SOV ----8 1199.1 ----28.3 27.8 0.5 21.8 21.4 0.4 63.51 ( 1, 2) 2 SOV ----12 1212.4 ----28.3 27.7 0.5 21.7 21.3 0.4 63.70 ( 2, 3) 1 SOV ----0 1192.6 ----1.6 1.6 0.1 1.3 1.2 0.0 62.64 ( 2, 3) 2 SOV ----1 1196.2 ----1.6 1.6 0.1 1.2 1.2 0.0 63.03 ( 3, 4) 1 SOV ----66 1216.1 ----200.1 191.1 9.0 153.9 147.0 6.9 61.94 ( 3, 4) 2 SOV ----63 1177.5 ----200.4 191.2 9.2 154.2 147.1 7.1 61.84 1 FR ESIM CUMULATIVE VALUES OF FUEL CONSUMPTION LINK LINK TYPE FUEL CONSUMPTION GALLONS M.P.G. VEHICLE TYPE1 2 3 4 5 6 7 1 2 3 4 5 6 7 ( 6, 7) FRWY 8.70 16.69 0.00 0.0 0 0.00 0.00 0.00 7.63 13.53 0.00 0.00 0.00 0.00 0.00

PAGE 105

95 ( 7, 8) FRWY 5.08 11.67 0.00 0.0 0 0.00 0.00 0.00 11.88 18.85 0.00 0.00 0.00 0.00 0.00 ( 8, 9) FRWY 5.27 11.19 0.00 0.0 0 0.00 0.00 0.00 11.95 19.57 0.00 0.00 0.00 0.00 0.00 ( 5, 6) FRWY 3.90 9.35 0.00 0.0 0 0.00 0.00 0.00 17.52 24.75 0.00 0.00 0.00 0.00 0.00 ( 4, 5) FRWY 0.23 0.52 0.00 0.0 0 0.00 0.00 0.00 18.28 25.12 0.00 0.00 0.00 0.00 0.00 ( 1, 2) FRWY 3.75 9.64 0.00 0.0 0 0.00 0.00 0.00 17.10 24.42 0.00 0.00 0.00 0.00 0.00 ( 2, 3) FRWY 0.18 0.49 0.00 0.0 0 0.00 0.00 0.00 20.08 27.09 0.00 0.00 0.00 0.00 0.00 ( 3, 4) FRWY 27.76 64.68 0.00 0.0 0 0.00 0.00 0.00 16.82 24.66 0.00 0.00 0.00 0.00 0.00 SUBNETWORK54.86 124.24 0.00 0.0 0 0.00 0.00 0.00 14.53 22.16 0.00 0.00 0.00 0.00 0.00 VEHICLE TYPES 1, 2 = AUTO, VE HICLE TYPES 3, 4, 5, 6 = TRUCK, VEHICLE TYPE 7 = TR ANSIT BUS 1 FRES IM CUMULATIVE VALUES OF EMISSION LINK LINK TYPE VEHICLE EMISSIONS (GRAMS/ MILE) HC VEHICLE TYPE1 2 3 4 5 6 7 ( 6, 7) FRWY 0.45 0.46 0.00 0.00 0.00 0.0 0 0.00 ( 7, 8) FRWY 0.28 0.33 0.00 0.00 0.00 0.0 0 0.00 ( 8, 9) FRWY 0.29 0.30 0.00 0.00 0.00 0.0 0 0.00 ( 5, 6) FRWY 0.15 0.16 0.00 0.00 0.00 0.0 0 0.00 ( 4, 5) FRWY 0.13 0.15 0.00 0.00 0.00 0.0 0 0.00 ( 1, 2) FRWY 0.14 0.14 0.00 0.00 0.00 0.0 0 0.00 ( 2, 3) FRWY 0.10 0.10 0.00 0.00 0.00 0.0 0 0.00 ( 3, 4) FRWY 0.16 0.16 0.00 0.00 0.00 0.0 0 0.00 SUBNETWORK0.20 0.21 0.00 0.00 0.00 0.00 0.00 VEHICLE TYPES 1, 2 = AUTO, VEH ICLE TYPES 3, 4, 5, 6 = TRUCK, VEHICLE TYPE 7 = TRA NSIT BUS 1 FRES IM CUMULATIVE VALUES OF EMISSION LINK LINK TYPE VEHICLE EMISSIONS (GRAMS/ MILE) CO VEHICLE TYPE1 2 3 4 5 6 7 ( 6, 7) FRWY 33.14 34.93 0.00 0.00 0.00 0.0 0 0.00 ( 7, 8) FRWY 21.92 25.66 0.00 0.00 0.00 0.0 0 0.00 ( 8, 9) FRWY 21.74 22.91 0.00 0.00 0.00 0.0 0 0.00 ( 5, 6) FRWY 8.98 10.55 0.00 0.00 0.00 0.0 0 0.00 ( 4, 5) FRWY 7.69 9.65 0.00 0.00 0.00 0.0 0 0.00 ( 1, 2) FRWY 8.79 9.11 0.00 0.00 0.00 0.0 0 0.00 ( 2, 3) FRWY 5.05 5.36 0.00 0.00 0.00 0.0 0 0.00 ( 3, 4) FRWY 10.37 10.40 0.00 0.00 0.00 0.0 0 0.00 SUBNETWORK13.75 1 4.50 0.00 0.00 0.00 0.00 0.00 VEHICLE TYPES 1, 2 = AUTO, VEH ICLE TYPES 3, 4, 5, 6 = TRUCK, VEHICLE TYPE 7 = TRA NSIT BUS 1 FRES IM CUMULATIVE VALUES OF EMISSION LINK LINK TYPE VEHICLE EMISSIONS (GRAMS/ MILE) NO VEHICLE TYPE1 2 3 4 5 6 7 ( 6, 7) FRWY 1.77 1.50 0.00 0.00 0.00 0.0 0 0.00 ( 7, 8) FRWY 1.06 1.05 0.00 0.00 0.00 0.0 0 0.00 ( 8, 9) FRWY 1.24 1.10 0.00 0.00 0.00 0.0 0 0.00 ( 5, 6) FRWY 0.85 0.82 0.00 0.00 0.00 0.0 0 0.00 ( 4, 5) FRWY 0.83 0.81 0.00 0.00 0.00 0.0 0 0.00 ( 1, 2) FRWY 0.89 0.87 0.00 0.00 0.00 0.0 0 0.00 ( 2, 3) FRWY 0.77 0.74 0.00 0.00 0.00 0.0 0 0.00 ( 3, 4) FRWY 0.88 0.83 0.00 0.00 0.00 0.0 0 0.00 SUBNETWORK0.99 0.93 0.00 0.00 0.00 0.0 0 0.00 VEHICLE TYPES 1, 2 = AUTO, VEH ICLE TYPES 3, 4, 5, 6 = TRUCK, VEHICLE TYPE 7 = TRA NSIT BUS 1 FRESIM INTER MEDIATE LINK STATION DATA AT TIME 7 15 0 ----------------------------------------------------1 LINK: ( 7, 8), LINK TYPE: FRWY, STATION PLACEMENT 1320 FEET FROM NODE 7 MEAN SPEED ----------------------PERCE NT OF TRAFFIC AT OR BELOW INDICATED SPEED, FPS -------------------LANE (FPS) 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 2 75.8 0 0 0 0 0 0 0 0 0 0 0 0 1 29 36 23 9 3 0 0 1 LINK: ( 4, 5), LINK TYPE: FRWY, STATION PLACEMENT 75 FEET FROM NODE 4 MEAN SPEED ----------------------PERCE NT OF TRAFFIC AT OR BELOW INDICATED SPEED, FPS -------------------LANE (FPS) 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 1 91.2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 33 23 23 18 2 91.2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 36 23 19 19 1

PAGE 106

96 LINK: ( 2, 3), LINK TYPE: FRWY, STATION PLACEMENT 75 FEET FROM NODE 2 MEAN SPEED ----------------------PERCE NT OF TRAFFIC AT OR BELOW INDICATED SPEED, FPS -------------------LANE (FPS) 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 1 92.6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 29 22 24 22 2 92.6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 27 23 27 22 1 FRESIM INTER MEDIATE LINK STATION DATA AT TIME 7 15 0 ----------------------------------------------------1 LINK: ( 7, 8), LINK TYPE: FRWY, STATION PLACEMENT 1320 FEET FROM NODE 7 MEAN HEADWAY ---------------------PE RCENT OF TRAFFIC AT OR BELOW INDICATED HEADWAY, SEC -------------------LANE SEC 0.4 0.8 1.2 1.6 2.0 2.4 2.8 3.2 3.6 4.0 4.4 4.8 5.2 5.6 6.0 6 .4 6.8 7.2 7.6 8.0 2 1.60 8 30 28 10 8 7 2 3 2 1 1 0 0 0 0 0 0 0 0 0 1 LINK: ( 4, 5), LINK TYPE: FRWY, STATION PLACEMENT 75 FEET FROM NODE 4 MEAN HEADWAY ---------------------PE RCENT OF TRAFFIC AT OR BELOW INDICATED HEADWAY, SEC -------------------LANE SEC 0.4 0.8 1.2 1.6 2.0 2.4 2.8 3.2 3.6 4.0 4.4 4.8 5.2 5.6 6.0 6 .4 6.8 7.2 7.6 8.0 1 2.98 10 19 16 9 6 4 5 4 4 3 2 2 2 2 2 1 2 0 1 7 2 2.98 10 20 15 7 7 5 5 4 4 3 3 2 1 1 1 1 1 1 1 8 1 LINK: ( 2, 3), LINK TYPE: FRWY, STATION PLACEMENT 75 FEET FROM NODE 2 MEAN HEADWAY ---------------------PE RCENT OF TRAFFIC AT OR BELOW INDICATED HEADWAY, SEC -------------------LANE SEC 0.4 0.8 1.2 1.6 2.0 2.4 2.8 3.2 3.6 4.0 4.4 4.8 5.2 5.6 6.0 6 .4 6.8 7.2 7.6 8.0 1 3.01 9 11 11 8 7 7 7 8 8 3 3 2 3 1 2 3 2 1 1 1 2 3.01 6 18 10 6 7 6 6 5 5 6 4 4 4 3 1 2 2 0 1 2 -----------------------------NETWORK-W IDE AVERAGE STATISTICS -----------------------------TOTAL VEHICLEMILE = 3549.14 VEHICLE-HOURS OF: MOVE TIME = 55.56 DELAY TIME = 8.18 TOTAL TIME = 63.75 AVERAGE SPEED ( MPH)= 55.68 MOVE/TOTAL = 0.87 MINUTES/MILE OF: DELAY TIME = 0.14 TOTAL TIME = 1.08 NETWORK-WIDE STATISTICS FOR SCRIPT PROCESSING 3549.14, 55.56, 8.18, 63.75, 55. 68, 0.87, 0.14, 1.08 TOTAL CPU TIME FOR SIMULATION = 13.76 SECOND S TOTAL CPU TIME FOR THIS RUN = 13.76 SECONDS Sample Output from CORSIM 6.0 On the following pages is a sample output for the f ollowing scenario (base case): 2-to-1 Lane Closure Work Zone Length at 0.5 miles Input Lane Distributions at 50/50 Sign Distance at 0.5 miles Truck Percentage at 0% Rubbernecking Factor at 0%

PAGE 107

97 INPUT FILE NAME: S:\Projects\Trucks on Arterials, W orkzones on Free RUN DATE : 02/01/06 TTTTTTTTTTT RRRRRRRRR AAAAAAA FFFFFFFFFFF TTTTTTTTTTT RRRRRRRRRR AAAAAAAAA FFFFFFFFFFF TTTTTTTTTTT RRRRRRRRRRR AAAAAAAAAAA FFFFFFFFFFF TTT RRR RRR AAA AAA FFF TTT RRR RRR AAA AAA FFF TTT RRRRRRRRRRR AAAAAAAAAAA FFFFFFF TTT RRRRRRRRRR AAAAAAAAAAA FFFFFFF TTT RRR RRR AAA AAA FFF TTT RRR RRR AAA AAA FFF TTT RRR RRR AAA AAA FFF TTT RRR RRR AAA AAA FFF TTT RRR RRR AAA AAA FFF VERSION 6.0 (BUILD 483) RELEASE DATE APRIL 2005 TRAF SIMULATION MODEL DEVELOPED FOR U. S. DEPARTMENT OF TRANSPORTATION FEDERAL HIGHWAY ADMINISTRATION FHWA OFFICE OF OPERATIONS RESEARCH, DEVELOPMENT AND TECHNOLOGY 1 CARD FILE L IST 0SEQ.# :----+----1----+----2----+----3----+----4---+----5----+----6----+----7----+----8 1 : 10 1 02005 0 1 2 : 1 0 0 20 7981 0000 0 8 700 7781 7581 2 3 : 900 3 4 : 1 60 4 5 : 0 0 0 0 0 0 0 0 0 0 0 0 5 6 : 6 7 8 26400 2 1 19 7 : 7 8 9 26400 2 1 19 8 : 8 98002 26400 2 1 19 9 : 5 6 7 26400 2 1 19 10 : 4 5 6 1500 2 1 19 11 : 1 2 3 26400 2 1 19 12 : 2 3 4 1500 2 1 19 13 : 3 4 5181800 2 1 19 14 :8001 1 2 0 2 1 19 15 : 6 7 0 0 0 11065 1 1 100 20 16 : 7 8 0 0 0 11055 1320 100 20 17 : 8 9 0 0 0 11065 1 1 100 20 18 : 5 6 0 0 0 11065 1 1 100 20 19 : 4 5 0 0 0 11065 1 1 75 100 20 20 : 1 2 0 0 0 11065 1 1 100 20 21 : 2 3 0 0 0 11065 1 1 75 100 20 22 : 3 4 0 0 0 11065 1 1 100 20 23 :8001 1 0 0 0 11065 1 1 20 24 : 6 7 8 100 25 25 : 7 8 9 100 25 26 : 8 98002 100 25 27 : 5 6 7 100 25 28 : 4 5 6 100 25 29 : 1 2 3 100 25 30 : 2 3 4 100 25 31 : 3 4 5 100 25 32 :8001 1 2 100 25 33 : 7 8 2 0 264 0 099999 0 2640 29 34 :8001 12400 0 0 100 50 50 50 35 : 0 170 36 :8002 35000 0 195 37 :8001 0 0 195 38 : 1 1600 0 195 39 : 2 4240 0 195

PAGE 108

98 40 : 3 4390 0 195 41 : 4 22570 0 195 42 : 5 22720 0 195 43 : 6 25360 0 195 44 : 7 28000 0 195 45 : 8 30640 0 195 46 : 9 33280 0 195 47 : 1 0 0 210 0SEQ.# :----+----1----+----2----+----3----+----4---+----5----+----6----+----7----+----8 TRAF SIMULATION MODEL DEVELOPED FOR U. S. DEPARTMENT OF TRANSPORTATION FEDERAL HIGHWAY ADMINISTRATION FHWA OFFICE OF OPERATIONS RESEARCH, DEVELOPMENT AND TECHNOLOGY 1 0 DATE = 10/10/2005 0 USER = 0 AG ENCY = RUN CONTROL DATA VALUE RUN PARAMETERS AND OPTIONS 0 0 RUN IDENTIFICATION NUMBER 1 RUN TYPE CODE = ( 1 2, 3) TO RUN (SIMULATION, ASSIGNMENT, BOTH) (-1 ,-2,-3) TO CHECK (SIMULATION, ASSIGNMENT, BOTH) ONL Y 0 FRESIM OFFLINE INCI DENT DETECTION CODE = (0, 1) IF OFFLINE INCIDENT DE TECTION (IS NOT, IS) BEING PERFORMED FRESIM ENVIRONMENTA L OPTIONS --------------------------0 FUEL/EMISSION RATE TABLES ARE NOT PRINTED 0 SIMULATION: PERFOR MED ENVIRONMENTAL MEAS URES: CALCULATED RATE TABLES: EMBEDD ED TRAJECTORY FILE: NOT WRITTEN 0 CODE = (0,1,2) FO R UNIFORM DISTRIBUTION, NORMAL DISTRIBUTION, ERLANG DISTRIBUTION 0 INITIALIZATION CODE (0,1) = (DO NOT, DO) FORCE FULL INITIALIZATION PER IOD 0 INPUT UNITS CODE = (0,1) IF INPUT IS IN (ENGLISH, METRIC) UNITS 0 OUTPUT UNITS CODE = (0,1,2,3) IF OUTPUT IS IN (SAME AS INPUT, ENGLISH, METRIC, BOTH) UNITS 700 CLOCK TIME AT START OF SIMULATION (HHMM) 7581 RANDOM NUMBER SEED 900 DURATION (SEC) OF T IME PERIOD NO. 1 60 LENGTH OF A TIME IN TERVAL, SECONDS 10 FRESIM TIME STEP DU RATION IN TENTHS-OF-A-SECOND 20 MAXIMUM INITIALIZAT ION TIME, NUMBER OF TIME INTERVALS 0 NUMBER OF TIME INTE RVALS BETWEEN SUCCESSIVE STANDARD OUTPUTS 0 TIME INTERMEDIATE O UTPUT WILL BEGIN AT INTERVALS OF 0 SECS. FOR 0 SECS. FOR MICROSCOPIC MODELS 1************************************************** *************************************************** ***************** ************* TI ME PERIOD 1 FRESIM DATA *************************************************** *************************************************** ***************** ************ 1 FRESIM LINK CHARACTERISTICS -----------------------------------AUXILIARY LANE ----------ONE ----TWO --THREE -G T T T T R R IGHT FREE Y NO. Y Y Y THRU CURV A PAVE TRUCK LAN E OF FLOW QUEUE P LNGTH THRU P LNGTH P LNGTH P LNGTH DEST RADIUS D SUPER MENT RESTRAINT SEP PAIR SPEED HDWY LINK E (FT) LANES E ID (FT) E ID (FT) E ID (FT) NODE (FT) E ELEV CODE CODE LANE 1 2 (MPH) (SEC) LINK NAME -----------------------------------------------------------------------------------( 6, 7) F 2640 2 8 0 0 0 1* 1 1 65 1.0 ( 7, 8) F 2640 2 9 0 0 0 1* 0 55* 1.0 ( 8, 9) F 2640 2 8002 0 0 0 1* 1 1 65 1.0 ( 5, 6) F 2640 2 7 0 0 0 1* 1 1 65 1.0 ( 4, 5) F 150 2 6 0 0 0 1* 1 1 65 1.0 ( 1, 2) F 2640 2 3 0 0 0 1* 1 1 65 1.0 ( 2, 3) F 150 2 4 0 0 0 1* 1 1 65 1.0 ( 3, 4) F 18180 2 5 0 0 0 1* 1 1 65 1.0 (8001, 1) F 0 2 2 0 0 0 1* 1 1 65 1.0 TOTAL LINKS: 10 INDICATES THAT THE DEFAULT VALUE IS USED

PAGE 109

99 LINK TYPE CODE AUXILIARY LANE TYPE CODE PAVEMENT CODE T RUCK RESTRAINT CODE F FREEWAY LINK A ACCELERATIO N LANE 1 DRY CONCRETE 0 TRUCKS ARE UNRESTRICTED R RAMP LINK D DECELERATIO N LANE 2 WET CONCRETE 1 TRUCKS ARE BIASED TO F FULL AUXILI ARY LANE 3 DRY ASPHALT CERTAIN LANE(S) 4 WET ASPHALT 2 TRUCKS ARE RESTRICTED TO CERTAIN LANES(S) CAR FOLLO WING SENSITIVITY MULTIPLIERS LI NK ID MULTIPLIER -----------------------(NO MODIFIERS WERE PROVIDED MULTIPLIERS FOR ALL LINKS ARE DEFAULTED TO 1.0) ANTICIP ATORY LANE CHANGE PARAMETERS LINK I D MIN AVE SPEED DISTANCE ------------------------------FRESIM TURNING MOVEMENTS ----------------------------------MAIN-L INE TRAFFIC ------------------EXITING TRAFF IC ---------DOWNSTREAM NODE NO. OF THE MAIN-LIN E DOWNSTREAM NODE LINK RECEIVING LINK PERCENTAGE NO. OF THE OFF-RAMP PERCENTAGE --------------------------------------------------------------------------( 6, 7) 8 100.0 ( 7, 8) 9 100.0 ( 8, 9) 8002 100.0 ( 5, 6) 7 100.0 ( 4, 5) 6 100.0 ( 1, 2) 3 100.0 ( 2, 3) 4 100.0 ( 3, 4) 5 100.0 (8001, 1) 2 100.0 FRESIM INCIDENT DATA ---------------------INCIDENT CODE DISTANCE LENGTH ---BY LANE (*) ---FROM OF RUBBE R WARN. 1 1 UPSTREAM ROADWAY TIME OF INCIDENT NECK SIGN LINK 1 2 3 4 5 6 7 8 9 0 1 NODE AFFECTED ONSET DURATION FACTO R POSITION -------------------------------------------------( 7, 8) 2 0 0 0 0 0 0 0 0 0 0 0.0 2640.0 0 **** 0. 0 2640.0 INCIDENT CODES: 0 NORMAL SPEED 1 REDUCED TRAFFIC CAPACITY DUE TO RUBBER NECKING 2 BLOCKAGE FRESIM LINK VOLUME -------------------FLOW RATE PERCENT PERCENT PERCENT HOV LINK (VEH/HOUR) TRUCKS CARPOOL LANE VIOLAT ORS ----------------------------------------------------(8001, 1) 2400 0 0 1.00 1 FRESIM LANE ALIGNMENT TABLE ---------------------------DISTANC E FROM UPSTREAM FEEDING LANE NUMBER LINK UPST. NO DE --------------------------------------------REASON LINK TYPE (FT) 1 2 3 4 5 6 7 8 9 10 11 CODE ---------------------------------------------------------TABLE OF FREEWAY WARNING SIGNS ------------------------------WARNING SIGN OBJECTIVE DISTANCE BETWEEN -----------------------------DISTANCE BETWEEN THRU EXITING THE WARNING SIGN LINK LINK THE WARNING SIGN TRAFFIC TRAFFIC

PAGE 110

100 TYPE OF WARNING SIGN AND UPSTREAM OFFRAMP CONTAINING WITH AND ITS VACATES MOVES TO WARNING SIGN LINK NODE (FT) NODE INCIDENT LANE DROP OBJECTIVE (FT) LANE(S) LANE(S) ---------------------------------------------------------------------------------------------INCIDENT ( 5, 6) 2640.0 ( 7, 8) 2640.0 1 FRES IM ORIGIN DESTINATION TRIP TABLE --------------------------------------FOR EACH ORIGIN NODE, TABLE PROVIDES LISTING OF P AIRS OF DATA : DESTINATION/ FRACTION OF ENTRY VOLUM E TRAVELING TO DESTINATION ORIGIN NODE (8001) 9/ 1.000 THE GRAVITY MODEL ACCURACY THRESHOLD IS 5.0E-0 2 GRAVITY MODEL RESULTS --------------------ENTRY VOL/DEST 9 8001 2400.0 2400.0 SUM VOL 2400.0 DEST VOL 2400.0 FREE FLOW SPEED PERCENTAGES -------------------------DRIVER TYPE: 1 2 3 4 5 6 7 8 9 10 --------------------PERCENTAGE: 88 91 94 97 99 101 103 106 109 112 MAXIM UM ACCELERATION TABLE ------------------------PERFORMANCE 0 10 20 30 40 50 60 70 80 90 100 110 INDEX FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/ SEC FT/SEC ---------------------------------------------------------------1 8.00 9.00 6.00 5.00 5.00 5.00 4.00 3.00 2.00 2.00 1 .00 1.00 2 6.00 12.00 10.00 8.00 7.00 6.00 4.00 4.00 4.00 2.00 2 .00 2.00 3 4.69 5.35 4.94 3.47 3.09 2.61 2.14 1.70 1.27 0.86 0 .46 0.06 4 2.81 2.42 2.15 2.04 1.74 1.42 1.12 0.83 0.56 0.30 0 .04 -0.23 5 2.76 2.37 1.81 1.56 1.25 0.97 0.73 0.52 0.32 0.14 -0 .05 -0.23 6 2.45 2.14 1.42 1.12 0.85 0.63 0.44 0.29 0.14 0.00 -0 .14 -0.27 7 7.47 5.33 3.17 2.66 2.29 1.65 1.40 0.95 0.75 0.50 -0 .33 -0.35 GRADE CORREC TION FACTORS FOR ACCELERATION (USED BY FRESIM ONLY) -------------------------------------------------------------PERFORMANCE 0 10 20 30 40 50 60 70 80 90 100 110 INDEX FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/ SEC FT/SEC ---------------------------------------------------------------1 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0 .31 0.31 2 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0 .31 0.31 3 0.21 0.21 0.21 0.22 0.23 0.25 0.26 0.27 0.28 0.28 0 .30 0.31 4 0.16 0.15 0.19 0.22 0.24 0.25 0.27 0.28 0.29 0.31 0 .31 0.31 5 0.18 0.17 0.20 0.23 0.25 0.27 0.28 0.30 0.31 0.31 0 .31 0.31 6 0.18 0.18 0.22 0.25 0.27 0.29 0.30 0.30 0.31 0.31 0 .31 0.31 7 0.27 0.27 0.27 0.27 0.27 0.29 0.29 0.30 0.30 0.30 0 .30 0.30 GRADE CORRECTI ON FACTORS FOR FUEL CONSUMPTION (USED BY FRESIM ONL Y) ----------------------------------------------------------------PERFORMANCE 0 10 20 30 40 50 60 70 80 90 100 110 INDEX FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/SEC FT/ SEC FT/SEC ---------------------------------------------------------------1 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0 .31 0.31 2 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0 .31 0.31 3 0.26 0.26 0.26 0.28 0.29 0.30 0.30 0.30 0.30 0.30 0 .30 0.30 4 0.11 0.11 0.23 0.27 0.28 0.29 0.30 0.30 0.30 0.30 0 .30 0.30 5 0.16 0.16 0.26 0.28 0.29 0.30 0.30 0.30 0.30 0.30 0 .30 0.30 6 0.20 0.20 0.28 0.29 0.30 0.30 0.30 0.30 0.31 0.31 0 .31 0.31 7 0.27 0.27 0.27 0.27 0.27 0.29 0.29 0.30 0.30 0.30 0 .30 0.30 1 IN ITIALIZATION STATISTICS TIME INTERVAL SUBNETWORK PR IOR CONTENT CURRENT CONTENT PERCENT NUMBER TYPE (VEHICLES) (VEHICLES) DIFFERENCE 1 FRESIM 0 39 10000 2 FRESIM 39 79 102 3 FRESIM 79 119 50 4 FRESIM 119 159 33 5 FRESIM 159 199 25 6 FRESIM 199 225 13 7 FRESIM 225 234 4 8 FRESIM 234 242 3 EQUILIBRIUM ATTAINED ALL EXI STING SUBNETWORKS REACHED EQUILIBRIUM

PAGE 111

101 1 CUMULATI VE FRESIM STATISTICS AT TIME 7 15 0 ---------------------------------------------------LINK STATISTICS VEH-MIN/ SECONDS/VEHICLE VEH-MILE ---------------------------VEHICLES LANE CURR AVG VEHVEHTOTAL MOVE DELAY VOLUME DENSITY SPEED LINK LINK IN OUT CHNG CONT CONT MILES MIN TIME TIME TIME M/T TOTAL DELAY VPHPL VPMPL MILE/HR TYPE ---------------------------------------------------------------------------------------( 6, 7) 609 598 315 38 27.3 300.7 4 10.1 40.9 27.9 13.0 0.68 1.36 0.43 1202.7 27.3 43.99 FRWY ( 7, 8) 598 592 0 25 23.8 297.1 3 57.4 36.1 32.8 3.3 0.91 1.20 0.11 1188.5 23.8 49.88 FRWY ( 8, 9) 592 588 290 22 19.5 296.1 2 92.1 29.6 27.9 1.7 0.94 0.99 0.06 1184.2 19.5 60.80 FRWY ( 5, 6) 590 609 75 4 19.8 300.1 2 97.6 29.8 27.9 1.9 0.94 0.99 0.06 1200.2 19.8 60.50 FRWY ( 4, 5) 589 590 6 2 1.1 16.8 16.6 1.7 1.6 0.1 0.94 0.99 0.06 1180.1 19.4 60.72 FRWY ( 1, 2) 599 602 46 17 18.9 301.9 2 83.9 28.2 27.8 0.4 0.99 0.94 0.01 1207.8 18.9 63.82 FRWY ( 2, 3) 602 602 2 1 1.1 17.1 16.4 1.6 1.6 0.1 0.97 0.96 0.03 1203.4 19.2 62.72 FRWY ( 3, 4) 602 589 590 144 134.9 2064.6 20 24.2 202.6 191.6 10.9 0.95 0.98 0.05 1199.2 19.6 61.20 FRWY NETW ORK STATISTICS VEHICLE-MILES = 3594.3, VEHICLE-MIN UTES = 3698.1, MOVING/TOTAL TRIP TIME = 0.915, AVERAGE CONTENT = 246.5, CURRENT CONTENT = 253.0, SPEED(MPH) = 58.31, TOTAL DELAY (VEH-MIN) = 313.57, TRA VEL TIME (MIN)/VEH-MILE = 1.03, DELAY TIME (MIN)/ VEH-MILE = 0.09 L INK STATISTICS BY LANE (SOME STAT ISTICS APPLY TO HOV LANES ONLY) SEC./VEHICLE SEC./PERSON ---------------------------------VEHICLES CURR VOLUME VOLUME OF TOTAL MOVE DELAY TOTAL MOVE DELAY SPEED LINK LANE TYPE IN OUT CONT VEH/HR VIOLATORS TIME TIME TIME TIME TIME TIME MILES/HR ----------------------------------------------------------------( 6, 7) 1 SOV ----3 95.5 ----34.2 27.5 6.7 26.3 21.2 5.1 52.62 ( 6, 7) 2 SOV ----35 2309.8 ----41.2 27.9 13.3 31.7 21.5 10.2 43.70 ( 7, 8) 1 SOV ----0 0.0 ----0.0 0.0 0.0 0.0 0.0 0.0 0.00 ( 7, 8) 2 SOV ----25 2377.0 ----36.1 32.7 3.4 27.8 25.2 2.6 49.88 ( 8, 9) 1 SOV ----9 870.6 ----28.3 27.0 1.3 21.8 20.8 1.0 63.62 ( 8, 9) 2 SOV ----13 1497.9 ----30.4 28.6 1.8 23.4 22.0 1.4 59.28 ( 5, 6) 1 SOV ----1 1189.5 ----29.8 27.9 1.9 22.9 21.4 1.5 60.40 ( 5, 6) 2 SOV ----3 1211.0 ----29.7 27.8 1.9 22.8 21.4 1.5 60.60 ( 4, 5) 1 SOV ----1 1139.5 ----1.7 1.6 0.1 1.3 1.2 0.1 60.53 ( 4, 5) 2 SOV ----1 1220.7 ----1.7 1.6 0.1 1.3 1.2 0.1 60.90 ( 1, 2) 1 SOV ----8 1208.7 ----28.3 27.9 0.4 21.8 21.5 0.3 63.56 ( 1, 2) 2 SOV ----9 1206.9 ----28.1 27.6 0.4 21.6 21.3 0.3 64.09 ( 2, 3) 1 SOV ----1 1175.0 ----1.6 1.6 0.0 1.3 1.2 0.0 62.41 ( 2, 3) 2 SOV ----0 1231.7 ----1.6 1.6 0.1 1.2 1.2 0.0 63.01 ( 3, 4) 1 SOV ----71 1163.1 ----203.3 192.5 10.8 156.4 148.1 8.3 60.97 ( 3, 4) 2 SOV ----73 1235.2 ----201.9 190.8 11.0 155.3 146.8 8.5 61.41 FR ESIM CUMULATIVE VALUES OF FUEL CONSUMPTION F UEL CONSUMPTION BY VEHICLE TYPE (GALLONS) LINK 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ( 6, 7) FRWY 6.55 11.93 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 7, 8) FRWY 3.70 7.31 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

PAGE 112

102 ( 8, 9) FRWY 6.65 11.03 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 5, 6) FRWY 3.81 7.90 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 4, 5) FRWY 0.22 0.46 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 1, 2) FRWY 4.17 8.21 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 2, 3) FRWY 0.20 0.42 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 3, 4) FRWY 27.19 54.36 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 SUBNETWORK52.49 101.62 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 VEHICLE TYPES 1, 2 = AUTO, VEHICLE TYPES 3, 4, 5, 6 = TRUCK, VEHICLE TYPE 7 = TRANSIT BUS, VEHICLE TYP ES 8, 9 = CARPOOL, VEHICLE TYPES 10 16 USER DEFINED F UEL CONSUMPTION BY VEHICLE TYPE (M.P.G.) LINK 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ( 6, 7) FRWY 12.65 18.31 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 7, 8) FRWY 22.09 29.48 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 8, 9) FRWY 12.09 19.60 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 5, 6) FRWY 20.85 27.90 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 4, 5) FRWY 20.91 27.58 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 1, 2) FRWY 19.48 26.63 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 2, 3) FRWY 21.63 27.81 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 3, 4) FRWY 20.55 27.74 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 SUBNETWORK18.55 25.80 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 VEHICLE TYPES 1, 2 = AUTO, VEHICLE TYPES 3, 4, 5, 6 = TRUCK, VEHICLE TYPE 7 = TRANSIT BUS, VEHICLE TYP ES 8, 9 = CARPOOL, VEHICLE TYPES 10 16 USER DEFINED FRES IM CUMULATIVE VALUES OF EMISSION VEHIC LE EMISSIONS BY VEHICLE TYPE (GRAMS / MILE) HC LINK 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ( 6, 7) FRWY 0.19 0.23 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 7, 8) FRWY 0.07 0.09 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 8, 9) FRWY 0.27 0.27 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 5, 6) FRWY 0.09 0.09 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 4, 5) FRWY 0.09 0.09 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 1, 2) FRWY 0.10 0.09 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 2, 3) FRWY 0.08 0.09 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 3, 4) FRWY 0.09 0.09 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 SUBNETWORK0.11 0.12 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 VEHICLE TYPES 1, 2 = AUTO, VEHICLE TYPES 3, 4, 5, 6 = TRUCK, VEHICLE TYPE 7 = TRANSIT BUS, VEHICLE TYP ES 8, 9 = CARPOOL, VEHICLE TYPES 10 16 USER DEFINED FRES IM CUMULATIVE VALUES OF EMISSION VEHIC LE EMISSIONS BY VEHICLE TYPE (GRAMS / MILE) CO LINK 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ( 6, 7) FRWY 9.99 14.52 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 7, 8) FRWY 3.10 4.18 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 8, 9) FRWY 19.49 19.89 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 5, 6) FRWY 3.44 3.85 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 4, 5) FRWY 4.24 3.37 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 1, 2) FRWY 4.90 4.60 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 2, 3) FRWY 3.00 4.11 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 3, 4) FRWY 3.85 3.68 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 SUBNETWORK5.65 6.05 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 VEHICLE TYPES 1, 2 = AUTO, VEHICLE TYPES 3, 4, 5, 6 = TRUCK, VEHICLE TYPE 7 = TRANSIT BUS, VEHICLE TYP ES 8, 9 = CARPOOL, VEHICLE TYPES 10 16 USER DEFINED FRES IM CUMULATIVE VALUES OF EMISSION VEHIC LE EMISSIONS BY VEHICLE TYPE (GRAMS / MILE)

PAGE 113

103 NO LINK 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ( 6, 7) FRWY 1.17 1.19 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 7, 8) FRWY 0.48 0.51 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 8, 9) FRWY 1.25 1.12 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 5, 6) FRWY 0.65 0.65 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 4, 5) FRWY 0.62 0.68 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 1, 2) FRWY 0.79 0.77 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 2, 3) FRWY 0.66 0.69 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ( 3, 4) FRWY 0.69 0.67 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 SUBNETWORK0.76 0.75 0.00 0.0 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 VEHICLE TYPES 1, 2 = AUTO, VEHICLE TYPES 3, 4, 5, 6 = TRUCK, VEHICLE TYPE 7 = TRANSIT BUS, VEHICLE TYP ES 8, 9 = CARPOOL, VEHICLE TYPES 10 16 USER DEFINED 1 FRESIM INTER MEDIATE LINK STATION DATA AT TIME 7 15 0 ----------------------------------------------------1 LINK: ( 7, 8), LINK TYPE: FRWY, STATION PLACEMENT 1320 FEET FROM NODE 7 MEAN SPEED ----------------------PERCE NT OF TRAFFIC AT OR BELOW INDICATED SPEED, FPS -------------------LANE (FPS) 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 2 71.4 0 0 0 0 0 0 0 0 0 0 1 5 8 41 37 7 1 0 0 0 1 LINK: ( 4, 5), LINK TYPE: FRWY, STATION PLACEMENT 75 FEET FROM NODE 4 MEAN SPEED ----------------------PERCE NT OF TRAFFIC AT OR BELOW INDICATED SPEED, FPS -------------------LANE (FPS) 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 1 89.1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 50 19 20 7 2 89.7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 42 27 18 9 1 LINK: ( 2, 3), LINK TYPE: FRWY, STATION PLACEMENT 75 FEET FROM NODE 2 MEAN SPEED ----------------------PERCE NT OF TRAFFIC AT OR BELOW INDICATED SPEED, FPS -------------------LANE (FPS) 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 1 92.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 34 23 20 22 2 92.9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 29 21 21 27 1 FRESIM INTER MEDIATE LINK STATION DATA AT TIME 7 15 0 ----------------------------------------------------1 LINK: ( 7, 8), LINK TYPE: FRWY, STATION PLACEMENT 1320 FEET FROM NODE 7 MEAN HEADWAY ---------------------PE RCENT OF TRAFFIC AT OR BELOW INDICATED HEADWAY, SEC -------------------LANE SEC 0.4 0.8 1.2 1.6 2.0 2.4 2.8 3.2 3.6 4.0 4.4 4.8 5.2 5.6 6.0 6 .4 6.8 7.2 7.6 8.0 2 1.51 6 39 31 9 5 3 3 2 1 0 0 0 0 0 0 0 0 0 0 1 1 LINK: ( 4, 5), LINK TYPE: FRWY, STATION PLACEMENT 75 FEET FROM NODE 4 MEAN HEADWAY ---------------------PE RCENT OF TRAFFIC AT OR BELOW INDICATED HEADWAY, SEC -------------------LANE SEC 0.4 0.8 1.2 1.6 2.0 2.4 2.8 3.2 3.6 4.0 4.4 4.8 5.2 5.6 6.0 6 .4 6.8 7.2 7.6 8.0 1 3.17 13 25 16 2 6 5 5 3 3 2 2 1 2 2 1 0 1 1 1 9 2 2.94 13 26 19 6 4 4 2 3 2 1 2 2 2 2 1 1 1 0 0 9 1 LINK: ( 2, 3), LINK TYPE: FRWY, STATION PLACEMENT 75 FEET FROM NODE 2 MEAN HEADWAY ---------------------PE RCENT OF TRAFFIC AT OR BELOW INDICATED HEADWAY, SEC -------------------

PAGE 114

104 LANE SEC 0.4 0.8 1.2 1.6 2.0 2.4 2.8 3.2 3.6 4.0 4.4 4.8 5.2 5.6 6.0 6 .4 6.8 7.2 7.6 8.0 1 3.06 7 15 12 6 5 7 8 4 5 7 4 4 1 2 4 3 1 1 1 2 2 2.93 8 14 12 7 5 7 8 4 4 7 3 4 4 2 3 2 2 1 0 1 -----------------------------NETWORK-W IDE AVERAGE STATISTICS -----------------------------TOTAL VEHICLEMILE = 3594.26 VEHICLE-HOURS OF: MOVE TIME = 56.41 DELAY TIME = 5.23 TOTAL TIME = 61.64 AVERAGE SPEED ( MPH)= 58.31 MOVE/TOTAL = 0.92 MINUTES/MILE OF: DELAY TIME = 0.09 TOTAL TIME = 1.03 NETWORK-WIDE STATISTICS FOR SCRIPT PROCESSING 3594.26, 56.41, 5.23, 61.64, 58. 31, 0.92, 0.09, 1.03 TOTAL CPU TIME FOR SIMULATION = 12.67 SECOND S TOTAL CPU TIME FOR THIS RUN = 12.67 SECONDS 0LAST CASE PROCESSED

PAGE 115

105 APPENDIX D STATISTICA OUTPUT SCREENSHOTS FOR EACH LANE CLOSURE CONFIGURATION This section presents the output screenshots from t he software package STATISTICA used to analyze the data for this report The models for each lane closure configuration are presented with their correspondin g R2 values. The first column of the output shows the parameter estimates for each of the variables and the third and fourth columns show the tand p-stats. The red color of the font across the rows is the way in which the softwa re conveys statistical significance of the variables to a pre-specified level. All models are statistically significant within a 95% level of confidence. 2-to-1 Lane Closure Configuration

PAGE 116

106 3-to-2 Lane Closure Configuration 3-to-1 Lane Closure Configuration

PAGE 117

107 APPENDIX E MODEL USAGE EXAMPLE: SAMPLE CAPACITY CALCULATIONS F OR EACH LANE CLOSURE CONFIGURATION The table on the following page shows a sample calc ulation of capacity values for all the models given in the text. The Variables are listed to the left, and the trial input values are given in the Trial Values column, each one highlighted in yellow. The highlighted values show the user inputs and the non -highlighted values are calculated from the respective inputs and applied in each resp ective model. The limitations to the ranges of each of the inputs are listed on the far right side of the table and are discussed following the presentation of the sample calculatio ns table. The parameter estimates are shown where applicable for each of the models, and the total capacities for each model are displayed a cross the bottom of the table. These capacities are calculated by summing the Intercept with each of the terms in the same column multiplied by their respective input values in the Trial Values column. The models and parameters are shown on the followin g page.

PAGE 118

108 VariablesUnitsTrial Values2-to-13-to-23-to-12-to-13 -to-23-to-1 Intercept 1623.0221595.8401665.420 SignDist miles1740.878711.490763.5600 1.50 1.50 1.5 Truck% %10-14.233-5.870-10.1200 200 200 20 Rubber% %5-22.830-17.880-20.0700 250 250 25 DistrLan1(6,7) fraction0.15 0.043 0.6280.076 0.3520.002 0.176 DistrLan2(6,7) fraction0.4 0.041 0.508 SpdLan1(5,6) mph35 11.00 60.50 SpdLan1(6,7) mph35 2.48 66.28 SpdLan2(6,7) mph35 2.93 66.64 DistrLan1(6,7) SignDist 0.15-409.758-1211.940-1698.760 DistrLan2(6,7) SignDist 0.4 -626.500 SpdLan1(5,6) SignDist 35 -10.580 SpdLan1(6,7) SignDist 35-13.513 SpdLan2(6,7) SignDist 35 -14.370 CalcSpdLan2(5,6) 22.3305 9.300 CalcSpdLan2(6,7) 6.374626.694 CalcSpdLan3(6,7) 6.7642 26.840 1743.161814.811400.62 Calculated Capacities (veh/hr/ln) Lane Closure Configuration Parameter EstimatesRange of Input Limitations

PAGE 119

109 The usable ranges of values for each of the lane cl osure configurations are shown below. Following each lane closure model’s range i s an explanation of the reasoning behind the numbers. 2-to-1 configuration Lower Limit Upper Limit SignDist 0.5 mi 1.5 mi Truck% 0 20 Rubber% 0 25 DistrLan1(6,7) 0.043 0.628 SpdLan1(6,7) 2.48 66.28 Notes: The limitations shown for the above configur ation are a result of the data ranges that were produced from simulation. For a t wo-lane facility, the speed of the vehicles in link (6,7) can vary significantly based on the level of congestion present in lane 2. After the work zone sign, there is never a distribution of vehicles higher than 0.63 in lane 1. This implies that the fraction of vehic les in lane 2 ,must always be greater than 0.37. These implications are practical to keep in mind when inputting values; in doing so, if a capacity estimate appears unreasonable, th ere may be a traceable input value that is not logical for the traffic stream.

PAGE 120

110 3-to-2 configuration Lower Limit Upper Limit SignDist 0.5 mi 1.5 mi Truck% 0 20 Rubber% 0 25 DistrLan1(6,7) 0.076 0.352 SpdLan1(5,6) 11.0 60.5 Notes: The limitations shown for the above configur ation are a result of the data ranges that were produced from simulation. For a t hree lane facility, the speed of the vehicles in link (5,6) should be assumed to be at a pproximately free-flow speed unless the queue is backing up beyond the location of the upstream warning sign. If the queue from the lane closure should spill back onto link ( 5,6), then the value of speeds on lane 1 and lane 2 may be different under specific traffic stream conditions. Because vehicles are merging from lane 1 to lanes 2 and 3 of link (6,7), the queue from lane 2 will spill back onto link (5,6) sooner than the queue in lane 1, ca using the speeds in lane 1 to be higher.

PAGE 121

111 3-to-1 configuration Lower Limit Upper Limit SignDist 0.5 mi 1.5 mi Truck% 0 20 Rubber% 0 25 DistrLan1(6,7) 0.002 0.176 DistrLan2(6,7) 0.041 0.508 SpdLan2(6,7) 2.93 66.64 Notes: The limitations shown for the above configur ation are a result of the data ranges that were produced from simulation. For thi s lane configuration, the speeds in lanes 2 will decrease with a growing queue on that lane. If the queue of lane 3 from link (6,7) spills back into link (5,6), then congestion is high, and a queue will likely be forming on lane 2. When inputting lane distributio n values, the user should keep in mind that lane 3 of link (6,7) is getting the remaining distribution of vehicles not assigned to lanes 1 or 2. For example, inputting the largest v alues for both lanes 1 and 2 (0.176 and 0.508, respectively) only leaves 0.316 of the traff ic stream in lane 3, the through lane. This is not realistic and the resulting capacity va lue will not be reasonable.

PAGE 122

112 APPENDIX F TRAFVU SCREENSHOTS FOR EACH LANE CLOSURE CONFIGURAT ION The illustration below shows the TRAFVU screen shot s for the following simulation scenarios: 0.5 mile upstream warning sign location (not pictur ed) 20% truck presence in the traffic stream 15% rubbernecking factor through the work zone (the presence of a rubbernecking factor is depicted by yellow-colored lanes)

PAGE 123

113 The distance shown upstream of lane closure in the illustrations is approximately 0.10 miles. The closed lanes are depicted by red-c olored lanes, and the yellow lanes show where the rubbernecking factor is being applie d. The 2-to-1, 3-to-2, and 3-to-1 lane closures shown are one minute into the simulation.

PAGE 124

114 LIST OF REFERENCES Associated Press. “Model Helps Schedule Work-Zone Lane Closures.” Better Roads, Volume 59, Issue Number: 3, pp38-39. March 1989. Beacher, Andrew G., Fontaine, Michael D., and Garbe r, Nicholas J. “Field Evaluation of the Late Merge Work Zone Traffic Control.” Transportation Research Record: Journal of the Transportation Research Board Issue Number: 1911. 2005. Beacher, Andrew G., Fontaine, Michael D., and Garbe r, Nicholas J. “Guidelines for Using Late Merge Work Zone Traffic Control: Results of a Simulation-Based Study ” Transportation Research Record: Journal of the Tran sportation Research Board Issue Number: 1911. 2005. Benekohal, R., Kaja-Mohideen, A-Z., Chitturi, M. “ A Methodology for Estimating Operating Speed and Capacity in Work Zones.” Transportation Research Record: Journal of the Transportation Research Board Issue Number: 1883. 2004. Chien, Steven I-Jy, and Chowdhury, Shoaib M. “Simu lation-Based Estimates of Delays at Freeway Work Zones.” Journal of Advanced Transportation Volume 36, Issue Number: 2, pp131-156. August 2002. Chitturi, M., Benekohal, R. “Comparison of QUEWZ, FRESIM and QuickZone with Field Data for Work Zones.” Presented at the 83th Annual Transportation Research Board Meeting, Washington, D.C. 2004. Chitturi, M., and Benekohal, R. “Lane Width Effect on Speeds of Cars and Heavy Vehicles in Work Zones.” Transportation Research Record: Journal of the Transportation Research Board Issue Number: 1920. 2005. Dixon, KK, Lorscheider, A, and Hummer, JE. “Comput er Simulation Of I-95 Lane Closures Using FRESIM.” 65th ITE Annual Meeting. 1995 Compendium of Technical Papers. Institute of Transportation Engi neers, Denver, CO. 1995. Federal Highway Administration. “User’s Manual for QuickZone Beta 0.5 (unpublished).” U.S. Department of Transportation, 2000. Florida Department of Transportation, 2006. Design Standards http://www.dot.state.fl.us/rddesign/rd/RTDS/06/2006 _Standards_600.htm Access date: May 4, 2006.

PAGE 125

115 Keller, EL, and Saklas, JG. Passenger Car Equival ents from Network Simulations. ASCE Journal of Transportation Engineering, Vol 110, No. 4 pp397-410. 1984. Kim, Taehyung, Lovell, David J., and Paracha, Jawad A New Methodology to Estimate Capacity for Freeway Work Zones. Present ed at the 80th Annual Transportation Research Board M eeting, Washington, D.C. January 2001. Krammes, Raymond A, and Lopez, Gustavo O. Updated Capacity Values for ShortTerm Freeway Work Zone Lane Closures. Transportation Research Record: Journal of the Transportation Research Board Issue Number: 1442. 1994. Manual on Uniform Traffic Control Devices for Stree ts and Highways 2003 Edition. U.S. Department of Transportation, FHWA. Washington, D.C. 2003. Maze, Tom, and Kamyab, Ali. Work Zone Simulation Model. Center for Transportation Research and Education, Iowa State U niversit y, Ames, Iowa. September 1999. Maze, Tom, and Kamyab, Ali. Work Zone Simulation Model Companion Report. Center for Transportation Research and Education, I owa State University, Ames, Iowa. September 1999. Maze, Tom, Schrock, Steve, and Kamyab, Ali. Capac ity of Freeway Work Zone Lane Closures. Mid-Continent Transportation Symposium 2000 Proceedings, pp178183. 2000. Maze, Tom H., Schrock, Steve D., and VanDerHorst, K era S. Traffic Management Strategies for Merge Areas in Rural Interstate Work Zones. Center for Transportation Research and Education, Iowa State U niversity. July 1999. McCoy, Patrick T., Pesti, Geza, and Byrd, Patrick S Alternative Driver Information to Alleviate Work-Zone Related Delays. Department of Civil Engineering College of Engineering and Technology, Lincoln, Nebraska. F ebruary 1999. Mc Trans (Center for Microcomputers in Transportation). U.S Department of Transportation, FHWA. Gainesville, FL. Memmott, J. L., and C. L. Dudek. Queue and User C ost Evaluation of Work Zones (QUEWZ). Transportation Research Record: Journal of the Tran sportation Research Board Issue Number: 979. 1984. Owen, Larry E., Zhang, Yunlong, Rao, Lei, and McHal e, Gene. Traffic Flow Simulation Using CORSIM. Presented at the 2000 Wi nter Simulation Conference. 2000. Pal, Ratkim, and Sinha, Kumares C. Evaluation of Crossover and Partial Lane Closure Strategies for Interstate Work Zones in Indiana. Transportation Research Record: Journal of the Transportation Research Board Issue Number: 1529. 1996.

PAGE 126

116 Sarasua, Davis, Clarke, Kottapally, and Mulukutla “Evaluation of Interstate Highway Capacity for Short-Term Work Zone Lane Closures.” Transportation Research Record: Journal of the Transportation Research Boar d Issue Number: 1877. 2004. Schnell, Thomas, and Aktan, Fuan. “On the Accuracy of Commercially Available Macroscopic and Microscopic Traffic Simulation Tool s for Prediction of Workzone Traffic. Presented at the 80th Annual Transportation Research Board Meeting, Washington, D.C. 2001. Transportation Research Board. Highway Capacity Manual 2000. Washington, D.C. 2000. Ullman, Gerald L. “Queuing and Natural Diversion a t Short-Term Freeway Work Zone Lane Closures.” Transportation Research Record: Journal of the Tran sportation Research Board Issue Number: 1529. 1996. Ullman, Gerald L., and Dudek, Conrad L. “Theoretic al Approach to Predicting Traffic Queues at Short-Term Work Zones on High Volume Road ways in Urban Areas.” Transportation Research Record: Journal of the Tran sportation Research Board, Issue Number: 1824. 2003. Walters, Carol H., and Cooner, Scott A. Understanding Road Rage: Implementation plan for promising mitigation measures. Texas Transportation Institute, College Station, Texas. November 2001.

PAGE 127

117 BIOGRAPHICAL SKETCH Diego Federico Arguea was born in Jerusalem, Israel, in 1981. His parents are from Argentina and he grew there and in California and F lorida. He obtained his high school and International Baccalaureate diploma from Pensac ola High School, Florida. Mr. Arguea also has had the opportunity to experience o ne year of overseas education in Madrid, Spain. Mr. Arguea completed hi s undergraduate studies in civil engineering at the University of Florida in 2004, also obtaining a min or degree in business and an engineering sales certificate. He was licensed as an E.I.T. in the state of Florida in 2004 and has consulting experience from two internship e xperiences with civil engineering companies. Mr. Arguea is currently a masters cand idate and research assistant in the Transportation Research Center, also at the Univers ity of Florida, Department of Civil and Coastal Engineering, and he will be receiving h is Master of Engineering degree in August 2006. In addition to his studies, Mr. Arguea is active in the University of Florida tennis club and Institute of Transportation Engineers. He was also selected for the annual Eno Transportation Leadership Development Conference in Washington, D.C., and will be attending the conference in May of 2006. Mr. Argue a plans to pursue his interests in transportation engineeri ng and work in the consulting field while maintaining his ties to transportation research.


xml version 1.0 encoding UTF-8
REPORT xmlns http:www.fcla.edudlsmddaitss xmlns:xsi http:www.w3.org2001XMLSchema-instance xsi:schemaLocation http:www.fcla.edudlsmddaitssdaitssReport.xsd
INGEST IEID E20110217_AAAADK INGEST_TIME 2011-02-18T00:16:30Z PACKAGE UFE0014662_00001
AGREEMENT_INFO ACCOUNT UF PROJECT UFDC
FILES
FILE SIZE 97824 DFID F20110217_AACOOR ORIGIN DEPOSITOR PATH arguea_d_Page_036.jpg GLOBAL false PRESERVATION BIT MESSAGE_DIGEST ALGORITHM MD5
a3eea5e87403cceb4bd6c6b4627a5178
SHA-1
bab4cb4b0e707a1c5076dd8c65d6ee540c0ebdfc
78670 F20110217_AACOPG arguea_d_Page_046.jpg
b171216bad6ad0c446150873ae39454e
c9e80090e273f1c9e42a6bed6afe0b9ffbdb37e4
31302 F20110217_AACOOS arguea_d_Page_036.QC.jpg
d4b3998df8f967b4c92d46a8ef25f7ac
7b5c4f261dda03fe4b738e789795950a7b29a966
25512 F20110217_AACOPH arguea_d_Page_046.QC.jpg
c132584699e529ee031ebfbbd39705a3
d2fef75b3e4ef999d57618716415cde919d60dbf
106334 F20110217_AACOOT arguea_d_Page_037.jpg
d6028fc9c84eb4361f53ffcc84b8c5b8
ecbfbf99953a7b6c5bf0d47153f73ad8edc4a9c3
98740 F20110217_AACOPI arguea_d_Page_047.jpg
533f357ea067095b9b206a44f5154bba
6b8cc7e6cd0907686c11eb6a956ca0eea1225347
98831 F20110217_AACOOU arguea_d_Page_038.jpg
28c165a5f71580188cc1df8be99796f7
d7cd074a7f75e8df4870d8abefc1da46c203e114
99414 F20110217_AACOPJ arguea_d_Page_048.jpg
57716683572d61e35ca727ae8c111c65
c46b22a42e6f139d969b9d28dbe9a642417fb521
32831 F20110217_AACOOV arguea_d_Page_038.QC.jpg
9de46d6ebf2a040a4293350555866129
e61b067245d62ddf0d36fec0f77ab4662a887fc3
95561 F20110217_AACOPK arguea_d_Page_049.jpg
72df0062e7bc2eacdd57fa9654af1584
0d44553ee766669a1b77f61716d6483ac8502e84
96441 F20110217_AACOOW arguea_d_Page_039.jpg
a07c30db2106203532d62ebcc25e284b
0ee189cf96f408e75ee02bda9a37d500e5f0875e
31901 F20110217_AACOPL arguea_d_Page_049.QC.jpg
34e659e36a9a7663997abe2172c4950a
9ce162fb4e02cfd5662a8828d4cd15f8597b9d96
32256 F20110217_AACOOX arguea_d_Page_039.QC.jpg
c970830e9d9c6b8957b969b273642797
c0ed2a81d6dd472d13901c9ac4598d045fb680c4
27036 F20110217_AACOQA arguea_d_Page_059.QC.jpg
dd85ad163f33acfc37ec9fa5b95b0fe1
e2bc5e073209dcae88ffd19e05b6a7c31384815d
67089 F20110217_AACOPM arguea_d_Page_050.jpg
9b363ec361b8969d337884fe76b69a0b
1d36d65e706238e4a0074cfb0a17240ed520f2a4
75936 F20110217_AACOOY arguea_d_Page_040.jpg
10415495de571fbc7a109e4c6b0c82d0
904ebe78b1a89a5fcbf8cef65fed570b14799fd1
54711 F20110217_AACOQB arguea_d_Page_060.jpg
e6cbdd1ed2054d0fa7776ab85142e143
94f51bd1caeb2fb3a64684c9fc42dccbcf559db7
20250 F20110217_AACOPN arguea_d_Page_050.QC.jpg
f69adeb62e288731ade373796c180cc7
da0e79841a68b846da30cf35054716dc7dbf7057
71358 F20110217_AACOOZ arguea_d_Page_041.jpg
2a5c27625ad24090157abd6e392005c2
9b2b4f795b59e76dd2bf9e2a722122979fe03304
18505 F20110217_AACOQC arguea_d_Page_060.QC.jpg
266f6aff5cf8343fa2b52cc3f15e7ce7
5abfc79ba5a2fb3939c4b163fd66fd051f61a5dc
28575 F20110217_AACOPO arguea_d_Page_051.QC.jpg
7c754e051b9b776783c34122065a4526
132d51e608786e9f59fee65c4a63be992b81807b
52594 F20110217_AACOQD arguea_d_Page_061.jpg
80a9cfe996c62af9e9c551fb6bf8b7eb
87c75e8617a3618bf5111c7de3696f3acfe7e617
96846 F20110217_AACOPP arguea_d_Page_052.jpg
39b72210b594a3ef7a1f3a6878df5a46
abeb31d2ef000243e017a342e4a748a0120c2df1
72166 F20110217_AACOQE arguea_d_Page_062.jpg
16a113c4bb625e87062a4e0b50885d20
25d73904f02416d6c4af3c89c44f43da390e05d5
48598 F20110217_AACOQF arguea_d_Page_063.jpg
7c2fd16d6ecaa2a42e991021adb03a4f
d323d44197cc0d93dc047065302ae582f735072f
57233 F20110217_AACOPQ arguea_d_Page_053.jpg
c766a2b823fed32e050b6977cbd3803d
74eb2e0d850719bbcb25e2bb688da8fd2e3c9385
17025 F20110217_AACOQG arguea_d_Page_063.QC.jpg
95a3b72cfb0ed008d0a0b733c70c4320
2462139d54793674213e480fd214977445277f91
19531 F20110217_AACOPR arguea_d_Page_053.QC.jpg
e191e6eca67ce435122aa5037b7775a4
e595a26c631f4b1d3830a674e3233b02ce906ef2
94476 F20110217_AACOQH arguea_d_Page_064.jpg
5ede6df697fbacdb1c8d0c13da87d4e4
b811d2c9ef4d0b03199ca94d50e9afe0d795de25
56110 F20110217_AACOPS arguea_d_Page_054.jpg
10a2400c5016bcb0198f57d6ec463189
f7802c7bad0014012c3b3c2f82e713afc9a21644
32039 F20110217_AACOQI arguea_d_Page_064.QC.jpg
263fd17fa0e5adcc0fb116b97743e238
372c47a771627a9f07ec13bb6df7cb11b8958c52
18253 F20110217_AACOPT arguea_d_Page_054.QC.jpg
6c7bdda4052a90745c3179dcf5a63fb2
b53943654fff86fc2ad3b32aabb80fa0c5b994d7
94157 F20110217_AACOQJ arguea_d_Page_065.jpg
360103d40bb1e74ae7be9c9797fece61
2495d8d6ccdc341d71ca3cff9d8705027a10a740
72489 F20110217_AACOPU arguea_d_Page_055.jpg
04726a14472e9397bd6a8dae68454db5
62de6312fbcb7a7289f734a94263be4db22be978
31163 F20110217_AACOQK arguea_d_Page_065.QC.jpg
226202ee45a031cceaeb416170759e5b
d373a3def9b9bf4986460775d4437fe449d54323
24143 F20110217_AACOPV arguea_d_Page_056.QC.jpg
686f9ffedeb64a9bf51e9f995aa7daca
b7ec7b6f1a0f439a348e23efb29f9c08b1fa1d91
95885 F20110217_AACOQL arguea_d_Page_066.jpg
4ff7b20d9a67fba06b46b5da2e959d4b
8d9fed683dae453745b578ac6690ab30d90a61da
51628 F20110217_AACOPW arguea_d_Page_057.jpg
e42612438b63fe094008883b53a59008
5b3444bab67181fb6564539eaeac1de49e9916bf
30488 F20110217_AACORA arguea_d_Page_075.QC.jpg
b6052f5d2ef25202bd1dc21a0b9528bc
0da42de736d726f7b2d89e8a4fe6371f225cb21f
83707 F20110217_AACOQM arguea_d_Page_067.jpg
ca40aa89742db134e369d10bba1d6410
a902c8f24c3890d346b7e0ab5918666533fd5ccf
85265 F20110217_AACOPX arguea_d_Page_058.jpg
3b38ab4d9024435a4a66e8811bd8bd15
f81f724e7f6892f3be00095c3d47d340f6350693
101583 F20110217_AACORB arguea_d_Page_076.jpg
82e6bcf759cb6b646a074d0152252660
8df8785359e74a2cb63c7426c1ae91150e37fe3c
28125 F20110217_AACOQN arguea_d_Page_067.QC.jpg
5ae03761162001d77e4d5a76c4dfe0e8
e09449d9e299f474317f22a0b6852c0db4ae5a6e
28302 F20110217_AACOPY arguea_d_Page_058.QC.jpg
9bc31f2f7aa809bec0bbce095c6581b0
2a474a07d5edc290c4e1731738c23b8f4d9753af
33045 F20110217_AACORC arguea_d_Page_076.QC.jpg
df04b832119c960f7bc71dd81fde5427
8a8777cad37094b53bf6d5418d5311ce7b13ce9c
76111 F20110217_AACOQO arguea_d_Page_068.jpg
877bf47b8f5884322ffc5efc6ac3d56c
5394ceaf3098e50086104f8047538b36c1fed031
82382 F20110217_AACOPZ arguea_d_Page_059.jpg
3e75bf16f0d0653129ab3ccbd971161d
a64844494dbf5cd030b4c5c00f93bb9ffbe12ecb
17241 F20110217_AACORD arguea_d_Page_077.jpg
56c3177593efc0a8a035682d2d5b39bc
43ede54a49dd66312eeb9225ec2c6b2282906f28
26023 F20110217_AACOQP arguea_d_Page_068.QC.jpg
e46c7be90e84c83f7523a227d3d916e6
cf7f95a7f0683d81de707b6fe62d66f279a47bc8
6166 F20110217_AACORE arguea_d_Page_077.QC.jpg
39e589d3336b4be343cd1be4985cb04b
9ae8a96c86961717b256228a6763956d39828c67
55535 F20110217_AACOQQ arguea_d_Page_069.jpg
5bc5d7d23296a865f6e2b88f72b4c521
dcffe7fc48d5f8cf1838c54a5068c34b765fb8e2
85978 F20110217_AACORF arguea_d_Page_078.jpg
bb2606d4c26c0c4389050aa66b7e565d
93c3468fbf21b1a1542704cdb936763d67c2dad5
28117 F20110217_AACORG arguea_d_Page_078.QC.jpg
fa4a51b926a6d4c06289da66a0c77033
18b286df276599ac82cce663a08e63f74035850a
18805 F20110217_AACOQR arguea_d_Page_069.QC.jpg
d1058564fdfc1cea1b538e65abd22ac3
0f047e8cb580c6d03a0af9a65ebaf98dd5f01c87
100113 F20110217_AACORH arguea_d_Page_079.jpg
5104d65ff4f7e93eb8d224b9892e6e97
33f0a5cfa62202883836b288ed0a7abea5093322
16198 F20110217_AACOQS arguea_d_Page_070.QC.jpg
0852b0bc60a3ba7edc209454acbfaf22
f8ae880c73df94ea5e8712c8aab16abb58ac61cf
32756 F20110217_AACORI arguea_d_Page_079.QC.jpg
28e33c0c1fa1af633ed34787bb2b4091
4ba25f446a0ec9a80d7e79e4638fae4437357539
57014 F20110217_AACOQT arguea_d_Page_071.jpg
05d6dbfef468d11565d8593bc1940da5
c9e9ae19f8dd7d61d5b60cb352730ec8fbb1225e
92534 F20110217_AACORJ arguea_d_Page_080.jpg
bd1b9da9ed0c83735059d4dbc30e3d4e
a8c5d87755dac053040a8cbcf7950846979c7e01
109989 F20110217_AACOQU arguea_d_Page_072.jpg
d5f583958f383b1d176c5219911aab8e
db26127234b6de994e9a959045e3ec38161cc22c
36066 F20110217_AACOQV arguea_d_Page_072.QC.jpg
d8d765b101eaeef1ecb4887cb922b980
dee12e206a34f4f2d53efb15030868a793ecd842
102806 F20110217_AACORK arguea_d_Page_081.jpg
d99c23a7c9a2391c03c2958af2aad8a6
190860651bb334df472efeaa08171d666ada84dd
34154 F20110217_AACOQW arguea_d_Page_073.QC.jpg
bedeae3ce1f6da811e735ed26647e2f4
5a6f0cce9f3103932ffb9cb4e8e477d109bf8959
33979 F20110217_AACORL arguea_d_Page_081.QC.jpg
ff52d42d822fe4654001a6ef395d56c4
e7af7b25617587504fef55fb350c9398fc49e7c9
103781 F20110217_AACOQX arguea_d_Page_074.jpg
4d38573053f5a655fb7b46684801b086
9ef2527e4f0406808ed9bbd1c973832681bce2df
13755 F20110217_AACOSA arguea_d_Page_090.QC.jpg
67995948f6d1fe3d3ffef2c6f7ece907
436e17c0204e0428822c6f0019f3d491fa564436
102264 F20110217_AACORM arguea_d_Page_082.jpg
9d346d7add6e8b1512f935aaf56d402f
d7640830dea5c5b21e9e69d0437754dd929ff85b
34012 F20110217_AACOQY arguea_d_Page_074.QC.jpg
8506362a556b3f3500874611dd85f013
23a5ef49e45445decc069b8f32dd038870405fd7
30254 F20110217_AACOSB arguea_d_Page_091.jpg
79e94d91614c149859d896101d5e49f5
7b7304a02fb674dcbe6c321abb109289afb815e4
33479 F20110217_AACORN arguea_d_Page_082.QC.jpg
23a1df6d6320699e3ecb19440cc9c4e3
ca8518070e58ec28a703f7bdb81fa74ae923f071
93609 F20110217_AACOQZ arguea_d_Page_075.jpg
acd0cf2ce261aec5ecb2951558ab812c
e9d3ddb61db9ee2549b6bd797cc0add1de75bcb8
11735 F20110217_AACOSC arguea_d_Page_091.QC.jpg
bbc2687bdf17311c5f4c89ab41187891
ef4585e217759884ef3bb42464d9eac524adfa56
95656 F20110217_AACORO arguea_d_Page_083.jpg
e7dcb7d4c689bc1b6acfcc9b663f6e44
7c3e215ac59dc7b100023e662982d948ee59f132
15139 F20110217_AACOSD arguea_d_Page_092.QC.jpg
9225e01847245cff9231ed22d3569dfe
fd155d45bf6d14607c205b1460668872a278c677
31530 F20110217_AACORP arguea_d_Page_083.QC.jpg
f537b9169e1537a3f77b5ad43ea719fe
56ccb2feef5330644bd0f3bc17d55efc7d90f6db
44733 F20110217_AACOSE arguea_d_Page_093.jpg
964f1b5c36d668ebeceb9dba325a2b56
44b287ad3e5c66f29b4352a8923ee1217065d1be
30267 F20110217_AACORQ arguea_d_Page_084.jpg
fd256a51cf3346c10ea2b5797a0ccac8
3379497bf493801311ca667486a81aece3c47cd6
16032 F20110217_AACOSF arguea_d_Page_093.QC.jpg
408a477ca17d2121e105e3b590b3e3a3
9c7b67733d3810729e6704ae6550e2919b3ee420
11610 F20110217_AACORR arguea_d_Page_084.QC.jpg
b8d35f12594c2b8c844292db895f8d07
9242fa5371f92980fafffb71e8608a5848411c8b
58247 F20110217_AACOSG arguea_d_Page_094.jpg
3bdfd73c5770bbf68b707b0288523fc1
257caffc5effb84474be788b3acfb07c2dd9fc41
20475 F20110217_AACOSH arguea_d_Page_094.QC.jpg
432c62ac3e80cd82ab0d85a3d9ce2d9a
8b0d5e280152273d2f3413ab8259ccafaf2d44a7
40430 F20110217_AACORS arguea_d_Page_085.jpg
9cf45710a846728afcd6f1145c44f86f
d13a2188efbc5df6af012f4a9e1a85eb1f6bf3ea
53785 F20110217_AACOSI arguea_d_Page_095.jpg
b0167785a5d1b3ccdb96e3a626f8b5f6
a24ffb9a6fb65ab39d9405929c2ed9494d068773
14886 F20110217_AACORT arguea_d_Page_085.QC.jpg
2ca797883189e8f014db90f08276b329
1d9fb989ced571f2787efdf51f850925cc4d0bec
19156 F20110217_AACOSJ arguea_d_Page_095.QC.jpg
1496e96cc7cb04d2f49ae1a03764c8c2
88d71fd48b6c34ba053a8dded5682acc1612e15e
16344 F20110217_AACORU arguea_d_Page_086.QC.jpg
30325f347ef216c200021c8c5f40c78a
1fd340150d030f8db57943ae6a3e34aab8893b89
46051 F20110217_AACOSK arguea_d_Page_096.jpg
d68eddd9ffa967895ccada455c949112
8dae798411a7a29d95ce2ff59ca6385c2c15ddf6
60794 F20110217_AACORV arguea_d_Page_087.jpg
742ca689e75125e9b25aaa0ac3e4c348
21d77f2af242dbe8e80267b2bf0cea97a8ac65a8
16371 F20110217_AACOSL arguea_d_Page_096.QC.jpg
96ff27398deef3c788561afe54f4da27
fc2347a3cbdb9529d93766626a89bcf3f43daeef
55970 F20110217_AACORW arguea_d_Page_088.jpg
6b2d554b0de91156fec2409b5c7595a4
2e20c8f7d9c15e57890294e33f757be7a2162120
93230 F20110217_AACOTA arguea_d_Page_104.jpg
8812c1f83c9e45c7ec04d39778deaa29
aa65d5943ae3b3475ab166ce86059046316dbc61
40087 F20110217_AACOSM arguea_d_Page_097.jpg
c12391280d5f18899a99e5b1674d7063
5e0e222a0e2baa1da04d1a4c5fcdb55eb02e3bf9
19475 F20110217_AACORX arguea_d_Page_088.QC.jpg
5c748edae98820208538874d255a127c
b602074283475409608184c7a2331530098c6a75
97005 F20110217_AACOTB arguea_d_Page_105.jpg
8f1b8afecd492ef51bf1227b7ba642e2
0a24aee152322cce8e6bac29590a8d5b7d2eee40
13938 F20110217_AACOSN arguea_d_Page_097.QC.jpg
d919a1c69ce5d7ef494fefc468a99b84
a74f8278d3de09ad0644960c5a8a1d5b775cfb52
16223 F20110217_AACORY arguea_d_Page_089.QC.jpg
367432a9de298d3c5d9393a79f8627e9
d5fc44c347270178dc4ce1fb66d6e9e2d494f970
77359 F20110217_AACOTC arguea_d_Page_106.jpg
1fb7aa076b4451eef7ea091da21cf58e
181a8e39822acb3821985a0ec7f7cabb238416ba
20072 F20110217_AACOSO arguea_d_Page_098.jpg
640c69328d22a4a725fdf70bc08e0024
9af3b51f55ad96c867542951df3b3f74b326a2fc
38257 F20110217_AACORZ arguea_d_Page_090.jpg
977c9a6df11aa71a77e0bc5f3cf10e33
6a1753bf85fdaf5a2e446e9731d0d13b7f58dbf1
26170 F20110217_AACOTD arguea_d_Page_106.QC.jpg
3d4cc83769ab25613fb0cf80c0f62172
639050ff6cb1651f655e86ee38e97000fdfa9eed
7540 F20110217_AACOSP arguea_d_Page_098.QC.jpg
53aae5cbac4cfe7bd55457157006a546
aeb341486cb5c791fa210927bd5386f6e9c19a7f
44347 F20110217_AACOTE arguea_d_Page_107.jpg
e6da3a2d5167762467c5b14b3f6c7cd6
a9f86f903b8580ac1232d0614c7016c89558aae5
40336 F20110217_AACOSQ arguea_d_Page_099.jpg
23e8fb5c7556dfb02ddf86bdadb2299d
3a58a4abde1da4c6132bd6b30648a5b5ef3bb4f9
13418 F20110217_AACOTF arguea_d_Page_107.QC.jpg
e1bb752734c96630e4f1e05220c53a5e
65b5abb4d4da6dac754aa44ffaa458ded19f899c
12696 F20110217_AACOSR arguea_d_Page_099.QC.jpg
b2304597c9c5e7a720d97900fbe8a7e8
e74e3a7c70ff30ebe7bc0be7c09b0c99efa41938
71665 F20110217_AACOTG arguea_d_Page_108.jpg
84e77a13cb03adb99b1e249d2aa96dab
900287d0f64e70147bc68c88e4483e1e66c65c30
44429 F20110217_AACOSS arguea_d_Page_100.jpg
8fcc9349336c4b5d63c5731e5bec14e3
a6b6fe280e4f9476f87c93f9abae699e86b7440f
22403 F20110217_AACOTH arguea_d_Page_108.QC.jpg
90028b51db8f58e834ab07bf7f2d217e
ca3406658be1d61c47633f3ebc9900fbdaaa4bc6
58476 F20110217_AACOTI arguea_d_Page_109.jpg
f4cb9077627cac4d8ec4cd55872b50ff
56ed455564bffdeac51424778f268379e341cd8a
13408 F20110217_AACOST arguea_d_Page_100.QC.jpg
b956904c3273476d4bd8c8483d580489
e38b5c9de8c8bdc6caa27aba54fbf86a300a17bd
18655 F20110217_AACOTJ arguea_d_Page_109.QC.jpg
56b301604121f6123481964aa407a2d0
aa2116f879f702c3cdd9295e73e12125c3ed390f
71439 F20110217_AACOSU arguea_d_Page_101.jpg
81dd0c428dc28a9199abb4dad43c842e
c327c468dd3f022dac09a8da1432ce8f47b3e111
86459 F20110217_AACOTK arguea_d_Page_110.jpg
ee30036764ef478991378f76a5bd4c5a
4a33b174ccd3272caa4a9aeadcce2f3dbbde2b47
21533 F20110217_AACOSV arguea_d_Page_101.QC.jpg
7f04ba7cc099501c69a83472d3a4a62b
116feae66dd8f6d48cdd4e82751569196d6f3857
26991 F20110217_AACOTL arguea_d_Page_110.QC.jpg
c396ee09d3a1b26c5cf37452de3e29ae
3baf73d58a117843941dea138e5a650e7acd35bf
58467 F20110217_AACOSW arguea_d_Page_102.jpg
ca62df4006994d67fdad0b7e5794eafb
ba9fa9024a798b3ef5642bd6f18e521c0d766bc4
94052 F20110217_AACOTM arguea_d_Page_111.jpg
89c32cfe7a8311679270de7ee2837a03
eff20a12e8d8bac042a15c0838a9deb945d2a16d
18667 F20110217_AACOSX arguea_d_Page_102.QC.jpg
55aaef1d8f2355fa27366d6136090095
fb52bfd4e95402da1c1c558174a89e6f9ef2e506
56244 F20110217_AACOUA arguea_d_Page_119.jpg
3ff7667c5cf9f21c50a90451aa31f4df
2ce863ec78896ac31e8dcb30d9d33b4695de67be
30168 F20110217_AACOTN arguea_d_Page_111.QC.jpg
bb8931eea4bc12a0e799ae68d0e922f0
7b28e3559b2af08d7aba9d0180d6f0cd179ccffd
86438 F20110217_AACOSY arguea_d_Page_103.jpg
1a0265f166ed6a5c17ad41bfbb04998e
fbf6e8500ae04ad7c668489a7c2b20d5a236ecf3
18676 F20110217_AACOUB arguea_d_Page_119.QC.jpg
c3fad6e396bd19bf700ca8d5f1d0b581
2931d0129938eb029655aa20b191a0dfafb42aa3
124701 F20110217_AACOTO arguea_d_Page_112.jpg
f002fae3bce2807faee7cb0dcbb0c5e7
de2e89a777d1c48727992efb49845d57d631064a
26977 F20110217_AACOSZ arguea_d_Page_103.QC.jpg
a010096fd1447b536fc7907fdeb89d15
0996d2245ecdefba72697111a9bb19b3f59a6059
49395 F20110217_AACOUC arguea_d_Page_120.jpg
50d2e971fcf537043de1ad2b1c330e13
0457bccac77152fc14d2d5ce422bbd3bd0adc8b4
94574 F20110217_AACOTP arguea_d_Page_113.jpg
87553038c1f9af1c2acd53a5f7cb3135
9d18c97e603d1e9ccb5a0b28c5020413526b5dbb
17109 F20110217_AACOUD arguea_d_Page_120.QC.jpg
894c3912272dddd070bf0b27f13d1214
61b6da449840caa533ec184745f071ad94e3de76
32364 F20110217_AACOTQ arguea_d_Page_113.QC.jpg
9e7ac2455ec76eece13bb6526b087029
d6e740ab9fe78325e98be335096fd53209515a06
54844 F20110217_AACOUE arguea_d_Page_121.jpg
d0c7a59a2b2ee6ffe204817b90dbe394
5083bbee248c367a21dda0673540f810971aa6e0
23884 F20110217_AACOTR arguea_d_Page_114.jpg
6a0626ac4d21c2478b9ccaaaf30ff134
f4a1666af5125f3628c9489644917b21af3bd166
18315 F20110217_AACOUF arguea_d_Page_121.QC.jpg
a167b0ae9c499f1d363fb2197b383583
596a88e78958df118bc416098dc4fc66cef3f5dd
8015 F20110217_AACPAA arguea_d_Page_044thm.jpg
055dcc78cc0a363bbad86caf760d3d6a
ea0a6fcdcf594e9a84a55b2391737638b78e9565
8670 F20110217_AACOTS arguea_d_Page_114.QC.jpg
f7a1abefc15c7f3d0a92f101a2bf25e6
80050dff0d724c2c1e4e6ffede70e1f187b43a80
20175 F20110217_AACOUG arguea_d_Page_123.jpg
ce26c2aa735b539b7a30b64068761dc2
567d0c0492de4823372cf96ecbac516133b5d759
6510 F20110217_AACPAB arguea_d_Page_046thm.jpg
d2fe955d2258f9bbbdfba7d1d644e504
cf7848efc5cc5ceedc3ae1096a843053c60d7aea
75565 F20110217_AACOTT arguea_d_Page_115.jpg
3bc426cc13a9fe56cd257be718403690
e681761a9ea1266404985a1581de594d23467742
6674 F20110217_AACOUH arguea_d_Page_123.QC.jpg
3dd1c59912ee70fff647f500b96ee5ff
baa57bd2a787db67066212d363d71820ac426c26
7853 F20110217_AACPAC arguea_d_Page_047thm.jpg
ae8dc1479283499a38b8c118768874d6
ab16a365dda1611a8994a05fe5d441915ac97f35
106601 F20110217_AACOUI arguea_d_Page_124.jpg
bb090b14cf22ba18d9c67166acf0e9f8
eda610d5b5c913f76a1ff7692964fcf6c55019e0
8293 F20110217_AACPAD arguea_d_Page_048thm.jpg
cc333687ff09df310a8cb6b60460e751
f4d9dc271648b9004476ce813f9d3a849f7a71f9
21740 F20110217_AACOTU arguea_d_Page_115.QC.jpg
8c5492ea43a1faed531bd91174324eb7
052d25eb390a72dce63717184e90d12c43b8fe4a
30259 F20110217_AACOUJ arguea_d_Page_124.QC.jpg
8ccce86f7f47603ab97c6e72611a048c
58d0b8f2648a9f5b5cb0f7b8cf5026647b8cd7e4
7892 F20110217_AACPAE arguea_d_Page_049thm.jpg
78ecc3cd8c682dd1ae523e710cdd9d60
ba25764bb6f2fd46ae8581bc5571d6ff401af9a7
74608 F20110217_AACOTV arguea_d_Page_116.jpg
da68859e8b075d4015034eafda33d454
04c7005ec6ec633ce5bfa35f92d1a897b32955c6
132373 F20110217_AACOUK arguea_d_Page_125.jpg
2825602858dbb1584bf25276c8651e32
a296a9d2b390fe1490824107259251ff82412786
5724 F20110217_AACPAF arguea_d_Page_050thm.jpg
bb84760308383350b51e02ec7a149e4d
b74f0f14d569430ca92dc3509eec2719fad6ea47
20854 F20110217_AACOTW arguea_d_Page_116.QC.jpg
c8ba2e9b27f27f08936ebc57c0bd7a05
a1bc119794c6a63ce11b3bba33797d00651d404c
36775 F20110217_AACOUL arguea_d_Page_125.QC.jpg
fea4b43d5033378c15050e1daf8244e4
95ce9542a00b2e7b06f1bf1d0270a7dc0263adb3
7185 F20110217_AACPAG arguea_d_Page_051thm.jpg
32240f83dcf343f9a1772274de242f4a
36a10c185c7929d7b05281a3afab5db25637a6f3
61874 F20110217_AACOTX arguea_d_Page_117.jpg
d62f400475e7a54548584a380c220938
8f106378bd5874f0ce7ca301fc00971f849461c0
1051969 F20110217_AACOVA arguea_d_Page_018.jp2
45d806e5e25d425414362cd875eda056
8b7640a3aab637bf0e14fe3cba33d6d4554e4927
19550 F20110217_AACOUM arguea_d_Page_126.QC.jpg
f20ed16e077bd4a75009edbc8c265fe0
f9b1356dd6e76d814913a3a0fafe62049c6ec50b
5547 F20110217_AACPAH arguea_d_Page_053thm.jpg
fb10fbb7e4d75ca1ed964366253e1eae
e481aa4ea933636f6dacea316d20b8903a0386c6
19967 F20110217_AACOTY arguea_d_Page_117.QC.jpg
27bf536fd81ab9d86bb286caa5add228
232b33377a92b42de8ab7ae992d9a7bb3d0fd073
922422 F20110217_AACOVB arguea_d_Page_019.jp2
d394e3546d9429b79c53c815887be0d7
b7a2b8a4500fef73a4200664afe0c5b239ea689f
79321 F20110217_AACOUN arguea_d_Page_127.jpg
3d6699d957f1c7f79d6ba4939b8a8ac9
4dac267e52741611097068b8f9de2268c1d0bf7e
5634 F20110217_AACPAI arguea_d_Page_054thm.jpg
147558d0d2c30288517171c1f1e03b32
4f29ba5382c080751503858efc8cee930b59d466
34346 F20110217_AACOTZ arguea_d_Page_118.jpg
07eff0f7d1f82d2cdbea7075ce339004
3edae3dd02021ecfd6d277cbfb94de1fcf17d8db
1051986 F20110217_AACOVC arguea_d_Page_020.jp2
f594cb4b59e8e9070dbb40f6c6ace18f
91d0bf8ac2dba85efdafbf8419f134ea9155d99c
249743 F20110217_AACOUO arguea_d_Page_001.jp2
e1aec5e1ec005460cdcd5d49355f4f90
f26a2905ed1c9932b86a266a8d72f91ef93ff72f
6706 F20110217_AACPAJ arguea_d_Page_055thm.jpg
a840aa9fab8b2675b818b615ed24ce28
2eaade4d6efbeabc7fd3b70710147393831f5f4f
903698 F20110217_AACOVD arguea_d_Page_021.jp2
763415851858f16172d4b8c99b8ef925
07005ce65c3c05e606e6bffc08e53d915b43942f
36036 F20110217_AACOUP arguea_d_Page_002.jp2
f047273e469a62770e6d55e7784cd059
0dcccd2bd5e40d95ab95955b2b05aaab87a73bbb
4979 F20110217_AACPAK arguea_d_Page_057thm.jpg
28fd9343af467c1534ffbbe6c8d33234
917cc998f9509d26f06a1e0e3c2da1df099254bb
1051971 F20110217_AACOVE arguea_d_Page_024.jp2
2aa1742bf94c71a649a3709b3fccc882
6970adc82498ccfc005cea637e8e8b8813346bd9
675969 F20110217_AACOUQ arguea_d_Page_003.jp2
8e78520b226b29c974f666dad33cab18
8431ac725380213e32aa7cf668e749a9aa73251e
6779 F20110217_AACPBA arguea_d_Page_078thm.jpg
22e4ad91bc0e01cf9d9e1fa6a7246ac5
068faffeb8db18926919ffdb7f2c8379d3ba6219
7710 F20110217_AACPAL arguea_d_Page_058thm.jpg
56a2212b98a8a860edea1ff17c59a76e
770a4ac422d50ea72a4fe2b7128abc967084b6b9
1051967 F20110217_AACOVF arguea_d_Page_025.jp2
bfc3e5c6c3fa43688b49de13ed9901cc
e0af5f63c4a0595c64419d8a8a87c9416fa19fce
382514 F20110217_AACOUR arguea_d_Page_006.jp2
9030fd734a70d65461d6ef6da6f96d76
06ec6f9ccbdb7dd392623a96afd3ce6bce7db8a4
4970 F20110217_AACPAM arguea_d_Page_060thm.jpg
5155b106fe4c169192bbcc422268a6cf
b4764224d953af4cbb268c9721a8fc6c2c9d5e3a
1051980 F20110217_AACOVG arguea_d_Page_026.jp2
018fda87118769ab1cbaacf3541f4aa3
cc4021ded5d566a8a038aadc7a00cafc4c173edb
295882 F20110217_AACOUS arguea_d_Page_007.jp2
6845e150d9e697df30a3208d68fcc7c7
34cc0b706c1011cd9f2bc1df4e0743b5283bc427
8410 F20110217_AACPBB arguea_d_Page_079thm.jpg
7cad3473f42b5772dc68a175c33c66f9
3f984424d25be3fae6bd8292387eff2c89f0f466
5008 F20110217_AACPAN arguea_d_Page_061thm.jpg
5ca0c8de1e21d86f1749d935b4b2999d
95c1b7ca81058e4413af05cece9c98304fa3c6ae
1051960 F20110217_AACOVH arguea_d_Page_027.jp2
984f888399740f3600dad6e064162d3e
ac56301132aba97b518ba764761eb4fee21c378d
929049 F20110217_AACOUT arguea_d_Page_008.jp2
27eb18bc01550587b80315d1bbf580ba
2b195d1cbd073f9985292652aa7ff4dccde53b35
7836 F20110217_AACPBC arguea_d_Page_080thm.jpg
d6472b4a7b331e009ea483e1b1aa0f2c
ca00483766cc793a4ca2b5abb26dc84d2c97d665
6503 F20110217_AACPAO arguea_d_Page_062thm.jpg
24f3c7344e95e655e1195bbda4bd76e0
777c007d9c0e697fc0afc69e1a49d2ad95789c97
1051984 F20110217_AACOVI arguea_d_Page_028.jp2
42d3ceb0e97143dedba22580d1f2eae7
2467baeaec9d66ed50d5ad10a0e2f422b735f520
900431 F20110217_AACOUU arguea_d_Page_009.jp2
b6a361d9e001f7ecc7ec916a7e573fc0
fed494a12fb5a1c83e024330514b8ab070cbc923
8317 F20110217_AACPBD arguea_d_Page_082thm.jpg
a2e1c9223c2e5f2837cb15038886d65d
cdce37414fb0fcc9f7b6ba06463327083117d571
7860 F20110217_AACPAP arguea_d_Page_064thm.jpg
eaa63abd3195f643617e15f7edb6195e
d6ca9b6c827083b519a0d9f774ae1ff9c770603b
1041009 F20110217_AACOVJ arguea_d_Page_029.jp2
034ba1e8e9db51debe1edc108e3b7a95
56079134b49125dec402ca24887c174ba48d1819
7667 F20110217_AACPBE arguea_d_Page_083thm.jpg
4e08b4f8605205955f4faa08ad99c03d
1d6a9f054e50a31cd15f3838f5f3f81966c0aa52
7962 F20110217_AACPAQ arguea_d_Page_065thm.jpg
d7df9e819c7f682f0bafffb8c0371bc6
6d04aa8780505fb47a9d3d16fbad9f9877452f7d
1051976 F20110217_AACOVK arguea_d_Page_030.jp2
d4bd773b1d1deaa865d1caf5d05f69e3
0e1b4161efc08165269a889521add10e73437b17
502784 F20110217_AACOUV arguea_d_Page_010.jp2
a04e36c2a89de375090096e044125a35
2c96d542b1d176e09a64f4703222b91f1e2a4fdd
3273 F20110217_AACPBF arguea_d_Page_084thm.jpg
55b3578f416c0b03603c373e7d5f2226
2fd399590d0a989485b8926218fcaf57977984fe
7221 F20110217_AACPAR arguea_d_Page_067thm.jpg
fe8c1bd3aaffadc3f4d5fa0973599057
a3a13d7478a891212fa435cea0d66efc0e69e6ee
1051955 F20110217_AACOVL arguea_d_Page_031.jp2
65da12e521acaa1c54131356b52cab18
825f59f7302dccd1e6eab84d74b3b9cf491174a3
1051981 F20110217_AACOUW arguea_d_Page_012.jp2
c0a54f0c9d249814132f9e51cddbe59c
abe0d47ec5d4c22e583217c669b94f16ab7e401d
4942 F20110217_AACPBG arguea_d_Page_085thm.jpg
2fc6cafb38985203ed2084c4702d9885
081e9263a038bdeb14db53785d6ad10e19db67c7
4997 F20110217_AACPAS arguea_d_Page_069thm.jpg
5fee70b9e9c271f5d293a2c3882ee344
46ce6b0d7ea16dbc2f74faff1993f4bd50359878
956303 F20110217_AACOWA arguea_d_Page_051.jp2
42414bf154c0ae19ca18ae838b6b0784
4ca3c999a26510700e3736f48d1fe4d121195306
973193 F20110217_AACOVM arguea_d_Page_032.jp2
d7e72eb420548d85ea97191d28224ed5
2b0f84bd12d89c02dd9b4f41bb625af04261a8d6
F20110217_AACOUX arguea_d_Page_013.jp2
60af423dc3dee1ad0e304b1f886257e6
352cf5c740ab2a146ebe61b9622652892b70105f
5364 F20110217_AACPBH arguea_d_Page_086thm.jpg
40f98fab0aede566c8b2f6b8780a4c7a
da3c265c1fba1d56e2ee3e0ed0c9d3a9df7df842
4369 F20110217_AACPAT arguea_d_Page_070thm.jpg
fb585d271ca0123d6790d8bd27478e2d
ef308db9a7f4101dbd94d53a5fbff0723b41726b
1051923 F20110217_AACOWB arguea_d_Page_052.jp2
ba373a6f53c2a7d44c7a9802b0754d31
530b8a302d13a8fee05e6df67fbab4275d4ddd00
994945 F20110217_AACOVN arguea_d_Page_035.jp2
bfc06afad593aab09aaf248a9d89a8bd
6d30386a94a7f4c5aa5865ad804c7e9ebe15c3fc
593479 F20110217_AACOUY arguea_d_Page_014.jp2
3be3750c0c0c7cf8786dcc5a79996c16
65a5fbeea37a1fa190ae7923851f3f0eb33880d4
6625 F20110217_AACPBI arguea_d_Page_087thm.jpg
181a54afe9979a07bb754efaa81aae0a
85846f02ffad50299fb7292a6ed18cefd1be64dc
5280 F20110217_AACPAU arguea_d_Page_071thm.jpg
3c46e294289371a5e671cffbbaa548ac
da13be42df4033892e226b2a0a78cd6ea590b352
736472 F20110217_AACOWC arguea_d_Page_055.jp2
53289b014647eca64c3f48a4d8fd1075
55dca6e8704287578747a2d03367eaa81aac5f0a
F20110217_AACOVO arguea_d_Page_038.jp2
d3c62b171e3481e8907c311dda848cda
10055d85efcdd71676dab5ae6453de38d5e543fd
1039385 F20110217_AACOUZ arguea_d_Page_017.jp2
47c59b3dfb2d8aaaf5e9e32e27091b41
a58da21ce9add67282c1993094590e7e844d96ff
5392 F20110217_AACPBJ arguea_d_Page_089thm.jpg
01cb5e691b9c277846dfd652479b9ae9
8ef17d58d35ed22d953d3aed4db049dce1ba945f
8597 F20110217_AACPAV arguea_d_Page_072thm.jpg
056ec996840226be0af81519a41a216d
7d1e8f5f400e3f15b2ad8aaf63e9ba9bd7ccb3fd
725397 F20110217_AACOWD arguea_d_Page_056.jp2
16a97685589a9111273ff572070ea0fd
3c5689b91c07cf853d9874b7a7fb36391c66492a
F20110217_AACOVP arguea_d_Page_039.jp2
0fa33a9ad076f568f1ff7bb02888c627
f9a91496e1740e1347cc4e9518473c0dcb305fe7
4537 F20110217_AACPBK arguea_d_Page_090thm.jpg
25ee31405791a839f2cbdb491d6d1abb
c97c7f635ec2b9177e062bca20ec856ce12d88bd
8559 F20110217_AACPAW arguea_d_Page_074thm.jpg
04e8b433e1252e31e3e2f935acc1c3dc
07092d717419f04edcb20eebd670e8ef0e07fe01
493455 F20110217_AACOWE arguea_d_Page_057.jp2
e14b2e0fde30b37d5cebd2c582d68660
6c2405ad2219d970275006cac7a4506b530576f4
770850 F20110217_AACOVQ arguea_d_Page_040.jp2
24496b3a313f0c1d831cb05d5a67ebf2
e16f5ed87016786917fbde8596cb4dd8329fd1d5
3519 F20110217_AACPCA arguea_d_Page_107thm.jpg
376241c564e2ad0f838e251e6c16c8a3
87127d27840a4c124980bc527259d8d51f29b7d2
3228 F20110217_AACPBL arguea_d_Page_091thm.jpg
4d7a829f4bb1d10ff14ca27e85f2abc4
9e504587357d812f093bc46dd92f6da67bf25eef
7508 F20110217_AACPAX arguea_d_Page_075thm.jpg
5d5a6dd79bde784a5c19a3a08f8983b8
2276c01cf6d0e4b4a4344e06d14b3e2cb768ac8c
871893 F20110217_AACOWF arguea_d_Page_058.jp2
2856a89896e3c162d6c9d4e170f46c92
1880a11a18233bc4652c702de8b2043dd8a1fdfd
745473 F20110217_AACOVR arguea_d_Page_041.jp2
df2fef0864bb3bd06e978f1564ca2269
0a4ad34c597feee63fc400cce5bd1dcbe9250c31
5905 F20110217_AACPCB arguea_d_Page_108thm.jpg
7b82562e3e6b6dd638518fced0f10204
99b3076da18ea1986fbc88c8dcfb0e253d6dd57e
4891 F20110217_AACPBM arguea_d_Page_092thm.jpg
4f45a159b49518b60fbe4a2f7f4204ea
05171f5b6689f4ade9a53247a75f218dd24384e1
8381 F20110217_AACPAY arguea_d_Page_076thm.jpg
1bd3240914288b7a197ac5ea881e5df3
1dcc4ac459cb54be9507f247e6a7405ce9e63193
838169 F20110217_AACOWG arguea_d_Page_059.jp2
e2891d363ba6d990c0ff66bfd70225e6
cf7ef746f485d6b7ef0ee2705b5d66a4db1bec5d
571495 F20110217_AACOVS arguea_d_Page_042.jp2
093b3cfa097e0472ac89f110b9ef8fff
a8e20e91854e017a16db6692e76e571114104593
5070 F20110217_AACPBN arguea_d_Page_093thm.jpg
06bf31fb6c35234eac18d95cbca92e8b
dfee6ef96084744d85706a10cf10772c995cf1ed
1632 F20110217_AACPAZ arguea_d_Page_077thm.jpg
4d66ac662795cddb0b9f7152ea65fa88
50da53953fee426b60d3fd14cf096f5ad7d9ae04
725434 F20110217_AACOVT arguea_d_Page_043.jp2
50043b61c7fe4eb0a5660702e6730c4d
9a8b44f19cbb25061e00a8bcd0a5fa5eff0f0841
489464 F20110217_AACOWH arguea_d_Page_060.jp2
9b5f2a149aa468df06a02b54eea3730c
d818a7972d1fcaf5582a0014d20373cd4fb21b4f
5463 F20110217_AACPCC arguea_d_Page_109thm.jpg
5d4b3875ff52ab0ff45f7be50f7a9432
92a1f4f8013b6fa0419e5af027b43620a8435f30
6257 F20110217_AACPBO arguea_d_Page_094thm.jpg
2a5cd62719732c93f0f0054e89260b35
5790d8a566a97ed9e6ba235f4f6be17673c4a095
1051974 F20110217_AACOVU arguea_d_Page_044.jp2
046399e5abd6ac86a9babecb6eb3c430
8ed022ee08377aef708f9861aac237ef2fde7f01
503917 F20110217_AACOWI arguea_d_Page_061.jp2
960e1f5ff196c3468ce6f194faa6d888
2a80d588790d39906a2e3dcd3a8314bb75157bd6
6995 F20110217_AACPCD arguea_d_Page_110thm.jpg
d40deeaef326372f976b58248bde05d1
c3118089e3245425f51fa98bb48ca6a3b32ab983
6098 F20110217_AACPBP arguea_d_Page_095thm.jpg
dc2e4ee76e7ad1fc3d0f1cec92dd0b14
f93cada4bb0bd74b5367aaa07372766d89976558
809775 F20110217_AACOVV arguea_d_Page_045.jp2
65e9df1777981e72813851db51bdf256
3dd424aacc51b6abf48bea872610504ea110af9b
715488 F20110217_AACOWJ arguea_d_Page_062.jp2
8953ca11a10511ac88b00aea1c8d117a
accdefc07ae49d3417af8bf522bf462463076275
8754 F20110217_AACPCE arguea_d_Page_112thm.jpg
63304dde7023d07defa20b112547b7d6
bf3582e60e3e5815396115270f281bbbca613b9d
5301 F20110217_AACPBQ arguea_d_Page_096thm.jpg
f617718324594c674174796a9e516186
f12154ab0a314ef56b0f9f329449070d79c2899c
417945 F20110217_AACOWK arguea_d_Page_063.jp2
dba46a9a81770a1e54f26aa06c7d8796
58786b2a73e4cd60d6714a6e234b181e7fc31adf
8100 F20110217_AACPCF arguea_d_Page_113thm.jpg
c3c51ee2c8da25a2f3e7f38f8c907afa
9569ead092eaa73fe1ce0b9224c3c7901e41e856
4834 F20110217_AACPBR arguea_d_Page_097thm.jpg
ac29a4e8f183c318762067c3d7a296c0
7ddf7ae22633f73a3c7294be80a3d948acc72e27
857141 F20110217_AACOVW arguea_d_Page_046.jp2
56437a2ac5351314219c08a7e5253dd3
69c9ea09b88efac57d539e75aeae747433136467
1039355 F20110217_AACOWL arguea_d_Page_065.jp2
0e1a7fd107f04e3f74ccf49c08251ac1
e89a86eb91e32e6589d92326c46ae09648629cb5
5818 F20110217_AACPCG arguea_d_Page_115thm.jpg
e58c6839b31b364541b9a6b7151a4854
59b07cb435b0379f071bd9f5e51630746a8ec91a
2465 F20110217_AACPBS arguea_d_Page_098thm.jpg
b817e84ae96620b3995d68a96dae2ab2
280785b987dcc659bd232bdab784ef2d01c26d6d
1007651 F20110217_AACOVX arguea_d_Page_047.jp2
1bca364bbc4a00174d2c6b9680dc6735
6ee757f35b829b2ed7a026927170b14066890741
378268 F20110217_AACOXA arguea_d_Page_086.jp2
87b4bc1b35710e250132d791a7932cec
c6ba6c0474339754c22c1d0261fb16c915b8d351
1042781 F20110217_AACOWM arguea_d_Page_066.jp2
8b024c66b93ee71cf167fd38eb664c48
c8d424a2e91b5471826d6568d9aa26e19540d429
5312 F20110217_AACPCH arguea_d_Page_116thm.jpg
5ffd702678e6ba0e171b886f6903348e
63ae670b94f3866ac2ab3a729c37ea0baf134268
3431 F20110217_AACPBT arguea_d_Page_099thm.jpg
6755360732ca50d6bc1212d6b6aa2a37
7a4d0be4f6bec876af6b021546fc4397f7f305db
1051942 F20110217_AACOVY arguea_d_Page_048.jp2
9e1462c4a2ea782c26b06dac9d9a8abb
82f059f1ccfc53dd68c268f985aebf81f22222cd
555829 F20110217_AACOXB arguea_d_Page_087.jp2
94bf15eff68a4df4287bea5d7ad47e2c
2947d6878fbbb46878c0912483e9fbe9801e96e5
900591 F20110217_AACOWN arguea_d_Page_067.jp2
11297852a6582110d76e1d2219178d63
7a9467cc10abd3bdbe2629766cd69b8e1065a096
5180 F20110217_AACPCI arguea_d_Page_117thm.jpg
6cd5349e1f621cc8fa2760e90306ad30
f4dde8011b9cd76bbf0459701ab8c369724883f4
3560 F20110217_AACPBU arguea_d_Page_100thm.jpg
b06b8462d81064c90cbd96858a6d65e7
c2651f7b976f5c99f41722095b179008df5aca79
1043688 F20110217_AACOVZ arguea_d_Page_049.jp2
19b31515821e39583128f351e88ff68c
575ee4e68f928b57cde516c7b5664b5777abf412
483216 F20110217_AACOXC arguea_d_Page_088.jp2
241371f31cc43a372ec3ce5acd97af07
07123e4f68c428f3f7d55c4662e38e5ad6a5378e
591242 F20110217_AACOWO arguea_d_Page_069.jp2
5ae93117710318566eb1f84425e8d005
f2a633305fd1cfc02bf34ce3c8826a0cc3eaf189
3595 F20110217_AACPCJ arguea_d_Page_118thm.jpg
1227ce5f65d2219f7e043be2f3a95ea4
14e35eb4b3032fc7a860f439d7242a99a5a830f1
5717 F20110217_AACPBV arguea_d_Page_101thm.jpg
3a72bfe23c81c27f97f54aa09774b802
209ffcb83782f27fa03785718bfd34ab2f89662e
306773 F20110217_AACOXD arguea_d_Page_090.jp2
724f6c484bd70550139abfffd08352a3
e8017c03808f6ae7c74a5700773ec7af10b5d800
511069 F20110217_AACOWP arguea_d_Page_070.jp2
3864a62d7ad3fdfc2602fc7751d62281
4dc4dc7ec3e63e5649b5bd89ea957ac70a1d39b3
5059 F20110217_AACPCK arguea_d_Page_119thm.jpg
b1dfa52956045a42a105d05e0b2126ae
9a1ecbfc8e70246addc81c6df5b8d7be3e1ce788
5472 F20110217_AACPBW arguea_d_Page_102thm.jpg
967d567d7afc539f69a4a4f8e33afee4
cd8679561c2db352ab5902490679ffd4955da15f
370429 F20110217_AACOXE arguea_d_Page_093.jp2
cfe4a1265e3f85599aeb1dcc21c47b67
5d8414be32030c2b8ea51a434b29604053310c7a
622769 F20110217_AACOWQ arguea_d_Page_071.jp2
8cb2e3ab2b855b7704bd89687f99678e
89a99eb48488c86d4084e874b591edececffbffd
4314 F20110217_AACPCL arguea_d_Page_120thm.jpg
5f209654144fbe6077790ec545ce909f
7b88fcefc68732b6821144c783e2c8f3871e658c
7007 F20110217_AACPBX arguea_d_Page_103thm.jpg
7f432f2c4f612858efdc753cff77c103
3040f98abcd2d0e91998c631999e24924db30260
540898 F20110217_AACOXF arguea_d_Page_094.jp2
0e8377351f30d88a3493365805a19fd8
cbed60881cec33848a3c5f7c6af98d0bfbdb5475
1051985 F20110217_AACOWR arguea_d_Page_072.jp2
9613bb8648b6c28abea3fdc9cbe7ca8e
0366ea2f7f9405e4432736058cca433063165aa8
4728 F20110217_AACPCM arguea_d_Page_121thm.jpg
51efcfb2edc789f374c0b38174999873
50fc0d99f014669b7f76365a511eded767a2748c
7589 F20110217_AACPBY arguea_d_Page_104thm.jpg
1c90c267576e8ff87bcd8aa9d244dfb5
ee1fef78202d6676a39c58fc65f91fdef1eb9383
467942 F20110217_AACOXG arguea_d_Page_095.jp2
1f5413922548ece95e7600846cb6ded3
7253a40f9d7b8cc430842aab6c22ed771e364e31
1051979 F20110217_AACOWS arguea_d_Page_073.jp2
f98ba46024fc12a61c3bd05fc934c40f
377578a1d91a914d3301857b476be7cb12fb65b8
5580 F20110217_AACPCN arguea_d_Page_122thm.jpg
c526fbb20f8318bca02e61d4ca3e9490
2e2defba0c729536594da15044063fa6e52af7be
7522 F20110217_AACPBZ arguea_d_Page_105thm.jpg
85ff903f3d1042ce62436bf479936421
649ef6539aec756e0ac58651daa4cf06f88c74b6
324674 F20110217_AACOXH arguea_d_Page_097.jp2
a61d359cef82be637fa9b738e96a806d
6c8e624be3b1169c6fbd5b66ea9d1fc9796e9c5e
1051915 F20110217_AACOWT arguea_d_Page_074.jp2
cf4daa9f0b16a5715ac2ba16f5f590af
f0c3817eaefa11c997dff364402b2404f4648a60
2049 F20110217_AACOAA arguea_d_Page_081.txt
959ff1ef0776428666bc7c1be88ccd20
380b93d799d7455bf99eb9c0093f510098241a86
1866 F20110217_AACPCO arguea_d_Page_123thm.jpg
8daeebc324bf9c20a8cd6a6c7811a936
f9c98358f0e13c2f8721b7146b9c4071f02b47e9
153553 F20110217_AACOXI arguea_d_Page_098.jp2
ceb9eff000e625a94936e0afd0061528
e82889736a6cf38bbd4652bc256b47cbee9d89f3
1042581 F20110217_AACOWU arguea_d_Page_075.jp2
0c3666a6b20931cc3eda922b1fa7a612
e068b990b7e4771ffd2ededf3bf9b2c2f6ced88e
7268 F20110217_AACPCP arguea_d_Page_124thm.jpg
34035c80ce0da69dec0517da015ce542
7ccf9f6c6e8d1c36717955b70c490d5976490982
402799 F20110217_AACOXJ arguea_d_Page_099.jp2
a9c7e271d3b63cae8285d0a4e37ac54b
46499ff0781e1cb71d5f40cae63a698d4f03482f
160656 F20110217_AACOWV arguea_d_Page_077.jp2
add25c37b265451d7ce6c04342d0d121
5b97add044517d8dca03ba9c9cb1ab8f276cc353
8423998 F20110217_AACOAB arguea_d_Page_063.tif
63b904a5069087abaf737b4cfa05dd1f
c18db0e32f5c37a471633fb5866ba5c08578b143
4959 F20110217_AACPCQ arguea_d_Page_126thm.jpg
98293deea50f417eb6f5a718a17389c6
b90c4268491ff99b82cb5ae2ff5603287e121176
482490 F20110217_AACOXK arguea_d_Page_100.jp2
d81bd0a28f6b5955a20a2629a3c551d0
92644a88979d2b86174103bf2ee877e4bee6ac72
1033914 F20110217_AACOWW arguea_d_Page_080.jp2
66a6aa03836882e355aae3c7bf3bf40e
82f21b515df7874aa80d6507d9da1fc6099988b9
1014772 F20110217_AACOAC arguea_d_Page_023.jp2
f8fc7d0636c7600b058f1839481d505d
f281d6950ee5c8f3b07da057d42fbb19823654e9
6789 F20110217_AACPCR arguea_d_Page_127thm.jpg
c1de662a4de2e584552fd4180e4f0589
f42f60c65853291a6577da77ffa49ce3daff3c0a
802128 F20110217_AACOXL arguea_d_Page_101.jp2
3e789b8337bee79a9c3b18dcbb44f82c
344f36cd53e09adf278fd041290336e7b34de071
6825 F20110217_AACOAD arguea_d_Page_045thm.jpg
54deb2cd48a16887319ab77d15211117
9c02928dccc8366465f7d21f398901ac9d8cfa3d
2317146 F20110217_AACPCS arguea_d.pdf
ac1ab786eb9a98da87e470818a50937e
3b977d9941b6f7afddb9da275db175d104dec0c0
1051962 F20110217_AACOYA arguea_d_Page_116.jp2
8dbe6972863e79d5c50bfc75cdba70a2
f596bdf64d89fdb94cdb3f29f79aa07223236cf9
F20110217_AACNUK arguea_d_Page_117.tif
c44b7ce64c0c4f618035d7f06c58baeb
e6a0898f8181c707be1b48390a9b06fb39567c39
627892 F20110217_AACOXM arguea_d_Page_102.jp2
0cff86883737d0f458dfecf6e6a094b3
056fb07ffa5f669d071cae42641aac1f0f4a7603
1051982 F20110217_AACOWX arguea_d_Page_082.jp2
e2849edeff0d73c009e56da5294671f0
3006ac9bbd1ace06f6c5e5ed89790be3f73d7f48
18442 F20110217_AACOAE arguea_d_Page_071.QC.jpg
3a492eab64cd65ab1b15d1de23cb2ee7
40c80dc1e4915787a4b899db5ed68b0eceffc261
149996 F20110217_AACPCT UFE0014662_00001.mets FULL
96f136bc1ac3c843f2b32ce553912c1f
79d16ad356c4052b752b4fb2250990b9e023f536
659909 F20110217_AACOYB arguea_d_Page_117.jp2
c0ebbab6d7ecc7b1c13c3a6911ffc140
45cb9c47f43f11e421c685c556edde64f22d1080
6812 F20110217_AACNUL arguea_d_Page_056thm.jpg
a79242753527709575f73b50c6bc3fc9
d211309d16361c9311bc9f619ae03ed3f30aaeba
949240 F20110217_AACOXN arguea_d_Page_103.jp2
0e980daf8e049579ae26afca895fb1c4
0057f0aa906e7ac46073ec6cec444fc05a42b6fe
250989 F20110217_AACOWY arguea_d_Page_084.jp2
1737280c7ae16ae61e6442f49286005f
2b8e3a64b14dfb9effc944cfb4983b84cbf5b1ee
27374 F20110217_AACOAF arguea_d_Page_006.pro
3bd6ae696b00864f2ce2fb8ab29fbe7a
b6ac424170bbc1e183f6d7c7bca1bc4434642179
5113 F20110217_AACNVA arguea_d_Page_113.txt
da4aba3e5963ec33080b19a2f7b54ab4
777631795b3263daf851f96ddcda964487e56035
432402 F20110217_AACOYC arguea_d_Page_118.jp2
57d368082ac37ef540d8ca866f5d8719
5047f560296a30468605ebc395ceee573295b345
F20110217_AACNUM arguea_d_Page_060.tif
c215c7601f98c0f1b9fe194e8c88d093
b1eb665ff8b0d224e20bc83dee11be9314db3376
866078 F20110217_AACOXO arguea_d_Page_104.jp2
2f37c384d7a10002f10efa167706b7cc
b5d1f7fc66247ca24010c895497d44793f766295
342293 F20110217_AACOWZ arguea_d_Page_085.jp2
f76d03da913fd97f9d115c7803d30c9d
86703ffbeab6742d65a99cf6ffdb0661f160566d
205295 F20110217_AACOAG UFE0014662_00001.xml
5bbc72b8a73b54d6ccaf56dd71ef4ea8
ddca5ba6dabdc51dd2c0523cfe4b42dc8e781f41
5021 F20110217_AACNVB arguea_d_Page_104.txt
4edb7d2cb655c5733b5f5f87d61ef6e5
bef78aa8f7b26e3e9b68c8fa4f4c5b7f9ba798d5
591804 F20110217_AACOYD arguea_d_Page_119.jp2
ebf8a5c95263aa4527de468c2e8239c7
546ada54c4e9056457a0f80448b8cb393cd2e77c
F20110217_AACNUN arguea_d_Page_008.tif
ec76d89bb6f833d4d968382c884ae586
9c140194681ea76ef68fe92a1f96ea3ca60f552d
977960 F20110217_AACOXP arguea_d_Page_105.jp2
74624c10c7bd3b32a42d58f71eb35366
8b045440abbabcd5ea27c009d2fb30af758afac8
1923 F20110217_AACNVC arguea_d_Page_018.txt
8213f5a72830665911eb3db27a684eee
a3b475f6aac809b9e798118a4d9c410e6bb22b65
509517 F20110217_AACOYE arguea_d_Page_120.jp2
e53806ccac57c2eb88b0686d9a92970c
b1ff4b174fb1be34d9d97159aa5227be0471ff0b
1051954 F20110217_AACNUO arguea_d_Page_079.jp2
98035e9482defdf55bbf1d49121a525f
e2c473d755bf392639a21d36c88c0563bf24d03c
750359 F20110217_AACOXQ arguea_d_Page_106.jp2
345d136b302645210f99d10d29d027ac
7334a7586eec70d4f2d66ad05beea641403bdbb1
6528 F20110217_AACNVD arguea_d_Page_043thm.jpg
2616d5351d7581bdfcd28d61d6e8b097
e0642e22f93382ec3d3a6b87621d5a21c950a1ee
567693 F20110217_AACOYF arguea_d_Page_121.jp2
a5e60430af30c2811c93a3e03e02ea8a
bc610e11b6b69aa376601267d863f580c8c14072
72733 F20110217_AACNUP arguea_d_Page_056.jpg
cfff2d716ea7783bacd52948f63334c6
c98c4dffb5ce090182b03ceb6e8febbfd2bf558c
479612 F20110217_AACOXR arguea_d_Page_107.jp2
4d137b99c7717f76d51f5d6fac3537d7
078575a9f193d6a1d7a742de03f4345ee015a3ce
F20110217_AACOAJ arguea_d_Page_001.tif
1893dc8bb9f2d6a59184fbc7ce0f931f
75f554e89ffa21a6b9d0f1e743e0109e3cb4a9b7
5992 F20110217_AACNVE arguea_d_Page_088thm.jpg
c059d4752701308b2c97419ae992fe08
49a2daa7e8a5aaa8e07054ff893e6b35cd6a988f
820513 F20110217_AACOYG arguea_d_Page_122.jp2
a019e146102bab70299eb4ef96decf88
40a6a012327d57c9a1e3e6e381efb87c36199b5d
47469 F20110217_AACNUQ arguea_d_Page_010.jpg
0e5ae20c475698e5c50774efd7826b34
be32c6506a9c2921bd6941dda40293c6d7cafd51
825229 F20110217_AACOXS arguea_d_Page_108.jp2
a4d7a7635ca83d065e0ac54432d61af8
8d21c34001f8bb54230c1623e115ec9b4c494158
F20110217_AACOAK arguea_d_Page_002.tif
1a0a5a59bb60b4ed9503edb1830c9fe3
1307e350a248c5c9885acd7cb2b5396624b1122d
48848 F20110217_AACNVF arguea_d_Page_012.pro
b868d1d092e994c9f5f26bdedfb27b96
840420138791666eee01fb9c1c3df8215993a5c4
185207 F20110217_AACOYH arguea_d_Page_123.jp2
07832172258674c0aaca0f4adc65de67
5cfedc8a3c4460ad104461819ac8f588ebacd854
1095 F20110217_AACNUR arguea_d_Page_006.txt
bfbc14f207e1a4db9b8482d95987b9f2
99e786241c8c6ebfdc58d3082ae865bcabd952f4
627874 F20110217_AACOXT arguea_d_Page_109.jp2
3f7df8526c3a97f16bf93a5c9dee352b
ea675fbc8c2243631a4b8cafc310be38bb3c5c4c
F20110217_AACOBA arguea_d_Page_020.tif
7c1c39f156c1a07373ba5d4939be2d1f
aa6184f489e6e1054ee67a1c72c6f23da8e2bd80
F20110217_AACOAL arguea_d_Page_003.tif
a6be0638d0d13b4993481b52074afc45
79dc9099865f804d9e64cdae91e08c06d3c387aa
7090 F20110217_AACNVG arguea_d_Page_106thm.jpg
6d8b5d787111f14e103d77e6610c5189
9972c17e98ebd4226e23e3148fc12484eb352f8f
1051951 F20110217_AACOYI arguea_d_Page_124.jp2
9da6e99034585309051f5908bbe53b28
c842a45972388cb3efcaf944328faeeec166d72b
34434 F20110217_AACNUS arguea_d_Page_037.QC.jpg
ef40b72d87316778b853762091c6ba5d
ab2e7ec39c915bc816e420f092a782ced2b1435f
949551 F20110217_AACOXU arguea_d_Page_110.jp2
7dd4c242c5ca35e1960b97046dbfbfbd
27c094c79dd0e22ff5c361c2865a3b89dca66f84
F20110217_AACOBB arguea_d_Page_021.tif
6e9e89dc1370042fcbc4f41afbc69f15
6cfe2f865c6ccb3b25ae6eed8201dac50fac67c5
F20110217_AACOAM arguea_d_Page_004.tif
9b85996c67698c06a1be1a580d50840b
56c66600cf085b55f538d39826e22a17dcd12426
45152 F20110217_AACNVH arguea_d_Page_089.jpg
80c3c2d33853715aae59441b35044207
2f7a65001f563577c1b46bb1e7146ce308be8352
F20110217_AACOYJ arguea_d_Page_125.jp2
e47cccddef38ba428c1cbea8d2f426b7
8090e6641a5bab295ac700a685d14ca96768c5bd
18790 F20110217_AACNUT arguea_d_Page_004.QC.jpg
adc2e0fd797c865c6f8ebf47535484c4
964341b410324036cb6ebb7260464cc162248c22
885905 F20110217_AACOXV arguea_d_Page_111.jp2
a4843f3550793ffaf82d3c7dc8d69eb8
cd71f92a602bb5de29cc5451370701dd12229529
F20110217_AACOAN arguea_d_Page_005.tif
5e62fdeca2ea2e04a65983fe81181093
9bb0376f0a2f890387941eb51fae517cc5a5fd8c
24131 F20110217_AACNVI arguea_d_Page_055.QC.jpg
c4bf35c97169d81869cad6a060c79154
b2ca5cd2f1086bc47a3a32e4fa185923261ca9e8
771889 F20110217_AACOYK arguea_d_Page_126.jp2
192cfa95ccaa6d0a5884a230d3cb8eae
a2d5898cc40a9eedc10a95b566d2d2a14f7e1e0a
8225 F20110217_AACNUU arguea_d_Page_030thm.jpg
a915954b9fd519bd077b2516bd35ee6b
51f197d22ebafd5a0af0a759eceafb7efebca864
F20110217_AACOXW arguea_d_Page_112.jp2
b252e4484f5794a5e50542244382c9ca
01f63e39a57cf33f3df481170636a13b7afa9975
F20110217_AACOBC arguea_d_Page_022.tif
fe65b5db41c332527c74ef2c92f96517
724c7991d84a6b8d5ec7bbbdc1ea6dae489a26ab
F20110217_AACOAO arguea_d_Page_007.tif
6c63bff9e1f3c9c32fe3e75f4c301091
4e4974fc0dd78859cbbd195bb354aeddcd5f7733
29538 F20110217_AACNVJ arguea_d_Page_104.QC.jpg
c98a43f7481af8eb0ee38dd0858ab9a4
4c914419d01b4e772cd6890e210b1bb279c99299
857022 F20110217_AACOYL arguea_d_Page_127.jp2
8fd4b4018ef3206f0d5159c268b86c94
da8a0ecae7790be7afda0ab8ec71e9581f8e2ffa
672869 F20110217_AACNUV arguea_d_Page_050.jp2
b94c807605fbba2c23339174ce82ee03
9bc2e6cd31bb4dfc7fa2b42995fd0a4c49a7f4f4
882652 F20110217_AACOXX arguea_d_Page_113.jp2
1975ceaec8096530041e9c1638ad4664
f20bf6785eb8fea0112750b11a3edf51eb506e4e
F20110217_AACOBD arguea_d_Page_023.tif
c29809aaa438b09ca418fd6b62d83f39
7bf114cab5c66cb00cc747c6b7cd68dece11f5ee
F20110217_AACOAP arguea_d_Page_009.tif
841049476601d2f17d73b8a2a4796adb
5761040f9488ecd75a6cf75ff209514da14e6586
7675 F20110217_AACOZA arguea_d_Page_016thm.jpg
c2e43ae1c17944c684cd42a6debd069e
be0d5da2eb0628463c200b8d601f5db02cd49f87
38652 F20110217_AACNVK arguea_d_Page_112.QC.jpg
b2291b6157ce0a1e47d974388e76c0c5
675c1379a1148ce582e76c1943173ed17cc2d78d
2246 F20110217_AACOYM arguea_d_Page_001thm.jpg
e53b4ee05cc1c30220048cea6a2c0009
3744cea61b9accbc8b70912189c3b1dcb1ca0154
F20110217_AACOBE arguea_d_Page_024.tif
df89d43a0a552853d86706b101b08c5a
c7bbcfd445e39e761cfabb11714ba43fec51ad4d
F20110217_AACOAQ arguea_d_Page_010.tif
4119819bcf12aa23d03010febd3790e5
6cd5a407f927a94f6cbaa68a3ea373e423c67e1a
7805 F20110217_AACOZB arguea_d_Page_017thm.jpg
de42e3950c8375178bc44312a92dc77b
8831f35e505500a769dde70e933023b7da2f8908
F20110217_AACNVL arguea_d_Page_043.tif
77f253858415b0ab8430d5f781ce597f
c1e7a9222ea2c457bb93b0408ff07456ee48b748
563 F20110217_AACOYN arguea_d_Page_002thm.jpg
b5456c09ea16d24d1c23d13420249dc5
4b67d9b1dbb69be9900aea68af9327de2bfc5c4a
8425398 F20110217_AACNUW arguea_d_Page_118.tif
821edf16c5a1f5b481befd4b325b8e37
fadc65d9e48e2e19aa2d01cee2faba1cea5a7c93
210609 F20110217_AACOXY arguea_d_Page_114.jp2
07f6373d81cbe16e165bb1580392d1ba
5a78bb1fa9dd1d598e10ca677749c04fc670c825
F20110217_AACOBF arguea_d_Page_026.tif
097a67415618f2a3a00e0c96f5725b87
767e7e9a76e37060b180f0eeda64b4130b3ac9f6
F20110217_AACOAR arguea_d_Page_011.tif
f582577f98e98f5bffcbc05245c6ebcd
4f378f16f3727c09020b5ab18c6e93deadfecdc9
7939 F20110217_AACOZC arguea_d_Page_018thm.jpg
dc5ea5c3a9b80d1a23bcd587db4c81ce
9c9555e22c35548a546d130b3499cc2135d105bc
813629 F20110217_AACNVM arguea_d_Page_068.jp2
5e0a76b28d0f5b6742f798f3d8c2fe20
8ecc10272f64f8dfd2904daaca7646eac22e0b40
F20110217_AACOYO arguea_d_Page_003thm.jpg
0f49c5b4ab1d17bc848af18259eb7030
9247dbab2b66af382d5d84e89e9aea85e3a5a9a4
101872 F20110217_AACNUX arguea_d_Page_024.jpg
ea65c9288757e95da56e311dcd4d98de
c720cba764b9791983dd736d41bcdb22dba79148
944574 F20110217_AACOXZ arguea_d_Page_115.jp2
8469eb9f3e2bd64cb25edc6c981e048b
8ed7b79beac7353a8a6f1712ca57d41801505a7c
F20110217_AACOBG arguea_d_Page_027.tif
885fe3982c30deddbc9f49180b2006a8
ab4464e85b966cf3a6972605b8808e4b98ef4423
8230 F20110217_AACNWA arguea_d_Page_081thm.jpg
35ed71720c6822b552d2f91e1ab9ff90
406d5ca3d27b530c8a857a53e939c88a3a7cff94
F20110217_AACOAS arguea_d_Page_012.tif
2f1428e0d573da5e28d8355e51b2d9c9
551381e1d484beeeb26e6b6ff72ad18d504f4b35
7312 F20110217_AACOZD arguea_d_Page_019thm.jpg
44d5e25a3ca6a22be7f6431f63ab8d86
fe625d726f702e7cc27494da22341c844b1bbe22
F20110217_AACNVN arguea_d_Page_083.tif
c410e8e5a1b367fcafc45e18025eb262
be3d195e3ed45ab340c7f634c4e3bab604fb1f7e
4338 F20110217_AACOYP arguea_d_Page_004thm.jpg
64183e62c0abb988ce1a36a35ee98800
e15512b0738dc6cae9564bbbd0c2ed53fb37f891
52795 F20110217_AACNUY arguea_d_Page_073.pro
db9068c518eb7e4c2f3c940c8ae97818
2114f18524cadeb5dc6bbcf5b62019d8b41b5ab3
F20110217_AACOBH arguea_d_Page_028.tif
349a014e8218b97d6ca14b825131a30b
4d6a4f3c8b28d7436d16946afd982fdd2e3b1680
1566 F20110217_AACNWB arguea_d_Page_118.txt
b4078840087c2877e98ef0b39eca2a34
95458bd74a1732db7220f2a291c2ab9f159f6f67
F20110217_AACOAT arguea_d_Page_013.tif
e36f45dbf3c3d39641938b60171fdbac
5370830025c81a5a65cd4527d4538a054f7c37f1
8423 F20110217_AACOZE arguea_d_Page_020thm.jpg
ffb27286aa91a0b205ba4a9c08816d23
c26a261476f0260507a4913d8fd55cd7252a6d58
2472 F20110217_AACOYQ arguea_d_Page_006thm.jpg
1bc9779aed960202f52c86c9807f8f60
54459285c181fe21022257b50b4d3b43676b9107
551 F20110217_AACNVO arguea_d_Page_097.txt
46b9b64798d7d4e6ac94bf3702cebce0
53270ae8e2fb210f5ddf1c8255fea22515c3fef5
20914 F20110217_AACNUZ arguea_d_Page_060.pro
800f9b0bcf89b4c3d1b8711d51dfb7c1
c8fcf3e083b1d7afb23f30d2cc67ede74172f79c
F20110217_AACOBI arguea_d_Page_029.tif
a96d0c42f0de9074ac1ccb7d29042846
3287898e1445819983879e0ea9952f6a9cd9536e
1373 F20110217_AACNWC arguea_d_Page_042.txt
87fe09969648119cc0e1ee7d3233ef86
67b0e3317b23cc8337027178456c2fca7264393b
F20110217_AACOAU arguea_d_Page_014.tif
b6b3ec660ae2875e6035c1c66b0aebfa
35acdbf254373ac225ff3ed940cec4c6b48fe62f
7289 F20110217_AACOZF arguea_d_Page_021thm.jpg
d0ce01f25d0128ed9b5d6c4c8bd23a9a
cf0935892641efc448479a0859c377e7454c3d4c
2358 F20110217_AACOYR arguea_d_Page_007thm.jpg
f17bb9fce2036337f3ae427f0d8539f1
37e8395e435569f497a72c38444333eecfebd605
7793 F20110217_AACNVP arguea_d_Page_066thm.jpg
9f3444973c7e0ac125d86a5bbfef9aa7
477011b7ac59ee2ec6dc0edfd51f120bf731d91a
F20110217_AACOBJ arguea_d_Page_030.tif
3087cad1ab04c9d33d82f22e9dc30b3f
7fa938c7b151cba57ec8c9a28f26ffc4e1a5ec52
21661 F20110217_AACNWD arguea_d_Page_087.QC.jpg
b8c5d0d5aa776daa237f8062470a2580
e3cf5ac89c1909c9e081903aaede317321e633f7
F20110217_AACOAV arguea_d_Page_015.tif
b099455ed5ae07f0b8b4efcff0303144
0104cb5226a74f3f893caddbe5bb901c91681cb6
7061 F20110217_AACOZG arguea_d_Page_022thm.jpg
80ce755235f18d1276288c38baa6f4db
a6753622da8bd2a8268c847b7696dedc8faa11e8
5964 F20110217_AACOYS arguea_d_Page_008thm.jpg
1b4bf45cad339cd9a1c0c286df57d094
fb9a2342981c609f5bad0f3aad6632f7969d3264
F20110217_AACNVQ arguea_d_Page_061.tif
20d3da3f1207a2156bf7fc9921aa92b0
213d24a3c149e41e226c5b720712aedcb9f217e7
F20110217_AACOBK arguea_d_Page_031.tif
f372194883201fdf97fc496f10b5fd88
75d09929a551468e744442bc7ea42fd92c5b4ccf
1114 F20110217_AACNWE arguea_d_Page_070.txt
0aa581fb7d976fdb054eabd0be139f91
3ec288b1fee6cea847fcfd08a2e1d77292be2312
F20110217_AACOAW arguea_d_Page_016.tif
aaa6177f903f15a92cea1c05cb8e1867
1a7a262e726587c3010a16631f583f6a7b1b5ba7
7382 F20110217_AACOZH arguea_d_Page_023thm.jpg
801cca1ebb4bf0baf662f37d308e06dd
3dc6e56ed4a849de74cbf65f8d873671fb4c7b3a
6535 F20110217_AACOYT arguea_d_Page_009thm.jpg
276df8231991e5defd9451b32888a3b0
850d2eabecd2e74730c55cfa109c3fed416abd8f
19804 F20110217_AACNVR arguea_d_Page_122.QC.jpg
b6b06a8d1580e74f5cfe397a5c8108e0
e0391a2eb718791888e95312977d96cf4a34a589
F20110217_AACOCA arguea_d_Page_052.tif
9494ad0036cb8f95bcd4a42aba4de5bb
bac1ef198c8b4dc589826606ff8c278f2dbddfa4
F20110217_AACOBL arguea_d_Page_032.tif
c3cfd0082faa97067db84207fa13f38f
b63439fa85d18abc148e08719e977457c026448f
49047 F20110217_AACNWF arguea_d_Page_039.pro
00c9ddc223c8ae54dc17d0a3f30ef7e2
df45f57883ed4f8cb896eff32fcb9602189c1c5f
F20110217_AACOAX arguea_d_Page_017.tif
262c3bcdfc4d93f61010b7708870fb2c
fbec1effd0b0969a0a434db491886943c20abb71
8419 F20110217_AACOZI arguea_d_Page_024thm.jpg
333502e5f3f864c4e39d784b9f2179f9
20f7934f2932beecc61c4f0ebc5c11adbc2e28a8
3981 F20110217_AACOYU arguea_d_Page_010thm.jpg
1bdfa9fe1a5ad33a4a5305b92644416d
11d8dc600b04acb6a43d7613b953ade730cdeaff
F20110217_AACNVS arguea_d_Page_055.tif
7031e2e192210c9d7f06794146a1d7b7
42c54b6f745c1ace7048402b8ceae9e7e31778ea
F20110217_AACOCB arguea_d_Page_053.tif
15ca168dd50c59146b91650230e589f0
ee969d00d822c4288628dfe3a8caf992ffa442c8
F20110217_AACOBM arguea_d_Page_033.tif
1922a6a91bec386b4df77ee87c6b51e8
043e30a901583fd19fc329e210bea2e82fdb4a8e
105700 F20110217_AACNWG arguea_d_Page_030.jpg
f3aef5657533937758b53389ad201103
6141370ea333e82825b16515d1a5051993eb4d91
F20110217_AACOAY arguea_d_Page_018.tif
f6c9ef6628a82497cdcacce540f20e4d
a06ebe18a0547cfca9fc294e5a5becc40ea9b936
8283 F20110217_AACOZJ arguea_d_Page_025thm.jpg
ec545933a6c407bd2e7e258c9b793e8c
61f3d871840712534b78dea1e083f42fbfb8111e
7073 F20110217_AACOYV arguea_d_Page_011thm.jpg
bea8a38d6525f4416f32d757680ee8bc
ff7872f113d4a00757bf9f48910f5108202094f9
433109 F20110217_AACNVT arguea_d_Page_033.jp2
40242905a0ac29257666231781829a34
f5c36c6375eaef835c45f14f88f63ff9e9665f83
F20110217_AACOCC arguea_d_Page_056.tif
e70dfd613e0e312a57dad0a23f347202
03a804a11b042a864863a1b61c20aab5d533ec53
F20110217_AACOBN arguea_d_Page_034.tif
e06a1fa1aa1ef33c4c9df5dd8b5d8dcf
b7514812b99691c98fc8b8478d062ef484363162
1051928 F20110217_AACNWH arguea_d_Page_037.jp2
4d35d773e5fb63a43f6d0fc213d21c84
86892e45e440832924a10a3d17936c9c418304df
F20110217_AACOAZ arguea_d_Page_019.tif
c8e6cac93eafea2cc4baed34bdc9b663
5c27efb18863d7daefc93e6bf07d08599ec13f28
7979 F20110217_AACOZK arguea_d_Page_026thm.jpg
377e07a459463bed081bc6bd2ae6724a
8f062682ed6af7539855d6b07df7e53dd63032ef
7930 F20110217_AACOYW arguea_d_Page_012thm.jpg
fcde3c66ef53a8dbc0be85d501dafae3
dc5452eb42a75cd244ef551fe932e05c12622591
896 F20110217_AACNVU arguea_d_Page_096.txt
d88010df5f8b6c747b43a7cb54099484
69bab614128497f59628809db778356c40779275
F20110217_AACOBO arguea_d_Page_035.tif
60417730772d55786e8b3fdf46e012cd
bb1832ad4cae3c150d63b56119a1b3ff535634e5
18160 F20110217_AACNWI arguea_d_Page_061.QC.jpg
6b7fd02450322d403cbe493a25677303
90e3552f58be84acb6bc156e10db801c281fdcc4
8351 F20110217_AACOZL arguea_d_Page_027thm.jpg
5b6c0fe5e87a0bb9b355e3f03ce07c70
f7fce120a0c4281c24985c6e18d4625923770dc3
7807 F20110217_AACOYX arguea_d_Page_013thm.jpg
0a87f6a7ba81903847190a4adc68cc79
6e9577a20d2653316e645a4caf1ec28202367e41
F20110217_AACOCD arguea_d_Page_057.tif
c1b5d5b824409bd57d07db68fa1a8ad7
94cfff3d54a60f6549e50c77e0269f48a33b9775
F20110217_AACOBP arguea_d_Page_037.tif
fc9af7cde88c75bb4eefdfb1a6198d05
2d0715bb179837c5d22ba03a671037d9f14013aa
8767 F20110217_AACNWJ arguea_d_Page_125thm.jpg
9b1cd39d0f484598df8912c184b7ef03
395d8fa0a6345f2660eecb28d5e9415552789a01
88089 F20110217_AACNVV arguea_d_Page_051.jpg
b5eee94ba7c56dbc4baa2e66cda090fa
0015093612daf2243b49515fe3ec7786f014376f
8216 F20110217_AACOZM arguea_d_Page_028thm.jpg
eac00a46b474f2decca0ceeef7d3f1ff
d5c1320ec96b910273f6b7bb805b59535967c44d
4380 F20110217_AACOYY arguea_d_Page_014thm.jpg
9940798a26680bb40816b4326284c23f
cff5a5c7c86ba2b3bca35d677435fab42993cdd9
F20110217_AACOCE arguea_d_Page_058.tif
57754584ee05253493e243b26d4d77e1
9c183449f8ea0b1bb49d8ed090782c52fe8db34e
F20110217_AACOBQ arguea_d_Page_038.tif
29ecb4800df844da368c69298438bc7b
45a11516b2918895371763d7f99c8b231d2867d4
1178 F20110217_AACNWK arguea_d_Page_120.txt
81ffb8871ef9b283adb84627822fbbc0
d6bbc8ebe158cf5a2ca643e300a238739b03c989
31023 F20110217_AACNVW arguea_d_Page_105.QC.jpg
da9fcf683275b477b138f20890c522a1
215918cc19fbf876ba459ee5ed8921a7b547de5f
7826 F20110217_AACOZN arguea_d_Page_029thm.jpg
4a13fe95ce4a384df4c8c29c817a1c0a
3cf55b8f52132b90c2035558b3f89baaf620ee20
F20110217_AACOCF arguea_d_Page_059.tif
1f4e86ba5771da96aedbef086841e8f6
be2a00d671e36470f4441a8e4f67581e90033ce4
F20110217_AACOBR arguea_d_Page_039.tif
c0723ccec6999e115ea53f1454f225d4
45607d5a9ccdd379d5ff2e15fbf9db39b560a611
45991 F20110217_AACNWL arguea_d_Page_034.pro
57c1e54d08a9e9978cb1f6dce1f5d616
b1b7e2e7014b57c7e3925761fcb9cd945c2d73c2
8069 F20110217_AACOZO arguea_d_Page_031thm.jpg
7aa77cc6f314b3b18a7144e3180c86c8
d28192af31af94a79a52177b8ab1db3b822a96c9
7273 F20110217_AACOYZ arguea_d_Page_015thm.jpg
eacc9ca382c35c1e0f545ecdc00d8ee9
00cc4fa9e9682bc6bac3bd1bf3d4cf9769ac376a
F20110217_AACOCG arguea_d_Page_062.tif
bc7f4e2acfdb94b90c1c6067df5f549a
b9934d6dca15845bb368b74aa58d6d9414c21326
1051978 F20110217_AACNXA arguea_d_Page_036.jp2
debf43630902b341b532a69ede9b360b
b6cc43225093614656e36a0b03efb0e7e5fb463b
F20110217_AACOBS arguea_d_Page_042.tif
91256dfb1f4ad1c8cc3028857768c6a0
243362cd8f21baa7ba2a8c3c1401b1404e2d683b
1043596 F20110217_AACNWM arguea_d_Page_083.jp2
1a5334f3b0b8d0e7c0c5e03898f14441
5c15900ae5dcefe2d859a8ef7b6a9d407cf2d02a
2349 F20110217_AACNVX arguea_d_Page_114thm.jpg
30a27391abd1b2cd98c8190d6bc70676
61272a2bedd1ca58ea8566743be4f667c9f16bba
6906 F20110217_AACOZP arguea_d_Page_032thm.jpg
f5641c057d79e58ac7e7b9700f04f1a9
ce0143a595b2f7dba82f815c80bf7f64b05787ae
F20110217_AACOCH arguea_d_Page_064.tif
f3cf7b6d1d573dbdf4b5c9fecb0f2091
b97fb449f83759a899d29ed940d54592ad92fdfd
F20110217_AACNXB arguea_d_Page_036.tif
9def1ea8b3e8a5b630580f44a50fc475
215e0aa0ae34f562928e1cb7ec2aa02ed2a5b057
F20110217_AACOBT arguea_d_Page_045.tif
27d8af76a3db3083024ec7e5d6d534ba
3966d1bba02602d64e8f42858e8665efc80e9ab9
F20110217_AACNWN arguea_d_Page_102.tif
3826b30cf533847c9143dc70a145638c
020a442d49b9aa676203b768b0ef448c634e27e3
27948 F20110217_AACNVY arguea_d_Page_022.QC.jpg
5ad1cdacdc6292df28db7f7ab90173f9
7093f97aa0aebb56da2bcb6ba228cdc8872b6245
3475 F20110217_AACOZQ arguea_d_Page_033thm.jpg
c90168fee3e98e030d9c2406d1daeab7
38b26d0ae3ffc76bed13d3b461f084937e068b5c
F20110217_AACOCI arguea_d_Page_065.tif
62f4e2951ee535cea80c096453dade67
7f06c110748a6312e19f143d799fb20de28d3f98
11512 F20110217_AACNXC arguea_d_Page_118.QC.jpg
8541bd9877942a489782043ea37a8b90
a710109dc63749567ba8ffd8808072b361f61747
F20110217_AACOBU arguea_d_Page_046.tif
855718005c6b87209bfd7b5cdc426ea9
3d672b710a5e6a204d2f70fc089ab988682550ea
25575 F20110217_AACNWO arguea_d_Page_040.QC.jpg
a293dd5c25a9730a3c56dbb37cdc8358
86e8e1e1cdaa24ffbe22d968e37e8d2264aeb9df
84969 F20110217_AACNVZ arguea_d_Page_021.jpg
9d3459be1af3949009b9402a043f4eb4
5e636eba9e46c4fd475d09130d1499eb752339ac
7316 F20110217_AACOZR arguea_d_Page_034thm.jpg
825bf4f6357f0825a28db05a816b5c27
42e2be356d0e6ff87a90b4e269cb885d24400f75
F20110217_AACOCJ arguea_d_Page_066.tif
fa69db8f8e93f26686e59ff3f4b0bd54
20f5770303b90d643bfb31b202033a776d1aa66d
31698 F20110217_AACNXD arguea_d_Page_018.QC.jpg
e1a0c69805eaa76ab08b81641973f874
231c913758f59c38e984e1d8d435a3c984c61118
F20110217_AACOBV arguea_d_Page_047.tif
93d5eefb24628ece50d4dc2e5475fa08
bcefcb5bf01f9fefc71ac4b369a3dcc72e2d5d10
1233 F20110217_AACNWP arguea_d_Page_069.txt
96135a75684dd203b46a91e2e62dc22e
6894c8b97d343d2672cc1cd557d748fc39c6e561
7674 F20110217_AACOZS arguea_d_Page_035thm.jpg
7f7520e7651e3077dc5acbe5ec400fe3
327cdb66e7fba797506b2e8cfdf4e359e2b86e9f
F20110217_AACOCK arguea_d_Page_067.tif
374ccb0e719bfe88b3234ed7bd0dbe28
3df9d4cf0e24c8bfcca4e6a765d51767477eaeab
5461 F20110217_AACNXE arguea_d_Page_005thm.jpg
513aa159044e88dcf8b9448527b8309f
d14c3677fdd46241f6590cab2fcd5730ea9ec398
F20110217_AACOBW arguea_d_Page_048.tif
1d62800bf62c55c8be9f4a877bfa3c2a
4dedec7c022592c5b4a64284113495b326b6a263
31260 F20110217_AACNWQ arguea_d_Page_029.QC.jpg
000d7a7ecb006fe2103f87a2b5fab063
56349b606cd3524379447d82d01d888a917b3197
7842 F20110217_AACOZT arguea_d_Page_036thm.jpg
34bf68989747078c3bd27aeaabbde4bb
3f38ba2f1e5bc59132c5891517e8ffce750d099c
F20110217_AACOCL arguea_d_Page_068.tif
925bc267400b4d454d4e8a7526e488dc
2e498117d8688caae01c1c02a8422842dc2005c9
7975 F20110217_AACNXF arguea_d_Page_052thm.jpg
465c6c1fe5baf12283ff9ce75a4dd647
1183185ab450c94c4542c5846630094c7a9cdcbb
F20110217_AACOBX arguea_d_Page_049.tif
dd348cb5c0b70a6c14d16c9543b7addd
d4bfb955eda7fd88976c28624d17f1b913682b44
949125 F20110217_AACNWR arguea_d_Page_011.jp2
c22442896b26886279f0e334f67c1802
00e38a3cae779ea7d3437374efe4651898196594
F20110217_AACODA arguea_d_Page_084.tif
784d37bcad76866b5574098640ae40d0
c07de87b08a2a8aa81f4e7c8d47628dcd207ed7d
8590 F20110217_AACOZU arguea_d_Page_037thm.jpg
74522fd313e53287287eff3c72409d0b
b25cce533da9ad50cc26870cb711787e0a6597fe
F20110217_AACOCM arguea_d_Page_069.tif
35c26b20ccba7a0774214c45f4105cca
56be9e324556c368a470c8aed8370db6430563e0
F20110217_AACNXG arguea_d_Page_054.tif
78727f91174aa8e46302d5274c43f488
e4c8aefcd9f6bdaa2a8c91d3bae52f06875eef4e
F20110217_AACOBY arguea_d_Page_050.tif
3aea9a58a3d21ac6843a545311790f2f
674ffa82612694a6711e42476f5d347b795256a1
1939 F20110217_AACNWS arguea_d_Page_039.txt
7907d29cfda5ba3ece146fadf61b6997
035951d46aeda51a75c649cbe024e63a7004bfc8
F20110217_AACODB arguea_d_Page_086.tif
9b63079bf6bc9898d5f6cacf73c220bc
5c0567a160f01edb1e0d3f253d65a65d04df7a5c
8144 F20110217_AACOZV arguea_d_Page_038thm.jpg
631c8dd5e3c7cb3f166d2a5d8a7179bd
f85410175109a4edfa8e37c1f5bd25700e2dd33a
F20110217_AACOCN arguea_d_Page_070.tif
b1d6358713e665dd65514bf6fe2425d4
135b52fe7ee5b35a9467b832ffde40130518d008
50209 F20110217_AACNXH arguea_d_Page_025.pro
5dc6675711cbc4caabd909c874272a04
86aa8ca563abf6ef3caa5c77987e4b73b6041a1d
F20110217_AACOBZ arguea_d_Page_051.tif
3593076a3d268203b639f69a554c8257
d557433eee6e8473ac1dd3dcf9cf577862277bf6
1051977 F20110217_AACNWT arguea_d_Page_005.jp2
c64445345cf38b22f8314dad792ccadf
5d244249f9bc9878211910f3d7ed68eb26af6629
F20110217_AACODC arguea_d_Page_087.tif
221da9bc2356338645347d8a1ef503cc
de8f4a24d74eb15280cb692c00847c0fc5297447
8077 F20110217_AACOZW arguea_d_Page_039thm.jpg
37cf14174bfc8bf06ce80205cf4385f3
b7b2a75400b2c2b2a04ea7e42b38876b13808122
F20110217_AACOCO arguea_d_Page_071.tif
50d1c26ff3c29c96c4a82d5afc8c84f2
42cba3a3b1dce6184a674a82d49b8a6b89136ac3
24110 F20110217_AACNXI arguea_d_Page_062.QC.jpg
762f5103e6dbf8c004fbd970e4b3ce28
a3609172d438f93c3d137ea8252ea16d49c89a10
132610 F20110217_AACNWU arguea_d_Page_112.pro
ca4c846621a11d296a44cfad498a7e98
2167afdc1a00cea94dd9f797b55e355fff753393
F20110217_AACODD arguea_d_Page_088.tif
c6ace6ef8d3e073626b53962c19f863d
b9eb593712cd99a95f17a8784ec110ed801f5c07
6940 F20110217_AACOZX arguea_d_Page_040thm.jpg
d35916c8037fc4ddae2ad75381eb5012
7eeeef8cb0a75bc7677eb29af50991e1e91bfcf7
F20110217_AACOCP arguea_d_Page_072.tif
bc1be106b1474a393002267d4f30010d
684d51f91ddb225a9454d47e8fa5d9b0b492843a
1876 F20110217_AACNXJ arguea_d_Page_066.txt
c6689f8b5564b1cb5f6d96c5a6713edc
2800157b7930b8295498219c1858e0d02f51f523
1798 F20110217_AACNWV arguea_d_Page_051.txt
35fc96266553a3aa1e4c0242fb186094
87eacbf630c4fee0a6a04bb1e4ced61f48f6e5e5
6033 F20110217_AACOZY arguea_d_Page_041thm.jpg
aefa7dfd24f4e167e0ad2bd3e9ae6d22
47f73dabd26cd10eb6ad5aff513c9a8be94eb016
F20110217_AACOCQ arguea_d_Page_073.tif
6737c79ba83ffe113e4b5ded77aa2e35
d5638ebb089b076ba60f0f85b4666ae674adec57
257293 F20110217_AACNXK arguea_d_Page_091.jp2
d7e4fd3d991cfea74590afd3cfca8617
476428c1ab8a9b2478411849d75ccf56bf63f4b9
1051953 F20110217_AACNWW arguea_d_Page_081.jp2
d7b9d69a43e5813e214fea736caa3af7
a0ac96fc6040e1264684d8036d06ac7d78efd0f6
F20110217_AACODE arguea_d_Page_089.tif
8cfbb165905402aaf3c69727fc16d14f
cc60c4f91d7d48268a442f1921f82242b1c02e39
5819 F20110217_AACOZZ arguea_d_Page_042thm.jpg
3e528147ad3494efae40fa833d81fbcd
e5b534fbe044394e8ddec2edfc32e9b7c692fe86
F20110217_AACOCR arguea_d_Page_074.tif
59cc3096e4e49699f3b2a0d75215d5ca
d633667e1c87502d5e7a1aa47e71ec775deab39d
7066 F20110217_AACNXL arguea_d_Page_059thm.jpg
215fa985ec3a267b714d7c3249ebc664
a042918843ffcb15ed6426f77cf2d8b3b1741c6f
525 F20110217_AACNWX arguea_d_Page_122.txt
23ef4fd1716d62be9903dd49e1f257e5
89c9cf7afa82abeb8eec92862cf2e36d3503419f
F20110217_AACODF arguea_d_Page_090.tif
cc729b087ae267e7e9438afa21f4d3f0
a070e2373cb4035b2943a7f5d206a2e6ec949609
31800 F20110217_AACNXM arguea_d_Page_080.QC.jpg
c38136e772203d1f0d909f192cd89138
e511cc7c3c44eb1fcc555efb4d2ed5663ae428fc
F20110217_AACODG arguea_d_Page_091.tif
856101100e4b315aecab875e048199f5
595c8d8cb0a312fba3f98f2ac3cea19bf6f5bb6d
64003 F20110217_AACNYA arguea_d_Page_125.pro
de7ca73a6851a9050cdcb5d2789c0e06
d4fca714975246088ce24502b274af77477bd8ff
F20110217_AACOCS arguea_d_Page_075.tif
267370c375ebf76d35629b16cf9ba5e9
3ebb4a1be2572b7ccd00447fcc81b8c5e856e9c2
62992 F20110217_AACNXN arguea_d_Page_122.jpg
f29dfdde4c14fad1230dee14ec037fc6
f562d4864bb126cf4d29bbc61d19b198534def61
536208 F20110217_AACNWY arguea_d_Page_054.jp2
ca9528e0de58bcf21eec6c15f528727a
2a3147f9a1581b0e4a58678ee194e684ddae81ce
F20110217_AACODH arguea_d_Page_092.tif
2740d8ddee1dc81ce7672837f43e1319
e5435c6aacb7e430d1a6209add4efccf74ca0146
398779 F20110217_AACNYB arguea_d_Page_096.jp2
58f3787f70a0b5003a39d79426eba3ad
0d94d13dc38203018a89699a40d90319170004da
F20110217_AACOCT arguea_d_Page_076.tif
4731e35e4c23264e3ddf3549a85842cc
99ee4d4bab46526f5171971ffd9b2a26b21688fb
5555 F20110217_AACNXO arguea_d_Page_063thm.jpg
8b90fe1deedc3f5cf53b9133bd24b25e
2ff50579abbfaee0428810daf6709ad1c1ec7abb
23480 F20110217_AACNWZ arguea_d_Page_041.QC.jpg
2485759fe0aa00965f4ea7c21966c17c
c0e2ce153696c1f61089af55295e851cd01495d9
F20110217_AACODI arguea_d_Page_093.tif
eab191365f5155925d9edf01416198ff
009b117b4913a8f828d18636ab31e467d8562443
32477 F20110217_AACNYC arguea_d_Page_048.QC.jpg
ad638ef77ec2021f9f560d93eba72be9
1cb60f0a4ac8ea01ea0ce398f40da014a24c4272
F20110217_AACOCU arguea_d_Page_077.tif
6951e29d313042371930939ced3ec16c
76294b3c3e051fed908fb678d6de9e4f81379133
F20110217_AACNXP arguea_d_Page_085.tif
ca171989dd7d60fb55d9783fd202a918
6c06cc2c2303dd46a8fbbe42eab01bf34b2ce78b
F20110217_AACODJ arguea_d_Page_094.tif
b53bed70f5a2350af8aec1c671b616b6
a6a00b000e0c6c673339433285ce3c7a41324c33
8587 F20110217_AACNYD arguea_d_Page_073thm.jpg
94260ad6e22cd38f1e4adfa36bd3c39e
9407692a53ab2be915c7102f2226d2b802fd82af
F20110217_AACOCV arguea_d_Page_078.tif
16e1fd8835c383c77f2e2553c62dc0ca
50356f618a6f2c440db3f65d8757e7b6407bf254
549164 F20110217_AACNXQ arguea_d_Page_053.jp2
7d81326953bf79efd7f596c3319c17c6
0570fc0ccebef21fadc755b3008701e466438172
F20110217_AACODK arguea_d_Page_095.tif
02bd7019ebeef13b0f5ade1b703557af
0a3606b7f680eeb78da5d1c43d5fc52d05e8a087
31781 F20110217_AACNYE arguea_d_Page_047.QC.jpg
1b89fc54ffac73c3f5b9c876a5dc1fa7
8cf8795a4fbbb44bb844db3c2be43f935d44d06c
F20110217_AACOCW arguea_d_Page_079.tif
12a78f6cb8d2b4273f27a80443ac1bfc
0fb0c536c3eb40941142d6f94de82758aabf617c
95078 F20110217_AACNXR arguea_d_Page_017.jpg
742c9249eb7c05f86202fb28be2101ad
ade45c07475f66fe50d422e5d53eea38fc3cd8d5
F20110217_AACOEA arguea_d_Page_114.tif
67fb7c7da6bbec5786fea3b9f87f1e4f
231e314df36b69267768d24fae00ce03927b5842
F20110217_AACODL arguea_d_Page_096.tif
3af881734df4c295dd958009ce24acfc
6d66d3ba57567c621acc1bd54d909b0f1cbe9529
6683 F20110217_AACNYF arguea_d_Page_068thm.jpg
97ab89b63ad318e60a0ef186036da750
87cd0c7b28f388b79e3129c1b8ec0c7f5fca84fd
F20110217_AACOCX arguea_d_Page_080.tif
35994cf24fd834b430199b4bf923742f
1d2312e70c8db3e19f517308baf3694e3b85dd12
30598 F20110217_AACNXS arguea_d_Page_016.QC.jpg
cc773757392fba40a1c15c3b80cac34c
2c154386c8a4c6f602dbdb0b0c7c4cb6e9922013
F20110217_AACOEB arguea_d_Page_115.tif
1a60534780ae7b64e31aff449eefe41c
ff95fb7cbe120b8b3f8babb6792aff2091316575
F20110217_AACODM arguea_d_Page_097.tif
7cb423bd9f0810be9336153d2d4504b0
cdeedb00edc50ba743d782a1b4f35475b13147ae
F20110217_AACNYG arguea_d_Page_126.tif
96000b19756a88ca56012318af7304e4
cd85b95fbf8b402cdb2e32782d7c70b94dc16037
F20110217_AACOCY arguea_d_Page_081.tif
5ba3d43d8a8a4ce3e37d6a0e1f389da3
501a839fc2730080458a2186844e518cf2329d15
32001 F20110217_AACNXT arguea_d_Page_052.QC.jpg
5751f2ca8130065d8a718867a806ce41
3a6ca3ad5b96fcb33504aaed54a240f308b6d563
F20110217_AACOEC arguea_d_Page_116.tif
fac746d68ede01935578d7c9ee75ee66
6042ba063de2e81ae5946d51c4d16dba34d1960c
F20110217_AACODN arguea_d_Page_098.tif
625df33c7644324eb02675ad69480e28
3ea630870ce7d43e3be1f0cce1a2c705c53aef9e
1279 F20110217_AACNYH arguea_d_Page_071.txt
69fdc78fbbd2338415594430469c9f71
f724a27fd94d85a6ce512476a355ecf38b933a1a
F20110217_AACOCZ arguea_d_Page_082.tif
348ba72b9f6e4a6bab349aa02972f56d
dc25d995e197dcfd2f1ab30ce2d6a992fc4e3d30
1560 F20110217_AACNXU arguea_d_Page_127.txt
a58120753bd6b6cb0973e37a1bb24b55
05edfa859e58c2206bcedd5a7faddf70813c81eb
F20110217_AACOED arguea_d_Page_119.tif
4c6cab3cb02694462785b6d59ce24639
ca2b4d08d1b50778fa1b2a3faa7984c1c3750b72
F20110217_AACODO arguea_d_Page_099.tif
c2c648f4f199fafcb73b5cd3f0e8bb5b
2844e67e0773a297b2aeae89e051e51b085eb778
945177 F20110217_AACNYI arguea_d_Page_078.jp2
0d0cbe942eeca6d2c6d16af9f978e719
49592b80e6e5c1b4af1f8d8e7aa866ab157d2774
891 F20110217_AACNXV arguea_d_Page_010.txt
78a45ab4a39a34cb1e88ded7389deeee
b770b5ed545d077c6088614263dd726f25cccf84
F20110217_AACOEE arguea_d_Page_121.tif
31daab806a41f02c3ede27a0c4ea1ca6
344fff50b66eabda529dce27805750850360960d
F20110217_AACODP arguea_d_Page_101.tif
4cabb0701226ad3dcfb313d79fbbb1f1
18e7d016ddafc9c22619482e5453e1c435272f8c
91166 F20110217_AACNYJ arguea_d_Page_035.jpg
0e8449f6b43a1e9b9f9b1453868056af
3207a78f1c00e82ff54703fc5da2099c9d0dae1b
42353 F20110217_AACNXW arguea_d_Page_011.pro
df711b07d168c149af252bfe3baa94cd
df9a24b6578b4caf00da169c1fe7d55488474f8e
F20110217_AACODQ arguea_d_Page_103.tif
260f1d6491a939044d0cdbd9deae7ea2
c172e3f64dbd99abf1d2732874f2652ec811d29b
909885 F20110217_AACNYK arguea_d_Page_022.jp2
647bb2140abbd527581802c6cdc38793
d74653598373a37f4fb9e5ca170bc18016099710
F20110217_AACNXX arguea_d_Page_100.tif
f16f75629e476e09045353273a99e4dd
d12f2000a562d561d2858a9646810d9b2af8a6fc
F20110217_AACOEF arguea_d_Page_122.tif
10e40c41ffb6c9de993564ee7fefc548
4904560944792d8d4bba1f3a0bb39ad36e274349
F20110217_AACODR arguea_d_Page_104.tif
5a4415e017a107574ccff6c09176d6ae
5a018e5dce73db9925a7a53d66e3eb39d2161534
2708 F20110217_AACNYL arguea_d_Page_109.txt
f1570551edcf18481390d531e3a96986
b1d229eb44630dfe402ad757af3fda6f57c44da8
23528 F20110217_AACNXY arguea_d_Page_087.pro
9087df496d3878f78819ca6fb3d085e9
4af3be1c99719729d12f5c09d8b535209c6ed4c4
F20110217_AACOEG arguea_d_Page_123.tif
8ca3542509d981caa08c6575774dad29
f137cbd1e5d72a39fa85d31adfb8789aac719e07
7668 F20110217_AACNZA arguea_d_Page_111thm.jpg
61f401302a10c468832a87f03be9a4b4
2db28b912bd5303eb006ba0ee145cd5255f4a77e
F20110217_AACODS arguea_d_Page_106.tif
c4816ee5c56d826d8780350cfef1a739
1709ed6cfe48f5f631847f8f5040be27c9f0cee1
33642 F20110217_AACNYM arguea_d_Page_044.QC.jpg
ce3e5892c02b70c6d2312dcfd99d0ea6
f8b9d37b26a75e49be9c88f6d2fc78c0eac65bc1
F20110217_AACOEH arguea_d_Page_124.tif
809c9b1c0f164be3942dbf9d13b18f6d
ab383342e64d9a46a1a149ac5be7b42f2c8a047d
93104 F20110217_AACNZB arguea_d_Page_016.jpg
d7cb7fe265988481ece2bdd3fff4aae1
38cb96ac09ccd662586837e8ef4a7e12ee2699c3
F20110217_AACODT arguea_d_Page_107.tif
6f6994d8ca38272a97bd3e22fb2b4c22
6a73036d639a4a5ba80a8ba5903a45eff0d5ac9c
105491 F20110217_AACNYN arguea_d_Page_073.jpg
8299f54ffa56c066fc9d7b04ff88e65f
495ac5fac1c6ff24eda9f34e5167bcb8e22505a7
44567 F20110217_AACNXZ arguea_d_Page_086.jpg
ef62ec56c83afe5c19e3d653a29ce1bf
7a91934dbe9eec152a462fa1859c34aa4768ef5c
F20110217_AACOEI arguea_d_Page_127.tif
d7e4f324c539cf81cedaa42de681fa90
3872110c6e1351b29f5baaec0f0c5fdabee0bedf
91146 F20110217_AACNZC arguea_d_Page_015.jpg
0b18d5be4747c5f2e3bf1405120cde4f
1a474e16ab844539b1b68aff392820693e4b10b5
F20110217_AACODU arguea_d_Page_108.tif
a7e439ec0891916b974659368c478a17
975e50140ed26453e7d8e28973b2c0bb063b2d41
1499 F20110217_AACNYO arguea_d_Page_062.txt
efe2c569f961b871f5aa8c793e87f75f
004826127c278fe419c626549f60ffdc43cb7e01
459 F20110217_AACOEJ arguea_d_Page_001.txt
282f060d36ef79c43293509ec2f2021c
50d504cc77fb74e62f6ef792cfb0a910b1d3e2c2
103603 F20110217_AACNZD arguea_d_Page_005.jpg
050ce9851d8a24d80cad78446f4c6464
8516e8e9be1f8d4c962e84204bde8a8a71778e70
F20110217_AACODV arguea_d_Page_109.tif
675d77c4777c56e46e434a8c9734ffe6
11b713a7fbc293dee7c5167883e4581d73a59748
18627 F20110217_AACNYP arguea_d_Page_057.QC.jpg
c356ddf0d05f83142b7b1e5fbb93d23e
a06e9b96f3f5629add62b0b1ea71f521c064ef21
114 F20110217_AACOEK arguea_d_Page_002.txt
adaf7d269ebd9ec45d15a9c46189e5b4
72a738603bf2a5696c7e39f7d186709cd6fcb398
383 F20110217_AACNZE arguea_d_Page_123.txt
04b3664670ed1ba515c4b3d8d0ab1ce1
1963bc9317ea1e3c64c75a37d5ec9353c6362809
F20110217_AACODW arguea_d_Page_110.tif
8432efd9e42283b76bcd5b54068790d3
08abeea8c7d8651816912b205b48356c388bdc36
31018 F20110217_AACNYQ arguea_d_Page_066.QC.jpg
ab1fc16ea47c1560f0bf96c4b1796b0f
3c2d6d6b10d4832d66f277be65c0f9d4e2fde730
1226 F20110217_AACOEL arguea_d_Page_003.txt
f4f42ae99b0783d3011767f6498628d6
78a65421b12f1688a30b91f827b6168444fe542c
33545 F20110217_AACNZF arguea_d_Page_027.QC.jpg
acdaa6b73be88a3a041ab0c5e4a62a2c
8a8dd559044c230f2c77ce43a37b6f6237d5028d
F20110217_AACODX arguea_d_Page_111.tif
2894549d44cb8d53be2204445e9eadd6
4fea21dfd4a1295ce7eee02cbbb179ec94b048ca
53055 F20110217_AACNYR arguea_d_Page_124.pro
42d63a2bac78b58a001af62a35d7d8f2
a0a1e320ca81c14107f97716d25692ab9f43cd1f
1682 F20110217_AACOFA arguea_d_Page_021.txt
fc3cd2e242b01f13cf8ae10a79077045
75ba3b23f0973ddf0f51f126e6cfe71f3a3ccbe3
3410 F20110217_AACOEM arguea_d_Page_004.txt
78b257c465adde245fa1132351710f18
09797959dff1f86042bd5f6562b906853c92674b
F20110217_AACNZG arguea_d_Page_120.tif
f9cbc0437685a8b91dbb6103d9e28ca1
096695007aee3fc2c2883404203d2a85222c3b7c
F20110217_AACODY arguea_d_Page_112.tif
6e80d3cdcc02e3a97903b7a7a9bdc22a
a187ef32a5c8698c98dadbab6319fed785c864c8
24061 F20110217_AACNYS arguea_d_Page_005.QC.jpg
69cce680fd0a23be7a684a2bab3e2544
00e143c429c89e54d885b9c66daa935fc7cf40c5
1760 F20110217_AACOFB arguea_d_Page_022.txt
399edf78a0bd3a6ec394a80fabd5b5da
a6cc90e8255d6ceca04c52ca1020f0832f1772b6
3732 F20110217_AACOEN arguea_d_Page_005.txt
3b1beaade214c4ba2d2400d06b069271
b230eecf286e6de48bc61c7ea2d656f3ea84bb80
997660 F20110217_AACNZH arguea_d_Page_015.jp2
ab307e54602bd0d90707b8e878888a5b
f43c669dccb214e0dd89895f20e9616e033521a6
F20110217_AACODZ arguea_d_Page_113.tif
017941d98be6b42428e11fe04b3703e5
cbd600165063674fb87f5c009945946591ad528a
1044734 F20110217_AACNYT arguea_d_Page_064.jp2
d866b82cf4ba5a7f149f56af7e471e9f
52c153ffe8502277e661bb01f58feace7eeb3526
1830 F20110217_AACOFC arguea_d_Page_023.txt
31d93b304748a1ddf50b199ce8303c80
e8f0ebbbb916ac254bfbe8103c66a44925738772
894 F20110217_AACOEO arguea_d_Page_007.txt
036aa1182d4c9aea7f7ed58b8108e0a2
9627d7ea6e48c511b47443cdb88345211323f510
F20110217_AACNZI arguea_d_Page_025.tif
c26e8e2bdf06370e59da22ef00fe4e0f
a8760cb66ced61c016903d12defccb8495c3f0c0
1153 F20110217_AACNYU arguea_d_Page_088.txt
e328e6040196778b0a86ea514cd4cdb2
e76ab4fd4651e6cd1b11bffdfb924914be44bcb3
1980 F20110217_AACOFD arguea_d_Page_024.txt
5f7fc65d791adb6ea32bfa04d93907c9
0600052692b3c7d47717b8e521712e5b3c4c6a9e
2355 F20110217_AACOEP arguea_d_Page_008.txt
511c3a744c3ca0060e7e1cf5c23c88bd
c2223f52b430583093d58ed9c435688eb363014b
870943 F20110217_AACNZJ arguea_d_Page_004.jp2
977d1917307595472640400764b4048c
beab00e905e3bcb0b37085322e19b120552b3e9e
F20110217_AACNYV arguea_d_Page_041.tif
38f9dbc8184f894797b2c6309b221bca
15ac8f0ac3ef21e631c5d7774d4bdd0d984ae9c8
1971 F20110217_AACOFE arguea_d_Page_025.txt
f9f1e93cef80ed2b90137d79701a149d
b0a4025846232e65aba0d5ceab4e29e412db7404
1715 F20110217_AACOEQ arguea_d_Page_009.txt
ca88b14d2e71299f8c1555ae07bfe5f4
ba861f9b7865aa02ec0ad5d0de4f7c77348c5fc4
25330 F20110217_AACNZK arguea_d_Page_127.QC.jpg
857f00bc0a5b184e05511242c33da05a
994e0175754fe2c6defc93364c43d15f9b92029a
8568 F20110217_AACNYW arguea_d_Page_001.pro
c902d81327567ff338d42fdf84de4802
766f43359545e60094ec0ef15a93784b6dd9efc0
1899 F20110217_AACOFF arguea_d_Page_026.txt
2428437a1ac74fcc2d8fb0c19a3585f6
c326c4b67b2f07f1b6bb360ab8264880e70f0e56
1791 F20110217_AACOER arguea_d_Page_011.txt
8e41abd4756212db6d62082fd88fa471
2eb8891dee55cfcaa9ea23985bf9391bc6c1025a
F20110217_AACNZL arguea_d_Page_006.tif
c609a229546e1076ff5af0ef195053eb
c965891a01df28462d7b205a36caed1d9f3135eb
1896 F20110217_AACNYX arguea_d_Page_047.txt
90ad22e7f4d6aaacbd76a13ec8629657
dd9b30d93b0a05b35ce63068099512ee98c44b2c
1928 F20110217_AACOES arguea_d_Page_012.txt
e7b4f05695c62ed195669ab4c0c3f61e
84883d2a6a6a269a4353eab39d9325d5c297f10c
2297 F20110217_AACNZM arguea_d_Page_115.txt
2eaf1efa5f621fbe5aa05db3eb3f485b
7d90b4964223d4e9ddcb543b1d55697cb4c889c3
F20110217_AACNYY arguea_d_Page_044.tif
5b48ad8f8e21cbd0e1e49e5d4f456bb8
c0273ac1a767b95c376596b1dbe86e5626c20666
2046 F20110217_AACOFG arguea_d_Page_027.txt
cdda821f090a5ea0b6fe974071f0d0a2
911ce907366c998f65da4d128bfcf2210d4c0081
1997 F20110217_AACOET arguea_d_Page_013.txt
8e8ba45ae6dd8a3cbbcf1d1a9efdc02d
f705c0cb5ccaa34941aa584cea397196a21bdd76
1020034 F20110217_AACNZN arguea_d_Page_016.jp2
3995d80e1e8dad1a6be6a238d2a61993
82f9f8b5621fa895638670804774c125874d3b3e
F20110217_AACNYZ arguea_d_Page_040.tif
e44220f1198341212c61cf5dec4a2659
95e3db434b1ba9d20da4dbe8d0ab50de8249f2b2
2078 F20110217_AACOFH arguea_d_Page_028.txt
b864a09df1a40ca00aae3b529db77a47
add07881e3905858bf4b7185b72aa91ba073dfe2
1040 F20110217_AACOEU arguea_d_Page_014.txt
b84134864e5e2bf4baa87df2c0a7152d
96ea31f4cf3d0e4d283f862623737d08f54dfb93
69786 F20110217_AACNZO arguea_d_Page_126.jpg
257b1515c4b88403074e49898e245ffd
7df5ad66e1f99d9781f5dee4347c82de40b8f4d4
1872 F20110217_AACOFI arguea_d_Page_029.txt
cf37b4309340ebb71c08dbd064707f7f
c4787e922349db2987391a90a957e2d75ff2d3fb
1807 F20110217_AACOEV arguea_d_Page_015.txt
bee66e2a671c6156a8f458f2af763b8a
6bd277d0e99867a3ce4c1d43ac3be59d398fe64b
F20110217_AACNZP arguea_d_Page_125.tif
0e178ab3afff03e43426873befe1c91b
3bf113830ba669a1b18307acead2cfcda9536684
2053 F20110217_AACOFJ arguea_d_Page_030.txt
f07cd2af7379a8974d6f978d99b31807
3dda1a09b422771c16abae1e99ffa3d3ee7480c9
1858 F20110217_AACOEW arguea_d_Page_016.txt
1f52520d4eef6d93cfc6fe5067e8e01a
393ad85fe824cc5f5c6affead5d60a3cb10e113a
355956 F20110217_AACNZQ arguea_d_Page_092.jp2
67e1e982e56358443c1f0c8528582388
2f8728fa9d6a9e9af99e78df077883f8848f9fde
F20110217_AACOFK arguea_d_Page_032.txt
32d1248f1ee6696d19acc5c279038f47
03340a4333b55902f07de9225c8e8f911412d023
1948 F20110217_AACOEX arguea_d_Page_017.txt
6583f1362d12c77b4d66ffc2a0690cb4
9935871d13c34bbbdf32d7c83d7c4086312c296b
119301 F20110217_AACNZR arguea_d_Page_044.jpg
9da9bb25aa6f05b020a9ce4f879081f4
549fc7a9abbbad58cd76a2a65f21c190f8b70c91
1926 F20110217_AACOGA arguea_d_Page_052.txt
52bfa0fc06dea4e522fee07c28f88415
e5bb53c81c70060a509a92fe88df3719ba6fe712
742 F20110217_AACOFL arguea_d_Page_033.txt
535fae95dadb50beaa4641a6725ea376
7c0e9d4509800e4ca6c1feab330dd19e2cc27918
1736 F20110217_AACOEY arguea_d_Page_019.txt
57e823b5f88381f68a4075a88a11085f
2930b182b1585727c131a90c2ee9780dca7a27ed
41786 F20110217_AACNZS arguea_d_Page_092.jpg
d570a835e76129a280b2692c916fb04e
2b1024662077d76e0dd5906e8261a64f8534c856
1093 F20110217_AACOGB arguea_d_Page_053.txt
135391b8bccb5dee46b7dff94fb03fa8
2a84972cd70d5d76f8861fe4b9f5a57d5ef54045
1884 F20110217_AACOFM arguea_d_Page_034.txt
d880474ccc709f19a7be92237ce099bc
1a7ef920988c8a182009ee5c24c991347fb146a6
2110 F20110217_AACOEZ arguea_d_Page_020.txt
4fadaee2aa1680027a62e34b4fb14aeb
8ee034d7cd788f09ecb153e90e58deed738263e1
1008055 F20110217_AACNZT arguea_d_Page_034.jp2
8e2e4b30dc2fa83db966aca5fc46ccb5
7ab23e40b5b1ae43a7f72d433ae63bde56cca4a3
1211 F20110217_AACOGC arguea_d_Page_054.txt
7c098020f1bcd6ae4e2bd531d90e96ac
e0e5f9b209551e3139f6e8a7a23c6ea2d5ad025e
1800 F20110217_AACOFN arguea_d_Page_035.txt
4e47eedd136259aba07b2495845bfddd
b86a2a95e40f0241471dc9cf7f3c53cbda155eed
386327 F20110217_AACNZU arguea_d_Page_089.jp2
56dfe336256e9e2e322353cd5191a438
6748b22dce72728257c5ed133a4b6ab382321a69
1239 F20110217_AACOGD arguea_d_Page_055.txt
da8352b237fdd51b6b509ad644642ab5
631ca4786ca576ae93be92de5e53e0359789e275
F20110217_AACOFO arguea_d_Page_036.txt
8138ba57a836b65a2e168c3d7856e98a
44405f4ac675ad3a56928baa2fff3dfe8ffaeee6
84210 F20110217_AACNZV arguea_d_Page_009.jpg
3b3e8a3b4874e1f4a66b00fdef9891c8
3dbfa8abf162c28714871e050796dfbaf0644864
1453 F20110217_AACOGE arguea_d_Page_056.txt
6d44e06e42de9637011b46a42973e347
394c091dcee762c4fff2c4de35bc79a9401f7139
2101 F20110217_AACOFP arguea_d_Page_037.txt
dd2383f174859782ee01501531d5af7a
2ac9139b345f0a24e67fba11ed0f191a084b94b1
1953 F20110217_AACNZW arguea_d_Page_031.txt
cfd1b1a07736c11b4dc6dee19cbbf741
396382de551d679e8445a344833a6e87df636bac
1016 F20110217_AACOGF arguea_d_Page_057.txt
b1ec867c89883162a34189985866fa2e
ad0f675d1f077e3d657422f6c17f35a6c09bba0b
1951 F20110217_AACOFQ arguea_d_Page_038.txt
ec079670bfac9c84d680a786d8bc83ea
256882af995caee1a5ee3595857e75b17b297488
F20110217_AACNZX arguea_d_Page_076.jp2
895b412b1eed6a1280643b5ae26cf1a6
0a6c1986cc4d805d394c00c39bf36e8465476cef
1635 F20110217_AACOGG arguea_d_Page_058.txt
d62004c5a2cd3d38dc9e11b79601de7f
b3ec8bead2616da1ecbc98befff89e88207483d6
1739 F20110217_AACOFR arguea_d_Page_040.txt
70fda4554a35d62255b0e1fb84588eeb
54eac35a39eec8d8caaeb5deaf4b2db33bcef70f
48838 F20110217_AACNZY arguea_d_Page_070.jpg
b6c368628cac302f7f7dc736cd9c18db
9120dc0ae0b6b6ce0c2b30f2de2ffd5e8f4ef7f3
1414 F20110217_AACOFS arguea_d_Page_041.txt
af08e435f83f5b45350aebd63133b703
aef95516554a7fdf798604a9829980ced7e8d09a
F20110217_AACNZZ arguea_d_Page_105.tif
f4961a8c7d163a51496b355ee1b730c3
5d4c26349b7f4e2083320fbc41563e85e2bc9026
1701 F20110217_AACOGH arguea_d_Page_059.txt
2c88d27cdd29b78fb6e5dd655d55ae18
aaededefdb939b1061a817c1ac8e95fa32d3f742
1687 F20110217_AACOFT arguea_d_Page_043.txt
e2baa03d8106c3f9275628012c57a27a
36b4d17a86bb37cb950cc88c2d9f83c5bb325fc9
994 F20110217_AACOGI arguea_d_Page_060.txt
8fb40cf40f577a60bf08c4203a0d757c
fbdcb5d3abe143448e0c9e8858ff6c42c1facb48
2502 F20110217_AACOFU arguea_d_Page_044.txt
29378dbb2a6bc51345d94c215f2b75f9
59f4ecf8782c1eb45489ac498117b5ccc32047d8
1191 F20110217_AACOGJ arguea_d_Page_061.txt
a23300ea72d661ba40e1d0a634bd6bad
e06de75f1158396bf1e6c3380257a60b701a4a30
1608 F20110217_AACOFV arguea_d_Page_045.txt
4d90c6fb063282ed7d55c3907f622a91
4e9dc3bf31c92d1a5bc6fd1a732344918b8eed91
628 F20110217_AACOGK arguea_d_Page_063.txt
42c7929d06cfe6ee4df64386a802e746
1b15bbf3e45a7541fd7af6d752c7c5c9150c6edc
1552 F20110217_AACOFW arguea_d_Page_046.txt
6060c6abcf1db54cd804a285da6d7f84
4bd83c8e97178989a9bbdf0a49b09895083f724b
566 F20110217_AACOHA arguea_d_Page_084.txt
2ed432db2505451cb9e3b27f7cb0ca15
d9da5777e3c418413de0164fb8c9829675fa2357
F20110217_AACOGL arguea_d_Page_064.txt
bae1dba07c52ff3eef50d4a41f10d950
486c00e2899952ea57d021b05af9906a0e159a87
F20110217_AACOFX arguea_d_Page_048.txt
6e02c84c66524f5a5e81aa9b7e3fe38a
eafe76bc2b3688cce11da3eb562c301355451aa0
639 F20110217_AACOHB arguea_d_Page_085.txt
3b402df7b99951d06fae0fdb4142d425
19b503832f2ad852870b5b29292d8522851ce1bd
1877 F20110217_AACOGM arguea_d_Page_065.txt
dfc356919db44bad238b67f0bde8fce9
756229455598ece3a5ab7a4556494fdae1ad6630
1895 F20110217_AACOFY arguea_d_Page_049.txt
857d8ea3eff6cf85b8aaeffb0320c6d6
b2ffa8fa11fa70fe470a138b85bb8961b13f2571
616 F20110217_AACOHC arguea_d_Page_086.txt
ad9f134aff62a47da6799ea73743ff92
8184fe906e9b48ed787f7dbc92dd4bc179965c1b
1647 F20110217_AACOGN arguea_d_Page_067.txt
c99c9e4bbc4a4ed32dd8450ed15c4a38
5e4d6ff67db698fa05ff7296a2c45f33dc6730e5
1563 F20110217_AACOFZ arguea_d_Page_050.txt
76c138186ca62a799d3690c51c15f88e
6f28acd4c264d1628e68c30a89d094de402f8c9a
1220 F20110217_AACOHD arguea_d_Page_087.txt
04c67c6ad96b09fec5bd5450d44723ce
3061fc718d60b79d720832a734caae7b7a8e48ff
1470 F20110217_AACOGO arguea_d_Page_068.txt
3cea80c51eee939c9729bb0118ac94c8
7f967349704bd0a5f2da48816ee9ff77454bf3de
739 F20110217_AACOHE arguea_d_Page_089.txt
027c8b74807900794f400e696f7e1149
e0abccf16f131bc0f293fc4b1a95e8f5124f3c5a
2143 F20110217_AACOGP arguea_d_Page_072.txt
4eafc488c19d476114b14594bbeebeb6
1e54c905bc9243190ed13da7ed2688aa58d7d227
F20110217_AACOHF arguea_d_Page_090.txt
c767825df6232102459e57b91ad83b5b
0c6b528ba00b5b9941569e3cfe7a743e54d5b651
2072 F20110217_AACOGQ arguea_d_Page_073.txt
8952b8ff53f588152aa4e2c1964e7664
c7dd3bb2b53529f33281e950fb6a1894b212738b
707 F20110217_AACOHG arguea_d_Page_091.txt
3d4ff708c8d7a64b8929b6f9b3b678e4
dc51c29fdcd85599433904daebf9d22e6597fa60
2070 F20110217_AACOGR arguea_d_Page_074.txt
081e7055d698cfd21cec828af53533b5
a087828d447353d2796f9ec460d63d6cd1830628
710 F20110217_AACOHH arguea_d_Page_092.txt
9c8d4920c7f71bb60a589989d0e90f22
f2b9ef9680e43197705f3c0226099bbaf3b2a28f
1870 F20110217_AACOGS arguea_d_Page_075.txt
bce351af566bddc1e4cb8111f2bdb8b2
5c5665bd4b16e33700961f7870b3484fd578cab6
1993 F20110217_AACOGT arguea_d_Page_076.txt
a2ce3fd7c96028b8a9aa5ba379da4c79
41988bc7d094bf3227a209e74e41a667ee2c6341
575 F20110217_AACOHI arguea_d_Page_093.txt
f667f95dfb02892fed9e762e5599c834
70c62d50508847b7ae35b1839fcf6762ce8829d8
330 F20110217_AACOGU arguea_d_Page_077.txt
dd46dcbb37767161eee2e788afe7f58b
6dcaa55f5d28c491ae8a72d02c597e051192ca96
1085 F20110217_AACOHJ arguea_d_Page_094.txt
bda7f545b46dd817ff2990babaa6614e
72f971873acbd8699fa1db0ad3ec278fa553e4b6
1757 F20110217_AACOGV arguea_d_Page_078.txt
12d924eb10cd8478116b411253251191
923de61a52326d6ce59643385f794dc5953660b6
1046 F20110217_AACOHK arguea_d_Page_095.txt
54de469ddb95774f1b25a45c5ec83e6f
e409755a311fb7fa757c6722e08a150791243a43
1981 F20110217_AACOGW arguea_d_Page_079.txt
55ad4c6a9c34c1668d21c45bc10a6874
24814069e5881cc4776f441ae517972e1e6a64a1
445 F20110217_AACOHL arguea_d_Page_098.txt
1b8589fd5085a272834aa535db344198
a40776aeede1f53cd7ecb62658af26853a8d0d39
1903 F20110217_AACOGX arguea_d_Page_080.txt
5983fd78664505baaec38ea4797fd73c
4c5489fc9037f7188c3c028cd36a0f674c1359fb
1208 F20110217_AACOIA arguea_d_Page_117.txt
9d7724443e5634061f9b9494d3f78945
f5ca8286d5e4818a8e426993c7100bb4a7c7e03b
780 F20110217_AACOHM arguea_d_Page_099.txt
16f2ad541b8e065449ebaec28c2cb1de
dec0c2eb94a9d89c8a85abfc1ea6fcfef2edc5e2
2031 F20110217_AACOGY arguea_d_Page_082.txt
c693641131e0350a7de5cb92634709fb
c1b5b5209517971816dc24be3dc491fb3a42d1b1
1337 F20110217_AACOIB arguea_d_Page_119.txt
3bb7945a32ba4640feb3fbb7dcbc1344
d1276eb53a17d1a62ad399403623550db805c137
4308 F20110217_AACOHN arguea_d_Page_100.txt
50c3cef880f4bb856daa7505af19b9c5
bf19922d12bfaec9a0137711e5d1a4861ace49d3
1825 F20110217_AACOGZ arguea_d_Page_083.txt
5168a4f751e165a251e0a3c483ae81d2
ca1bd049ae82fb1e0add2b09d98de8931a45794c
1073 F20110217_AACOIC arguea_d_Page_121.txt
48da5825a207d547b6083e227f88a2f9
209de99e504de5e09be0dfc822bcbbba7c3a3e14
5356 F20110217_AACOHO arguea_d_Page_101.txt
29be320e588c6754544de2417d974250
9c8556c708c9f17b2707109c62022f90b8c3a453
2165 F20110217_AACOID arguea_d_Page_124.txt
4b242ff791758a5c26aeae68a6a6e58f
ce2f1af83e4761aa4957200540696ff328b00b81
2589 F20110217_AACOIE arguea_d_Page_125.txt
67f1a75d29cb1a4c17b4421d95187e37
a724ad76591516bddd0c9bf0deb1e27e0264978b
2692 F20110217_AACOHP arguea_d_Page_102.txt
f52d6753d1c2cf02e7f506f1370f0179
d6d39ba707be57ac803420a06b1d6cb9426ea0fe
1351 F20110217_AACOIF arguea_d_Page_126.txt
12344ed744d0b83e84487b4626cb6bad
e25ef4f1fc733cf703e06d5a1ba652014a48b34f
5781 F20110217_AACOHQ arguea_d_Page_103.txt
4e5982e79e6608446d134c032adacbe2
251ac243e98494ea396e565cb984a8fb38fc8f13
1613 F20110217_AACOIG arguea_d_Page_002.pro
99615b4a4122a543e1b3bbba2aaeabde
2032741e030f3534858508affd5cb3b568e9dc4f
7046 F20110217_AACOHR arguea_d_Page_105.txt
ae0dd0739b418832472b4bf6011706c1
a707f67413260fa2ca071984974640c7786d866b
29681 F20110217_AACOIH arguea_d_Page_003.pro
19aeecf86833ecc69ba98fb9c0db7f0e
e4797ab745ca3ae4bd716e9d2c95c90a644c2ede
3341 F20110217_AACOHS arguea_d_Page_106.txt
5013eb6c04e7acb503aeb5137567d29a
1c1f8cc80c0b12a9b76e94403bec7ea1b5a70578
82990 F20110217_AACOII arguea_d_Page_004.pro
6e88d4ebe8a28a5fc59ffa56d56ff389
a459b51426e988713f9e5e2f8bd6fc8678d40727
4305 F20110217_AACOHT arguea_d_Page_107.txt
aee50dd3ef9f3bf087b2f8e34d473d42
607930df5e4b1e13741cf255808827d578d83214
5806 F20110217_AACOHU arguea_d_Page_108.txt
79eabb130b01afc079abdcba42a7e22d
d97b682575ee52bf40ceac69cef44867455901bd
91627 F20110217_AACOIJ arguea_d_Page_005.pro
750c507a1beb66a7c1e14bbfef1db634
bde5e2ae8aaa5288e72bf4e42259477c4ead6ca5
F20110217_AACOHV arguea_d_Page_110.txt
22056a1a3424ff6e284b6c6c9ed43c79
06953801d13c617d28f4abc1d1cee70b662996e5
20561 F20110217_AACOIK arguea_d_Page_007.pro
372ce870cd79c7bd0e015b449fb20286
0fa2d889d007a5c67fd6b24de1e3e8e72a2b898e
4978 F20110217_AACOHW arguea_d_Page_111.txt
feb5dbdc9d85eff8ee1e3cae1ec11df2
50fdd2d19d070743decc6077b515cd9f81cb779a
48028 F20110217_AACOJA arguea_d_Page_026.pro
1f075c1557b1d43c2132d8a4d700aa32
67f4e57ba94a38ec04e8e91dea3e00d60f71c656
58362 F20110217_AACOIL arguea_d_Page_008.pro
99d581762df5cb4e8b35164d99931769
21a53d4310b06d14ee1050d95442ec7dfe27ca70
6996 F20110217_AACOHX arguea_d_Page_112.txt
23458c2addb701a2b8cedd72b875e627
9f46a1c6f0844493af8d415f71050665e5523134
52145 F20110217_AACOJB arguea_d_Page_027.pro
e9ba54e76ee41dd32e9be9fd17e80929
5065c9207b265505bdd3c03525c77db0a9b7afc8
39111 F20110217_AACOIM arguea_d_Page_009.pro
b33858ca046bf78dd7109530d1d49234
30453fbb79a40dd307517c6299aea2e27c9647ef
992 F20110217_AACOHY arguea_d_Page_114.txt
a7ace7e0a754cad9b2fc6a48f74e6cbd
615fc316a91673f552e604a74cdcff7add70b484
52320 F20110217_AACOJC arguea_d_Page_028.pro
205287d2af32ce1a5c78ed3c91b371ca
c0e0f959a82e5d3141c6c84cc72a1f330e397161
22445 F20110217_AACOIN arguea_d_Page_010.pro
5eb1bbd6adbe83fbcdbfef572760fa29
af6bc25182f3c19295cd367c89842e6f8fb15170
3246 F20110217_AACOHZ arguea_d_Page_116.txt
005b0e761f411d10d225cf8f01c0d165
e17de79741a576ebb82099514d3f8282ee747d32
47079 F20110217_AACOJD arguea_d_Page_029.pro
8ae4fb9b96b999a47dca129f6044ff68
f411e4a52af8a4c32029d07e87ac6a31a20f21f2
48897 F20110217_AACOIO arguea_d_Page_013.pro
09a2b7e9ccfb75f9fb71f363eb08c632
01865e51cd66de6d992986a12d2095379007e718
52386 F20110217_AACOJE arguea_d_Page_030.pro
903991da7747a8dd2db6f927c2bd62e1
d829bce377208ed6985ed9c11e10ae7b7c232ea2
26037 F20110217_AACOIP arguea_d_Page_014.pro
bc3aba52eea56b6a1a4677a192dcaa31
846a3a95dda509e9688ffbb512beb1032863944b
48953 F20110217_AACOJF arguea_d_Page_031.pro
af7f9cfa7998c2518188689e1ac7058f
be321bf2554cd4cf8612628bd67eb7d1dd341e47
43743 F20110217_AACOIQ arguea_d_Page_015.pro
a6927036c39ac800e4c59d08702ff704
43e32ffd8886138967b54e9eb71afdf7e4578b3d
44206 F20110217_AACOJG arguea_d_Page_032.pro
6b93d000a264b6e11d3d0a0daf8270dd
6f4198c98b301fa1041ddaae2766bd761e0e2b04
46006 F20110217_AACOIR arguea_d_Page_016.pro
43f20bf64594f9d3822b974c10e7bdaa
c94dbe2c8a11e28df8318db2f280251c046e28b6
18503 F20110217_AACOJH arguea_d_Page_033.pro
6e0a7c9c2a8dc8c2b2961c5fab170aea
5a12f11b7b65fda76e973184da3d4d2161aabfbf
47822 F20110217_AACOIS arguea_d_Page_017.pro
45dde0631fbe1dc9928909783e2fab0a
85893d6c79a74cab7c7ecad05cde5dde729eb10b
44004 F20110217_AACOJI arguea_d_Page_035.pro
4ae06fb3d082ecaacd21a98dcdccede6
0c283b6c1d049df06cf1b0acaf8cf1bb1a2f73bd
48160 F20110217_AACOIT arguea_d_Page_018.pro
1fece2a930a4434c3fc5b75398e19de6
1346e736756da180be35849756503f123d6259e4
46736 F20110217_AACOJJ arguea_d_Page_036.pro
233ffe55b8659904d1d455e192fead39
0898f67d5c74178d5c3858f10280578867a877c2
41816 F20110217_AACOIU arguea_d_Page_019.pro
4a4fc794ff5d7616d3482dd59e19d992
63fa77990c5387d974e0421d118700aa28bb8608
53305 F20110217_AACOIV arguea_d_Page_020.pro
c86ce240bb639b9289a729571152918d
5eb7b9a116856df2babb7e21ac0a382b4a720948
53578 F20110217_AACOJK arguea_d_Page_037.pro
79e901699ea274e3a8ccca2639c28fb9
b3cb9a7a789b8c6767105d2e5fba813e53e93535
40923 F20110217_AACOIW arguea_d_Page_021.pro
fb6c889dafb2a62fb9b9f115b47b367f
325f4f3a882836e2550243a001773e5664810c5c
48928 F20110217_AACOJL arguea_d_Page_038.pro
240d9e7d36299b55cd82f1be74adc8ef
171e8e5917c21f6588af7682dccba23085920301
41745 F20110217_AACOIX arguea_d_Page_022.pro
55ee3fdf80ea6962e89cf96a14379b04
2a26b59b2b27dfe416fc22b6e1ee3a9fb404a1d0
23274 F20110217_AACOKA arguea_d_Page_054.pro
e37d924e9fd166edadf353852f4c0e3f
c5cde00fce194531c5cfe03176d6bc26c6ff5101
36052 F20110217_AACOJM arguea_d_Page_040.pro
a5981e41a755e06a26a9768ba6681a18
f4f05a597dd97e7c0dd7d8de7576a647a894cc79
44913 F20110217_AACOIY arguea_d_Page_023.pro
45e30cb0ec9f75874c3f341872153031
b0600937d9bb0cde3f5cb5c9c4c7d0aad4fe95a1
29626 F20110217_AACOKB arguea_d_Page_055.pro
61a3d4910d97a759fe7ed13ab8797e60
b6b4d449cc00b17139b23294465eb3884a00f64c
33445 F20110217_AACOJN arguea_d_Page_041.pro
1eaccacdb7f12e1f169f14e21f1c666f
f2238ce790fcf1efd575ae481fcc286d42cc3032
50083 F20110217_AACOIZ arguea_d_Page_024.pro
b12b2c886f0c171e96cc91b01b80d1a0
d6fc55ee3394870c3be47b0f6e9a827a638cb173
32545 F20110217_AACOKC arguea_d_Page_056.pro
02837fb1c2832ec6007d4dba1728dfc5
f0559c344da5f33965aae466d857b8e1aa1319f0
26227 F20110217_AACOJO arguea_d_Page_042.pro
bc7d069379ffe238d5ab2cc90982ad7e
439b298ce2121c7909b106bbc1359c22bb5afb1b
20079 F20110217_AACOKD arguea_d_Page_057.pro
493fcd2d4f0cfcf298a4a1a476e68abe
8df757a7d7acafab78f793603f30400ef82d4e94
35549 F20110217_AACOJP arguea_d_Page_043.pro
783dabe19178d6e6eac40bf3081dcce5
43be5718bac044fc07ff1d36f459fd415f3c3894
36742 F20110217_AACOKE arguea_d_Page_058.pro
0976016ff8e51560e94c739c428f7f48
948276eb450e7ce9f126dd2b3964d2517ee1c5b6
60889 F20110217_AACOJQ arguea_d_Page_044.pro
00bf1ae8818d845c098daa88878d02e3
a7b127a01c1ed018b929cde1a522bd6e7dc8c2f1
37295 F20110217_AACOKF arguea_d_Page_059.pro
95fdc9f18fb75df60acb675b61f15fd1
26cd00ad43d11487d9f1094ec9bc0da7238cdee6
36766 F20110217_AACOJR arguea_d_Page_045.pro
ec5dc752a955fedb1b1beb190a29916b
4528b3c9b7a4eff44f0bb7fe4fd5b47aee3fc8ef
21958 F20110217_AACOKG arguea_d_Page_061.pro
cb0ed40d3e29e937eaa176f8fb2190f1
5bf71c60402a83f239283791d914e61b4b8da5e4
38659 F20110217_AACOJS arguea_d_Page_046.pro
c3172e533436499118130c49d091bbca
cbea5e56f0c2379743e65ebeac85b7761af2c7e5
32823 F20110217_AACOKH arguea_d_Page_062.pro
0eaf4c6cb55b682073b2f53a5d023be3
c04f7a34c5af7e036f81cdaef68c2ac1d2fb851e
44736 F20110217_AACOJT arguea_d_Page_047.pro
17f97670109be63f81bad66353af1616
e6ff93cc069b73c2ecaa378644315863ea55b699
11446 F20110217_AACOKI arguea_d_Page_063.pro
4b28415441fa871a4f6fd6836d2d1333
f31abebc11c1a0e34d2c289c39279c781db2386e
48850 F20110217_AACOJU arguea_d_Page_048.pro
e1f13b0fb256f0eee390b76a7084c1f6
2b5f7f5dccac77bf3d979e252d6e1f25f6a85509
46310 F20110217_AACOKJ arguea_d_Page_064.pro
ba300f1346509f47b39b327532c8722b
e763793a07240f55c7f7f91dae356079edda78ec
47627 F20110217_AACOJV arguea_d_Page_049.pro
aaf1556d58d9cccaa607fcb176fdaadd
48be2774888c48e99936d671d0096fff5585b4b1
47170 F20110217_AACOKK arguea_d_Page_065.pro
21c03df3a91a5905d0d1ea6f247593c3
423a194a1cfdfeb5bb82535eb804b19e96af8e3f
32339 F20110217_AACOJW arguea_d_Page_050.pro
3e163563fa0bbcd76edd4ba6529d9388
445e9509cc55614910a15c588cdf570b6d11520c
42860 F20110217_AACOJX arguea_d_Page_051.pro
3a555aa12f63eb50114105f5db22fecf
87884fb318c35c687e2db0ec8348167c7c7f0fa2
51640 F20110217_AACOLA arguea_d_Page_082.pro
3b133241c9a3187c7c0697e0a979d2ae
984c7feb50630f498bdc54b242b8bd01dc98eb93
47125 F20110217_AACOKL arguea_d_Page_066.pro
412dd6520b2d8bbfa758cd02df2515ab
aea3da84ebc342378ff1ad4529a8895830c7ef5a
48344 F20110217_AACOJY arguea_d_Page_052.pro
078ea4315a9c834250b45354cf520923
7a1550482d603d5622209ed570ee1e21900d6786
46123 F20110217_AACOLB arguea_d_Page_083.pro
8451d81adadfdb4b1929e828bb143c7d
b6291fa604e2bc9823d04acd76be786cf16d8a37
40902 F20110217_AACOKM arguea_d_Page_067.pro
81e88820118afcab62c7715f001d0db6
38b38cb9c4dd319eb7e63b498c5a522e620c9d89
23202 F20110217_AACOJZ arguea_d_Page_053.pro
29d29f4b2295fa4854f62e3e4451836c
b852ac7052df9d8d928adec5f7cace549362603d
9530 F20110217_AACOLC arguea_d_Page_084.pro
418dd6987b72b89f7ae1e9938d0a60da
24421236acb6b6045af0e73f21fe20cf0c917b01
36315 F20110217_AACOKN arguea_d_Page_068.pro
d9ececfc475b82d5c7033d938032f9e9
e76550d557d3c8dee2b42dfa4eb130a2468e33b3
10857 F20110217_AACOLD arguea_d_Page_085.pro
0f0524799acc57fbff5c2ecaef202382
b59a364daf0a6ee4481f60a6b16f4188e57cc468
26274 F20110217_AACOKO arguea_d_Page_069.pro
6950912da7733db4e0bcd83cb071f645
1b837f713171cff9e335ac0b1532777c9b3ee4d6
13137 F20110217_AACOLE arguea_d_Page_086.pro
5de9b3e673d7753b907bb4fdf8c0c04b
37ae36c53bb64e57886582a4c58ae57e5a78c5c2
22589 F20110217_AACOKP arguea_d_Page_070.pro
c7d79c8dfb2e4512823a789f6ceb14a4
5a05096971b35ca447dd009e1ec2cdb525bb1c7e
21570 F20110217_AACOLF arguea_d_Page_088.pro
9604945bfb964f02c2f2527feeb76865
00f398a29d430667d26136137c4a3c7ff9a9fce3
27608 F20110217_AACOKQ arguea_d_Page_071.pro
725d981468ca9bdafa86171a670b81ab
228943dddbb434ecad2b41eec000a135875fad09
12864 F20110217_AACOLG arguea_d_Page_089.pro
67214099d8fdd1c1004eebccf1f70586
36a042a1880ca1757ec2b8f66a5aae55a4082ce4
54748 F20110217_AACOKR arguea_d_Page_072.pro
0e913f11491349f8cc704850cd2a771b
753489fd5c9a7c7e9706757ae1f644319377a152
6048 F20110217_AACOLH arguea_d_Page_090.pro
8f14bc339d6f476455dfd665fd0bfc2f
692bda99d716d333d5753fb4242f22a3612e9288
52819 F20110217_AACOKS arguea_d_Page_074.pro
7295f7290a7907e0a3ac4cfb2bfc7326
f4cc76fbcfa67c11e0b563cbb78ac7c45a6665bd
10510 F20110217_AACOLI arguea_d_Page_091.pro
0fccc3a80290729c33b94f0017462fcb
dab2d7180ea5483f62cfea2d6f5e791a05ce8223
47221 F20110217_AACOKT arguea_d_Page_075.pro
de2b9f7faeaf92d603e5f016f01d0b75
61274e32980a315900847a751c675bfc06f26c62
10468 F20110217_AACOLJ arguea_d_Page_092.pro
4a4fad09b45ef8b81c8e72c52aef54be
872501d082d918990692fd83806358d0a8522bb7
50543 F20110217_AACOKU arguea_d_Page_076.pro
8297e1a154f355d7bf2845e8cabdd1d0
93ec11a97dd19a666b650d25e499bcd9da83f196
12053 F20110217_AACOLK arguea_d_Page_093.pro
ad242dd656cc6ae71cc1b249971c636c
a77e35e59118ad4368844ae393135cba5c192766
7241 F20110217_AACOKV arguea_d_Page_077.pro
6d411c282a2685ce078e5d864effc7f5
89b22b77f153b63f25a88c7c075605f442702c56
20332 F20110217_AACOLL arguea_d_Page_094.pro
ddddbb641f35849e6c573134c2f9f41f
8e92dc7ca6791655e076eacff7666dbfc62ebeae
41905 F20110217_AACOKW arguea_d_Page_078.pro
cc9d001e1cda9826ef9b144b13a832a1
7f428380c927ed6fe440b5ac98fe251ea5e28763
50227 F20110217_AACOMA arguea_d_Page_109.pro
ff55df295aa139bac1c16f1e35a2cdb1
8a4d3f326c7aef7534961333bde33a69e6257e57
50193 F20110217_AACOKX arguea_d_Page_079.pro
d099f4f415569b9f66b5904285ec8f1e
a767e0b71614692fc0ed582abcb9e1deb8eef46b
97114 F20110217_AACOMB arguea_d_Page_110.pro
7a5641d48ad6b953ab4f64babdfbe0ab
f0c938f57e4645d93f2f588eb0c917bc4a8d97ed
19690 F20110217_AACOLM arguea_d_Page_095.pro
dc0bea5f2adef165cbacbb371fc4047d
45726d0a584822346a5d53cb8d5e9bb0cbc4c8cf
47160 F20110217_AACOKY arguea_d_Page_080.pro
472b56a6ab0ba82f94ca3260a1264bd7
b6fff707736d812927a1fc354c9757aca34cb4ac
89258 F20110217_AACOMC arguea_d_Page_111.pro
726f9a5ea81da6361ae800dc8d45e800
4375345b24babec1e5d0a1331e7e707567556961
14681 F20110217_AACOLN arguea_d_Page_096.pro
72c9ed7fddb15b76c271f4734c728b6b
5ab41382be92882a9af63aa6b291f592c53c30c3
51993 F20110217_AACOKZ arguea_d_Page_081.pro
2a9dc257276112da187599bdee512ab0
33a70641d41ba27bb322d7d69b32b3c284f9a7a1
94853 F20110217_AACOMD arguea_d_Page_113.pro
e5eead9f5fb45cdbe42446c8b7634409
8d6cd0e163cbc5fae11e9a33dc0727c7c9c07ce3
8616 F20110217_AACOLO arguea_d_Page_097.pro
9221f40c12f690a4cbc065023255a03f
8dd2b40316452ca20782e2fe45a511c419dfcb9d
19389 F20110217_AACOME arguea_d_Page_114.pro
0936525f8f3446e62675c05aeb4a788f
41a1cb89379c3534124587f293a079275d83b51c
6467 F20110217_AACOLP arguea_d_Page_098.pro
6af969b610280957996c07169fa96f9e
4cfc5d884bc08a37db3e27a05551dbf728d8b835
52763 F20110217_AACOMF arguea_d_Page_115.pro
54fc96649b14e6abdc234a69035e0f48
cafefd2b01b61049f33732791d5d90e273f94fdd
17219 F20110217_AACOLQ arguea_d_Page_099.pro
0cad7c6381e8487d1e3443c3a571401b
9806ede3ce325b163bc541592d8286e73b018c07
73454 F20110217_AACOMG arguea_d_Page_116.pro
227ce865ecd30b4d21ddd02e5e860ddb
c4895a194b0722f455f90be3458e7600fc19a34b
57248 F20110217_AACOLR arguea_d_Page_100.pro
7c06ad597adb0e0cb992600b6e8c81f4
748a9a10bf106332b3348752b3f1eb4d9894dfce
28318 F20110217_AACOMH arguea_d_Page_117.pro
a8a494abc75e3670c00513a67f474120
9bd58e905ce6943c246d11c6416ee3cc960c1eb3
85225 F20110217_AACOLS arguea_d_Page_101.pro
22ed74b60328ac5575166d0f7ff9cdf2
c6c26f7b6a53211f70b2be6a62f44718fb095bc0
24800 F20110217_AACOMI arguea_d_Page_118.pro
5c8ea36a0f46b29bd2f3c9ee9c0e267c
fd0a53b11017af80039a9f9569fc20d8f12ef933
50289 F20110217_AACOLT arguea_d_Page_102.pro
924c7521c7597ba430df246607cb7c24
60b934d3127dc2e3e0b6aa67b69cff54b935690e
28733 F20110217_AACOMJ arguea_d_Page_119.pro
b52baa76bb7a6433c14e5629b5e88a33
99f694327a8de7945852349ffe322dfcfe92e783
97090 F20110217_AACOLU arguea_d_Page_103.pro
f20b4fa5b4c8be4bf7d7ea2d2c05b658
7504de8b164debf7b1e9ef5cadd8004c6e375060
25024 F20110217_AACOMK arguea_d_Page_120.pro
45f807e358156bd0c8cf2262a0188128
f3e02a26fa8a3895d949b83eb3fd3c594e3d8ee5
88475 F20110217_AACOLV arguea_d_Page_104.pro
1aa0f3a52ae5b84c3716c69e150180e7
d24d1dd49e5daa3b7da8fab17cef43b8b0179a74
25804 F20110217_AACOML arguea_d_Page_121.pro
ed95d356639052af288fc4d99de47657
2789cd399c36ebde5d4255c052bc6d341ac34806
112564 F20110217_AACOLW arguea_d_Page_105.pro
f0411f43a6e50c7109408259aa3f1e6b
e34c40387d2aa3804def8705374569d5eb42e104
11453 F20110217_AACOMM arguea_d_Page_122.pro
7931182bf0f44655495816d633802edb
d649b36a42ebcd6cdc353b27a367157d11ea1d30
66698 F20110217_AACOLX arguea_d_Page_106.pro
660ff50dde1ea845bd8e854e8275142d
94fafffffdf8d7eeb559998e0e5cdcd92aff2af4
8435 F20110217_AACONA arguea_d_Page_007.QC.jpg
f45fc4a8d6a8cb2c64374e8a680ed077
b8302dfe43b4e79fdaa4703ebb90964cf876e4db
57251 F20110217_AACOLY arguea_d_Page_107.pro
68358739008154047d1f374c10312cb7
4c9c2aa297b5de556b99f468b0a5f1c69eed04f7
82614 F20110217_AACONB arguea_d_Page_008.jpg
0365efe13d292c85d3ac7ad68f33c719
876a256cb1af1cf7c667054e5494ce18108af7a5
88923 F20110217_AACOLZ arguea_d_Page_108.pro
94e39f69400b1b066110eb75536c647e
4532a0e285977f125bf118af820ad0501525c4ed
24518 F20110217_AACONC arguea_d_Page_008.QC.jpg
837cec10001605fc010dadc6ac5525d1
882e24badcbb3112728de3272a12cb833265af19
8424 F20110217_AACOMN arguea_d_Page_123.pro
974d22f3d286226e8ef1663683f645ef
74edab3b3387d9025289681f5fccd35a2dd1596e
26150 F20110217_AACOND arguea_d_Page_009.QC.jpg
9621622310d5ae262839acc9abd041fb
aecbbce249ebf59e83011bc5b7d2342366831e6c
32777 F20110217_AACOMO arguea_d_Page_126.pro
99059a6314cd21ecaf81303ec2f09e29
4073e977eb36abade544a79ff597aece8fb7c8e0
15755 F20110217_AACONE arguea_d_Page_010.QC.jpg
11cde8c8b0598df1b7eb027206ee7ec4
78a08d1d0065c1eeefb3d2aa6d39ed77478c2839
38191 F20110217_AACOMP arguea_d_Page_127.pro
069527affedc8ba6f99e243316e928c4
4ad06493ae490b4ac977fb0fd06e3e9183cb80de
87155 F20110217_AACONF arguea_d_Page_011.jpg
16fa1753a589056757d88e2b73a97c9f
e624e88564dca5757892f1657ee76449cacd4fdf
27015 F20110217_AACOMQ arguea_d_Page_001.jpg
7b8d48dcb25f206d5c41c40eef0a1b72
b2a1ebf36f56746049f79033d499cf42f159d5f9
27701 F20110217_AACONG arguea_d_Page_011.QC.jpg
b6bef2ad3181d1ac8a3b1376dd5a6b30
a919ad786f838ea3869f90d1e01261dc2ab87447
8402 F20110217_AACOMR arguea_d_Page_001.QC.jpg
9657941dee91d33a4e557d6280665896
4e4b580131da6f691cabc940e658e60c240d6254
98333 F20110217_AACONH arguea_d_Page_012.jpg
30b28248d85eb98ea48923ca8df83277
9ac25f4d52dc2d3944b0605dee1bb130dc75a472
5489 F20110217_AACOMS arguea_d_Page_002.jpg
549b08e8af720c1617e93908f8ce8260
c7b01e5e9f7bb1e3ca5b148d8a3382fef73abfb3
31550 F20110217_AACONI arguea_d_Page_012.QC.jpg
faa947679c2ee3c13c4e7889d903f0e3
845a18aa4be4f499499c50ffe3d9a908cabf6401
1406 F20110217_AACOMT arguea_d_Page_002.QC.jpg
5395d16f7aac67843faca3e2e1f742e4
50524069b5cc22132208e59d10e55b9f809fba75
97335 F20110217_AACONJ arguea_d_Page_013.jpg
8fc6fbd8625088fb021029b4ebde954e
e889b9863752dfc5dd061654a383ae7814909fb4
64520 F20110217_AACOMU arguea_d_Page_003.jpg
8483e15ded477b39dd015cb33825ffac
b525d15e6a34748948d664b04b77c235316da5d6
32164 F20110217_AACONK arguea_d_Page_013.QC.jpg
443e924bf38ae35e91e54db4be3f234c
f513b5e20a4abaaa09d08095e03d41b4a850b18e
20240 F20110217_AACOMV arguea_d_Page_003.QC.jpg
da80cf65b1d5f2b28aadd3efc3ad1e46
37c6edb61508e4a2dc2766706906773965b255f2
55628 F20110217_AACONL arguea_d_Page_014.jpg
586aecdfaece719888285a9e7ce8a9bb
c2ee193828320cc52ab474b9c02e759ab7bde822
81944 F20110217_AACOMW arguea_d_Page_004.jpg
87700e990780009060e996712671b6e8
911275f1489677dea4cac815c43611331368c6ff
33283 F20110217_AACOOA arguea_d_Page_025.QC.jpg
7fb08a1fb49ed8e3375433c7ce2f3bbd
f1ba87bd8cad268e466fef18d2606111f0f580a0
18381 F20110217_AACONM arguea_d_Page_014.QC.jpg
16608872a0bc0cf158809b79ba6bd9b5
8c6fcd47439e0e70217ee0c6c4b5052150f343d9
40561 F20110217_AACOMX arguea_d_Page_006.jpg
b2072152655a795104311af4e6ead052
cd1d1879510a34141984924514fc1c85e3f4b5c8
97476 F20110217_AACOOB arguea_d_Page_026.jpg
97491d76f26f5cd8be59a0f84d5b3b19
499055022eca676f475655244ae422da649ae1e1
29115 F20110217_AACONN arguea_d_Page_015.QC.jpg
a47d60e889caa196cb4acb719734667c
568c836b921da83ed8452ee042fddc665205e36e
11269 F20110217_AACOMY arguea_d_Page_006.QC.jpg
a90383e02e8b4af3ff48c25e47d3f3f9
c7e971d47b275e321f1f68a22ee756f910657144
31412 F20110217_AACOOC arguea_d_Page_026.QC.jpg
2525d18796fdc30305a83c2d3022cae1
fe52e98bd31112466797e4156198c037efcace86
29908 F20110217_AACOMZ arguea_d_Page_007.jpg
bc86315517609a8867f6ca9668eecde0
0207c8291dcad4811638154a490753c37ebbb6ae
103813 F20110217_AACOOD arguea_d_Page_027.jpg
956f4fe61ecfed1c702829c3e848f514
f5af7926f2b99ae2c928a2794c8be6854b5ba847
31362 F20110217_AACONO arguea_d_Page_017.QC.jpg
50fafb35e64622199ec01c11d1eb3ec4
2a347069f93b88ce237e183a583c46b1a69aec69
105360 F20110217_AACOOE arguea_d_Page_028.jpg
aecedcb9a70ee84162373942b52e39e5
cb7e34df3137875bcb0c3c2f3a09b6603922164b
97474 F20110217_AACONP arguea_d_Page_018.jpg
0db94f0952ccbf284e9037f635c5949f
cad80dcda2744294e66bbef0b719e8c27d978db8
35215 F20110217_AACOOF arguea_d_Page_028.QC.jpg
04ec019a846f69f86ca260fde6e9b4d9
665645979ca2c4baa3b6cd4308489cfbc22c35ad
85053 F20110217_AACONQ arguea_d_Page_019.jpg
5b2331a8fd90e68464d40c6d438ffef9
3a701284eef4d0950a4ce905df52da2866e34ed8
93254 F20110217_AACOOG arguea_d_Page_029.jpg
77b5ecfbb05de3371b10981f3a2cbca2
6706989f58644c0a71e4e1190c240b6f4158ac44
27693 F20110217_AACONR arguea_d_Page_019.QC.jpg
5593ad23c36a38a8d6098dbb609dfb8b
b041b0db8abd66f672e0c0ce4499770f780d1781
35335 F20110217_AACOOH arguea_d_Page_030.QC.jpg
19be6de6ebb39d5b3a8bce51aaff6e24
ed6801d28c3679e322f99cc116563b840034ef4a
106484 F20110217_AACONS arguea_d_Page_020.jpg
81dbc87cd1b898dcad0b795b94fd37e7
5862d56bd1fe24150ac03804c4509ab9d6a81f1c
99097 F20110217_AACOOI arguea_d_Page_031.jpg
5cf7eee85571b99cea38e9377db871a7
eba36169791d325bed9abe7db82ef8a71569b848
34614 F20110217_AACONT arguea_d_Page_020.QC.jpg
4ec19705ee0bbb27d095976401f510af
0ee33e948de610502eef7fb5ad0004f4c64d6f3e
32948 F20110217_AACOOJ arguea_d_Page_031.QC.jpg
38d42ca7d39037fd852135eea96c5286
08dc91a51de465b2405ad27adb14fca36a97bee3
27623 F20110217_AACONU arguea_d_Page_021.QC.jpg
3cccb7c11097ea3402290e883661596b
5015a560c2f2ca36ffe19970451c9f8091bcc5c8
88824 F20110217_AACOOK arguea_d_Page_032.jpg
286282f6524ab062faa5a600de307f68
c318bb5916dedabe15b0ae2d3e89508c9acb3dcc
83465 F20110217_AACONV arguea_d_Page_022.jpg
04e4cdb0c7479748a35e796a4e85d815
11f238b3fae2dc150d63a5da77ddcc0d4287c0ef
29059 F20110217_AACOOL arguea_d_Page_032.QC.jpg
794c29a221945ba4d5385fece7cb3411
e11a768ccc960fff9091d7cdf3a03ca9c8fce6ef
91657 F20110217_AACONW arguea_d_Page_023.jpg
e62a30be47b8c4e55b8ca516048251e9
caeb1e02653e26dd370be24bc0a39ca8aba7a99a
41227 F20110217_AACOOM arguea_d_Page_033.jpg
da54a5a7ec270a7b97ea6d1b001679d4
63177ae017cc2b86763dc9f48302a29029a6aa69
29124 F20110217_AACONX arguea_d_Page_023.QC.jpg
f08a21f1b6f7c01e718fcfd22e5dee06
5bffcc5118524e54bfc6721c0a80d30151b0f265
57739 F20110217_AACOPA arguea_d_Page_042.jpg
e56ce18a4f3a92b574c61165ad4dbfa4
3e6c13319ec996d3cb7e88ed04743bd393395645
13717 F20110217_AACOON arguea_d_Page_033.QC.jpg
4bd43d066e5d72cfb1dc4d1833bcb4e9
e71e2535a667676910b8bc415233b62d99afd52d
34134 F20110217_AACONY arguea_d_Page_024.QC.jpg
a26cdf7598c6e9587edd80b591e49cc9
67908c3fd204325c59cfd5cd9aaa95f72f71c582
19026 F20110217_AACOPB arguea_d_Page_042.QC.jpg
6160bbbfdcffb8fb26372633818d7d9d
e4b8e9de147668437f7ab2e5ff1ca73542474bd5
92083 F20110217_AACOOO arguea_d_Page_034.jpg
35de1e08430c7310f5910556daaf1ad4
94c418e2340197b9eba7e8e3cf9e634c5c2fc58f
103771 F20110217_AACONZ arguea_d_Page_025.jpg
c21dcbd078603498dac2b59a5db1c15e
1a0e530583601b10783129328befded7c56f0c93
70367 F20110217_AACOPC arguea_d_Page_043.jpg
369dab1969f7809c43215539d01fa98a
bf21f496fce77c0d3f8d8e6ef1acc6b3f33e2009
22909 F20110217_AACOPD arguea_d_Page_043.QC.jpg
8ffcdf458274ca1db26a6897e0924433
725da90f97039061b8412ac18687ae49c5e3d7b4
29552 F20110217_AACOOP arguea_d_Page_034.QC.jpg
5b766dc8d40fea802c4808477c1f7f67
2886f7458d762f830686b7941ea950c02b1df19a
78417 F20110217_AACOPE arguea_d_Page_045.jpg
c1b1191c1577139df0cae5fcb2db26ba
c58557484048652821a9b4a25a84c9349f547c42
29822 F20110217_AACOOQ arguea_d_Page_035.QC.jpg
7109b311cf3643a9840d4ba0692f4dd6
616df8356ee4885937ce3848d94554a45c7b0443
26326 F20110217_AACOPF arguea_d_Page_045.QC.jpg
d9b9fae61792cd8221664943a08c48fb
495248e1f712843bb456d9364a4d7743fede63e3


Permanent Link: http://ufdc.ufl.edu/UFE0014662/00001

Material Information

Title: Simulation-Based Approach to Estimate the Capacity of a Temporary Freeway Work Zone Lane Closure
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0014662:00001

Permanent Link: http://ufdc.ufl.edu/UFE0014662/00001

Material Information

Title: Simulation-Based Approach to Estimate the Capacity of a Temporary Freeway Work Zone Lane Closure
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0014662:00001


This item has the following downloads:


Full Text












SIMULATION-BASED APPROACH TO ESTIMATE THE CAPACITY
OF A TEMPORARY FREEWAY WORK ZONE LANE CLOSURE

















By

DIEGO F. ARGUEA


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF ENGINEERING

UNIVERSITY OF FLORIDA


2006
































This document is dedicated to my parents and sister















ACKNOWLEDGMENTS

This section is written in appreciation of all those involved in the research process

and the writing of this document. I thank the University of Florida and the Department of

Civil and Coastal Engineering for the opportunity to participate in a Transportation

Project and produce unique research. I thank my committee, comprised of Dr. Lily

Elefteriadou, Associate Professor, Committee Chair, and primary advisor; Dr. Scott

Washburn, Associate Professor; and Dr. Sivaramakrishnan Srinivasan, Assistant

Professor. I thank them for the advice, guidance, and feedback throughout the research

and writing of the report. I would like also to thank McTrans and those involved with

software development. I thank them for providing insight into the details of the problems

and solutions within the algorithms, fundamental to the effective use of the software

packages. I thank the group of master's and doctoral candidates that provided me with

the technical support and guidance when needed. Finally, I thank my friends, family, and

loved ones for the emotional support and encouragement throughout this endeavor.
















TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ................................................................................................. iii

L IST O F T A B L E S ............................................................................................................ vii

L IST O F FIG U R E S ......................................................................................................... viii

A B ST R A C T ....................................................................................................................... ix

CHAPTER

1 IN TR O D U CTIO N .................................................................................................. 1

B background .............................................................................................................
Problem Statem ent.................................................................................................. 3
R research O objective ................................................................................................. 3

2 LITERATURE REVIEW ....................................... ............................................. 5

Work Zone Capacity in the Highway Capacity Manual (HCM2000).......................5
Current FDOT Methodology...................................... ......................................... 7
Work Zone Capacity in the Literature ................................................. .............. 8
Work Zone Analysis Software............................................... ............................ 13
Early and Late-Merge Maneuvers Upstream of a Work Zone .................................14
Other Freeway Work Zone Literature .................................................................... 18
Safety ..................................................................................................... ........... 18
Traffic D version ........................................................................................... 19
Delay and Queuing ....................................................................................20
Summary and Conclusions ...................................................................................22

3 METHODOLOGY ...............................................................................................24

Sim ulator Selection............................................................................................... 25
Challenges with Previous Versions of CORSIM/FRESIM.......................................26
Resolved Challenges with Current Versions of CORSIM/FRESIM........................26
Modeling of Work Zones with CORSIM 5.1 .............................................................28
Operating Conditions..................................................................................28
Simulation of a Work Zone .............................................................................29
Lane Closure Location ........................................ .......................................29









Results of Preliminary Analyses .....................................................................33
Sim ulation Scenarios ............................................................................................ 33
Simulated Test Section................................................................................33
Input V ariables .............................................................................................. 34
Simulated Test Section Setup-Input Fixed Values .........................................37
Required Number of Simulation Runs ............................................................39
Total Number of Simulation Scenarios ..........................................................39
O utput V values ....................................................................................................... 40

4 MODEL DEVELOPMENT................................................................................41

D ata A nalysis........................................................................................................ 4 1
2-to-1 Lane Closure Model Development...................................................42
3-to-2 Lane Closure Model Development...................................................54
3-to-1 Lane Closure Model Development...................................................54
Final M models ......................................................................................................... 54
Variable Explanations and Example of Model Usage.......................................55
Capacity Estimation Models for each Lane Closure Configuration..................58
2-to-1 lane closure configuration .............................................................58
3-to-2 lane closure configuration .............................................................59
3-to-1 lane closure configuration .............................................................60
D discussion of Results .............................................................. ....................61
Effects of Model Variables on Capacity ..............................................62
Model Application by the FDOT ............................................................65

5 CONCLUSIONS AND RECOMMENDATIONS .............................................68

C onclusions...........................................................................................................68
R ecom m endations................................................................................................. 70
C alibration ..................................................................................................... 70
Future Research and Applications....................................................................71

APPENDIX

A MODEL DEVELOPMENT RELATIONSHIPS FOR A 3-to-2 LANE CLOSURE
CO N FIG U RA TIO N .............................................................................................74

B MODEL DEVELOPMENT RELATIONSHIPS FOR A 3-TO-1 LANE
CLOSURE CONFIGURATION................................... ........................................81

C SAMPLE OUTPUT FILES FROM CORSIM 5.1 AND CORSIM 6.0 ..................... 89

Sample Output from CORSIM 5.1 ...................................................................................89
Sample Output from CORSIM 6.0 ............................................................................ 96









D STATISTICS OUTPUT SCREENSHOTS FOR EACH LANE CLOSURE
C O N FIG U R A TIO N .................................................................................................105

2-to-1 Lane Closure Configuration..................................................................105
3-to-2 Lane Closure Configuration..................................................................106
3-to-1 Lane Closure Configuration..................................................................106

E MODEL USAGE EXAMPLE: SAMPLE CAPACITY CALCULATIONS FOR
EACH LANE CLOSURE CONFIGURATION..................................................107

F TRAFVU SCREENSHOTS FOR EACH LANE CLOSURE
CO N FIG U RA TIO N ........................................................................................... 112

LIST O F REFEREN CES .................................................................................................114

BIOGRAPHICAL SKETCH ..................................................................................... 117
















LIST OF TABLES

Table page

3 1. Delay Values for Combinations of Lane Closures and Lane Distributions (Ten
Sim ulation R uns)................................................................................................30

3 2. Average Speeds per Vehicle (Ten Simulation Runs) ..................................... ..31

3 3. Average Speed Values for Different Combinations of Lane Closures and Lane
D distributions ....................................................................................................... 32

3 4. Effects of Rubbernecking Factor on Capacity through Work Zone Lane Closure ..37

3 5. Variation of Input Parameters............................... ................. ........................... 40
















LIST OF FIGURES


Figure page

3 1. Sketch of the freeway network used in data collection..........................................33

3 2. Relationship between work zone flow and work zone length................................35

4 1. Relationship between work zone capacity and upstream warning sign distance .....43

4 2. Relationship between work zone capacity and truck presence in traffic stream......44

4 3. Relationship between work zone capacity and truck presence in traffic stream......44

4 4. Relationship between work zone capacity and lane changes in link (6,7) ...............45

4 5. Relationship between the number of lane changes in link (6,7) and the length of
link (6,7) .......................................................................................................... .46

4 6. Relationship between work zone capacity and the average speed per vehicle in
lanes one and tw o of link (5,6)................................... ..........................................47

4 7. Relationship between work zone capacity and the average speed per vehicle in
lanes one and two of link (6,7)..................................................... ....................48

4 8. Relationship between work zone capacity and the vehicular distributions on lane
one of all links ....................................................................................................49

4 9. Relationship between vehicular lane distributions in lanes one and two of link
(6,7) and the location of the upstream warning sign ...................................... ..50

4 10. Relationship between work zone capacity and the interaction of lane
distributions in link (6,7) and upstream sign distance ......................................... 51

4 11. Relationship between the work zone capacity and the interaction of the speeds
in lane 1 of link (6,7) and the location of the upstream warning sign ...................52

4 12. Relationship between the speeds in lane 1 and lane 2 of link (5,6)......................53

4 13. Relationship between the speeds in lane 1 and lane 2 of link (6,7)......................53















Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Engineering

SIMULATION-BASED APPROACH TO ESTIMATE THE CAPACITY
OF A TEMPORARY FREEWAY WORK ZONE LANE CLOSURE

By

Diego F. Arguea

August 2006

Chair: Lily Elefteriadou
Major Department: Civil and Coastal Engineering

The Florida Department of Transportation (FDOT) is interested in updating its

methodologies for estimating capacities on freeway work zones in Florida. The current

methods have not been modified since 1995, and the FDOT is particularly interested in

new ways to facilitate the scheduling and managing of lane closures. This thesis

proposes new simulation-based models for estimating the capacity of a temporary

freeway work zone lane closure. Some of the factors considered in model development

include the location of the upstream warning sign, the presence of trucks, the presence of

law enforcement and/or heavy equipment, and the length of the work zone. In addition to

these inputs, the average speeds per vehicle and the vehicular lane distributions for

specific network links were considered in model development. A large matrix of

scenarios was created so that the effects of all combinations of factors could be observed.

Data were collected from simulation of these scenarios using the software package

CORSIM 5.1. Three lane closure configurations-2-to-1, 3-to-2, and 3-to---were









simulated and one model for estimating capacity was developed for each. All models for

each lane closure configuration consider the input factors named previously as well as

average speeds per vehicle and lane distributions of vehicles upstream of the work zone

lane closure. The final models show the effects of each of these factors on the throughput

capacity of a freeway lane closure. A higher fraction of vehicles in the to-be closed

lane(s) prior to the work zone leads to a significant decrease in capacity. Likewise,

higher speeds in the to-be closed lane(s) also lead to a capacity decrease. The result of

this simulation modeling offers valuable insights into the relative capacities under

different geometric configurations and traffic stream scenarios. Future research is

recommended to calibrate the models to actual field conditions.














CHAPTER 1
INTRODUCTION

This section presents a brief description of existing problems and current issues

facing transportation agencies. A background is given on specific problem areas and the

efforts attempted to increase efficient traffic stream flow through a work zone lane

closure. The problem statement is then provided, followed by a concise statement of

objectives for this research.

Background

Many state transportation agencies are experiencing growing congestion and traffic

delays in work zones on rural interstate highways. This congestion results in

unproductive and wasteful delays for both motorists and commercial vehicles. It also

creates hazardous conditions in which vehicles stopped in the queues are being

approached by vehicles upstream at very high speeds. The delays also result in driver

frustration, making some drivers willing to take unsafe risks in an effort to bypass delays.

The Florida Department of Transportation (FDOT) is currently interested in

updating its existing methodologies for estimating capacity values through a lane closure.

These capacity values through work zones are important so that queues and thus delays

can be accurately estimated as well. The level of operation of a facility can be assessed

from these queue lengths and delay values. The current methods have not been updated

since 1995, and the FDOT is particularly interested in an updated method that will

facilitate the scheduling and managing of short-term work zone lane closures on

freeways. The development of this updated capacity estimation procedure will form a









part of a decision matrix that the FDOT is developing to assist engineers and contractors

in selecting the proper tools to evaluate lane closures.

The need to maintain adequate traffic flow through short-term interstate work zones

is vital on today's heavily-traveled freeways. Numerous states have policies that provide

guidance for when short-term lane closures can be instituted. These policies are related

to maximum allowable traffic flows, vehicle delays, and queue lengths. Generally, these

threshold limits are defined on a state by state basis as a function of traffic stream

characteristics, highway geometry, work zone location, type of construction activities,

and work zone configuration (Sarasua, 2004).

Limited research by State Departments of Transportation-Nebraska and Indiana,

for example-has been conducted involving the identification and evaluation of

alternative strategies designed to control traffic speeds and merging operations in

advance of lane closures (McCoy et al., 1999). In addition, work has been done in the

fields of early merging and late merging strategies: models have been developed to

predict delays, queue lengths, and lane capacities using many rural interstate areas of the

United States as observation sites for data gathering (Beacher et al., 2005). The early

merge encourages vehicles to merge into the through lane at locations far upstream of the

lane closure. This can be achieved by signs or physical barriers. The late merge concept

is designed to encourage drivers to use all lanes approaching a lane closure and then

alternate their entry into the through lane, guided by static signs in addition to normal

work zone traffic control. Although some states have put these into practice, only a

handful of short-term field studies have formally evaluated their effectiveness. There is









little information available on when the early or late merge should be used, however, and

a limited understanding of the factors that influence their performance.

The FDOT requires an updated method that will facilitate the scheduling and

managing of the lane closures and considers additional operational factors. Some of the

same factors used in the current methodology will be considered as well as new factors

that may also have an effect on capacity reduction.

Problem Statement

The existing procedure used by the FDOT applies an obstruction factor based on

lateral clearance and travel lane width, and a work zone factor based on work zone length

to the base capacity to estimate a restricted capacity. The procedure was developed in

1995 and does not account for operating characteristics of the facility. It is also limited in

that the restricted capacities are estimated for 2, 4, and 6-lane two-way facilities that are

converted into one-way facilities. The updated capacity model will consider several

additional operating factors in addition to those considered in the current methodology.

Furthermore, the updated model will estimate restricted capacities for one-way freeway

facilities with lane reductions.

Research Objective

The objective of this research is to develop an analytical model to estimate the

capacity of a temporary freeway work zone based on various geometric and traffic

factors. One factor of particular interest is the vehicular lane distributions at different

distances upstream of the lane closure. The effect of lane distributions of vehicles

upstream of a temporary freeway work zone on the capacity of the work zone has not

been previously investigated. This relationship is important for selecting an optimal

traffic management strategy to implement in order to maximize traffic flow and









passenger safety through the work zone. Another new factor that will be considered is

the average travel speed of vehicles by lane, also upstream of the work zone. This factor

will serve to compare capacity values at different speeds, potentially leading to speed-

control strategies to maximize throughput. The lane distributions' and upstream speeds'

relationship to the work zone environment and early merge and late merge

implementation can begin to answer questions regarding which strategy may be preferred

for a given set of environmental and geometric conditions.

The models) developed will be based on the work zone environment and

geometry, the percentage of large trucks present in the traffic stream, and the presence of

other conditions that may affect capacity through the lane closure. The relationships

between the lane distributions and performance measures through the lane closure will

also be developed to enhance the traffic management strategy selection process.














CHAPTER 2
LITERATURE REVIEW

An extensive literature review was conducted to identify and evaluate existing

research involving freeway work zone lane closures. Specific focus was given to

capacity models developed for estimating vehicular flow through said lane closures. This

chapter presents several angles of work zone capacity research ranging from existing

capacity models to their implementation as part of different types of traffic management

strategies. The first section discusses the treatment of work zone capacity in the

Highway Capacity Manual (HCM 2000). The next section presents a review of the

current FDOT methodology and its limitations, followed by a review of the literature on

capacity and its definition for work zones. The fourth section reviews the software

available for work zone analysis, since many computer models have used capacity as a

key input parameter to help quantify queue length and delay and to calculate delay costs.

Next, literature on freeway merging and general traffic management strategies is

reviewed. Then, a section is presented outlining previous research on queuing and delay

estimation; both being important in identifying additional factors that may affect

capacity. The last section includes a brief summary of the findings and recommendations

from the literature.

Work Zone Capacity in the Highway Capacity Manual (HCM2000)

The HCM 2000 defines capacity as "the maximum sustainable flow rate at which

vehicles or persons reasonably can be expected to traverse a point or uniform segment of

a lane or roadway during a specified time period under given roadway, geometric, traffic,









environmental, and control conditions; usually expressed as vehicles per hour, passenger

cars per hour, or persons per hour." The HCM 2000 (Chapter 22, Freeway Facilities)

recommends that a value of 1600 pc/h/ln be used as the base capacity value for short-

term freeway work zones, regardless of the lane closure configuration. It is stated that

this base value may be higher or lower when adjustments are applied in accordance to the

specific work zone's prevailing conditions. The intensity of work activity-characterized

by the number of workers, types of machinery, and proximity of travel lanes to work

under way-can have an effect on the capacity, increasing or reducing the base value by

up to ten percent. Also, the HCM 2000 states that the effect of heavy vehicles should be

considered, as truck presence leads to reduction of capacity. Another element reducing

the base capacity value is the presence of ramps. The HCM 2000 recommends that to

minimize the impact of ramp presence on capacity, ramps should be located at least 1,500

ft. upstream from the beginning of the full closure. If that cannot be done, and the ramp

is within the taper or the work zone itself, then either the ramp volume should be added to

the mainline volume to be served, or the capacity of the work zone should be decreased

by the ramp volume (up to a maximum of half of the capacity of one lane). The HCM

2000 provides the following equation (Equation 22-2, HCM 2000) for estimating

capacity at work zones, which considers reductions due to the three elements discussed

above:

ca = (1,600 + I R) fHv N (Eq. 2-1)

where

ca = adjusted mainline capacity (veh/h)

fHv = adjustment for heavy vehicles; defined in HCM Equation 22-1









I = adjustment factor for type, intensity, and location of the work activity (ranges

from -10% to +10% of base capacity, or -160 to +160 pc/h/ln)

R = adjustment for ramps, as described in the preceding paragraph

N = number of lanes open through the short-term work zone

An additional factor discussed in the HCM 2000, which would decrease capacity

and can be considered, is the lane width. It is stated that capacity may decrease by 9-14%

for lane widths of 10-11 ft. Note that this factor is not included in the capacity estimation

equation, nor does the HCM discuss potential interactions between the various factors

affecting capacity.

Current FDOT Methodology

The Florida Department of Transportation is interested in updating its methodology

for estimating restricted capacity through a temporary work zone lane closure. Their

procedure was developed in 1995 and does not consider operating characteristics of the

traffic stream in its reduction estimate. Rather, geometric conditions form the basis of the

method. The procedure is limited to the following lane reduction configurations:

* 2-lane, 2-way facility converted to 2-way, 1-lane
* 4-lane, 2-way facility converted to 1-way, 1-lane
* 6-lane, 2-way facility converted to 1-way, 2-lane


The base capacities, respectively, for the three configurations listed above, are

1400, 1800, and 3600 vehicles per hour. Capacity reduction factors are then applied to

these base values so that an estimate of restricted capacity may be obtained. The

obstruction factor is obtained from a table and is based on the width of the travel lane and

the lateral clearance to the travel lane. A lateral clearance of 6 feet and a lane width of 12

feet results in a reduction factor of 1.00, or no reduction. A lateral clearance of 0 feet and









a lane width of 9 feet results in a maximum reduction factor of 0.65. The other reduction

factor considered in the method is a work zone factor that is also obtained from a table.

This reduction factor is based on the length of the work zone and ranges from 0.98 to

0.50 for work zone lengths of 200 feet through 6000 feet, respectively.

Work Zone Capacity in the Literature

There are several articles in the literature on lane-closures in freeway work zones.

Krammes and Lopez (1994) presented recommendations on estimating the capacities of

short-term freeway work zone lane closures. Their research served as the basis for the

HCM 2000 methodology. The study consisted of analyzing lane closures in Texas

between 1987 and 1991. The data collected represent over 45 hours of capacity counts at

33 different freeway work zones with short-term lane closures. Five different lane

closure configurations were analyzed, and data were only used from time periods during

which traffic was queued in all lanes upstream of the work zone area. Capacity counts

were taken only at the upstream end of the activity area (i.e., the beginning of the

bottleneck). The results of their study showed an average short-term work-zone lane

closure capacity value of 1600 pcphpl, and it was recommended that this value be used as

the starting base value when analyzing these freeway segments. It was also recommended

that this value be adjusted for the effects of heavy vehicle presence, intensity of the work

zone, and the presence of entrance or exit ramps near the beginning of the lane closure.

The following equation, Equation 2-2, estimates capacity in a lane closure, taking into

consideration the effects of work zone activity intensity, number of open lanes, and the

presence of ramps and heavy vehicle in the traffic stream.









C =(1600 + I R) H N (Eq. 2-2)

where

C = estimated work zone capacity (vph)

I = adjustment for type and intensity of work activity (pcphpl) suggested in the

research

R = adjustment for presence of ramps (pcphpl) suggested in the research

H = heavy vehicle adjustment factor given in the HCM

N= number of lanes open through the work zone

Research by Maze et al. (1999) evaluated traffic flow behavior at rural interstate

highway work zones, and estimated the traffic carrying capacity of work zone lane

closures. Traffic performance data were collected at an Iowa interstate highway work

zone using data collection trailers, constructed exclusively for this project. The trailers

use a pneumatic mast to hoist video cameras 30 feet above the pavement's surface where

the cameras collected video of traffic operations. Traffic performance data were

collected at one work zone on Interstate Highway 80 where two lanes are reduced to one

lane. Through analysis of these data, a work zone lane closure capacity from 1,374 to

1,630 passenger cars per hour was estimated.

Additional research was completed by Maze et al. (2000) considering the capacities

of work zones in rural Iowa. The paper discusses the procedure for developing an

estimate for vehicular capacity through rural interstate work zones in Iowa. The

following field data were collected during the summer of 1998 on Interstate Highway 80

between U.S. 61 and Interstate Highway 74:

* Traffic flow characteristics-speed, density, and volume-at the end of the lane
closure taper









* Traffic flow characteristics upstream from the lane closure (500 feet)

* The length of the queues throughout congested conditions. This is a measure of
storage and the difference in queue length from one time interval to the next is the
speed that the queue grows or is discharged.

One aspect of particular interest to the research was the observation of the rate at

which the queue increases or decreases. Field observation found that backward moving

queues were forming at speeds as high as 40 mph. With oncoming, unsuspecting traffic

arriving at 65 to 70 mph, this creates unsafe relative speeds of 100 mph, a problem for

rural Iowa's interstate traffic. It was concluded in the report that the capacities in rural

Iowa for work zone lane closures varied from 1,400 to 1,600 passenger cars. This

capacity estimation assumed a passenger car equivalency (PCE) value of 1.5 for heavy

vehicles.

Kim et al. (2001) conducted further research on the capacity of work zones. The

study objectives were to investigate various factors that contribute to capacity reduction

in work zones and to suggest a new methodology to estimate the work zone capacity.

The new capacity estimation model is based on traffic and geometric data collected at 12

freeway work zone sites with four lanes in one direction. Traffic data were collected

mainly after the peak hour during daylight and night (Maryland State Highway

Administration (SHA) has a policy that lanes cannot be closed during the peak-hour.)

Multiple-regression analysis was used to develop a model to predict work zone capacity

as a function of several key independent factors such as the number of closed lanes, the

proportion of heavy vehicles, grade, and the intensity of work activity. The proposed

model was compared with other existing capacity models, including the Krammes and

Lopez model discussed above, and showed improved performance for all of the

validation data. The following equation estimates capacity through a lane closure, and









considers additional factors such as lateral distance to the open travel lanes, work zone

length, and the location of the closed lanes (left or right or even middle).

Capacity = 1857 168.1 NUMCL 37.0 LOCCL 9.0 HV

+ 92.7 LD 34.3 WL 106.1 WIH 2.3*WG HV (Eq. 2-3)

where

NUMCL = Number of closed lanes

LOCCL = Location of closed lanes (which lanes are closed)

HV = Proportion of heavy vehicles

LD = Lateral distance to the open travel lanes

WL = Work zone length

WIH = Intensity of heavy work zone activity

WG HV = Work zone grade Proportion of heavy vehicles

According to the above model, Kim et al. suggests that work zone length has an

effect on capacity in the following manner: a long work zone length will likely have more

intense work activity, thus reducing capacity. However, there is already a term in the

model, WIH, that considers work zone intensity. It is unclear then why there is an

individual term for work zone length, and not an interaction term with intensity.

Sarasua et al. (2004) conducted a study in South Carolina to determine the number

of vehicles per lane per hour that can pass through short-term, interstate work zone lane

closures, with minimum acceptable levels of delay. After review of other states' policies,

the methodology was developed based on a 12-month data collection period during 2001-

2002 from 22 work zone sites along South Carolina's interstate system. Heavy vehicles

were considered in the analysis, implementing the software Satflo2 to develop PCEs









based on recorded time headways. Sarasua's paper presents a summary of the data

collection procedures and data analysis methods, as well as the final form of the work

zone capacity model. The research recommended a base capacity value of 1460 pcphpl.

A report by Benekohal and Chitturi (2004) describes a methodology for estimating

both operating speeds and capacity at interstate work zones. Data were collected at 11

work zones in Illinois with time-coded video recording equipment. Headways, speeds,

and travel times were among the performance measures recorded. The following speed-

flow relationship was developed from the data to establish the lower part (congested part)

of the speed-flow curve:

q = 145.68 U 0.6857 (Eq. 2-4)

where:

q = flow in passenger cars per hour per lane (pcphpl)

U = speed in mph (input speed must be lower than the speed at capacity)

The free flow part of the curve is based on information from the HCM 2000 and on

field data collected in work zones. The authors state that the capacity model is based on

the principle that work zone operating factors (such as work intensity, lane width, lateral

clearance, etc.) cause reductions in the "operating speed". Operating speed in a work

zone is defined as the speed at which the vehicles would travel through the work activity

area after reducing their speed due to work intensity, lane width, lateral clearance, and

other factors. The adjusted capacity is estimated as follows:

Cadj = Cuo fHv PF (Eq. 2-5)

where

Cadj = adjusted capacity (vphpl)









Cuo = capacity at operating speed UO

fHv = heavy vehicle factor

PF = platooning factor (which accounts for the underutilization of available

capacity, and is a function of drivers' aggressiveness, traffic volume, and work

zone operations)



The model was validated for a two-to-one lane closure, but the authors

recommended additional data collection from work zones with different lane closure

configurations to further verify the validity of their methodology.

Work Zone Analysis Software

Most computer models, such as Queue and User Cost Evaluation of Work Zones

(QUEWZ), have used capacity as a key input parameter to help quantify queue length

and delay, and to calculate delay costs. Memmott and Dudek (1984) developed QUEWZ

to estimate user costs incurred due to lane closures. The software is designed to evaluate

work zones on freeways, but is also adaptable to different types of highways (Associated

Press, 1989). The model analyzes traffic flow through lane closures, and helps plan and

schedule freeway work-zone operations by estimating queue lengths and the additional

road user costs. The costs are calculated as a function of the capacity through work

zones, average speeds, delay through the lane closure section, queue delay, changes in

vehicle running costs and total user costs. Since its development, QUEWZ has

undergone two major modifications. One of these is the ability to determine acceptable

schedules for alternative lane closure configurations-crossover or partial lane closure-

based on motorist-specified maximum acceptable queue or delay. The second of these

improvements is the development of the algorithm that can consider natural road user









diversion away from the freeway work zone to a more desirable, unspecified, alternate

route (Associated Press, 1989).

Another popular software package is QuickZone 2.0, which was released in

February 2005 in its full version (Federal Highway Administration, 2000). This software

is an enhanced version of QuickZone, an Excel-based software tool for estimating queues

and delays in work zones. The maximum allowable queues and delays are calculated as

part of the procedure in optimizing a staging/phasing plan and developing a traffic

mitigation strategy. As a result, lane closure schedules are recommended to minimize

user costs. This is a quick and easy method, with a user-friendly, concise spreadsheet

setup. Within the software, however, the PCE factor is fixed at 2.3 for all heavy vehicles,

and the capacity of the work zone is fixed at 1200 pcphpl. This PCE value-2.3-is

higher than the value reported in the HCM for basic freeway segments (Chapter 23) for

level terrain, which is 1.5. This 1.5 value is the same one that is applied to the heavy

vehicle adjustment factor for short-term freeway work zones in Chapter 22. The capacity

value, fixed at 1200 pcphpl, is also quite conservative. As a result, delays estimated using

this software would typically be higher than those estimated using the HCM 2000

analysis.

Early and Late-Merge Maneuvers Upstream of a Work Zone

This section discusses types of merge strategies that have been developed to

improve work zone operations. Examples of such strategies include "early merge" and

"late merge". These can be implemented in the field using physical barriers or double-

lane markings, or even with the presence of a law enforcement vehicle. Variations of

these include the dynamic early merge (used in Indiana, known as the Indiana Lane

Merge) and dynamic late merge. The dynamic early merge is intended to provide









warning and merge signs at variable distances upstream of the back of the queue. The

distance is dependent upon the queue length, which is sensed by sonic detectors and

enforced with flashing do notpass signs. The dynamic late merge uses the late merge

strategy only when congestion is present, otherwise conventional merging is used. The

Nebraska Department of Roads (NDOR) refers to conventional merging as NDOR

Merging. Another merging strategy, called Zip merging, is primarily used in Europe and

was developed in the Netherlands. With this strategy, each driver does not change lanes

until a fixed distance from the lane closure, alternating between those in the through lane

and the closed lane. Technology has further allowed for improvements in merging and

work zone safety with the creation of "Smart" Work Zones. These are capable of

detecting congestion and providing real-time advisory information to travelers

encouraging them to divert to an alternate route. The remainder of this section discusses

literature related to the relationship between these strategies and capacity of the work

zone.

McCoy et al. (1999) identified twelve alternative strategies to control traffic speeds

and merging operations in advance of lane closures. Field evaluations of the NDOR

Merge and two alternatives, the Indiana Lane Merge and Late Merge were conducted.

Based on the data collected, a benefit-cost analysis showed the cost-effectiveness of four

alternative traffic control strategies relative to the NDOR Merge. The four alternatives

evaluated were: (1) the Indiana Lane Merge, (2) Late Merge, (3) Enhanced Late Merge,

and (4) "Smart" Work Zone. The NDOR Merge was found to be the most cost-effective

merge control strategy for directional average daily traffic values below 16,000 to 20,500

vehicles, depending on the percentage of trucks. The Late Merge, Enhanced Late Merge,









and "Smart" Work Zone were the most cost-effective alternatives at higher traffic

volumes.

An attempt to evaluate the effects that late mergers have on work zones is reported

by Maze and Kamyab (1999) in their Work Zone Simulation Model. During the summer

of 1998, traffic flow data were collected at merge areas of work zone lane closures on

freeways in rural Iowa. Using video image processing technology, the merge areas were

observed from the point of the flashing merge arrow board to the point where the

bottleneck begins-the site of construction. Virtual detectors were used to collect traffic

flow rates, speeds, and headways at the two ends of the merge area. Travel times were

also obtained by the noted vehicles' arrival and departure times. These data were used to

develop a microscopic simulation model specifically designed to examine the effects that

slow-moving vehicles and late mergers have on delay and average speed. The model was

developed for a work zone with a two-to-one configuration-two lanes reduced to a

single lane (Maze and Kamyab, 1999). The model can estimate delay, as well as the

length and dissipation time of the queue. The authors report that the length of the queue is

overestimated, because the model places 97 percent of vehicles in the through lane, rather

than distributing them more evenly over both through and merge lanes. For that reason,

queue length estimates are not included in the model and further data collection and

model enhancements are recommended before accurate queue length estimates can be

obtained.

In a study by Walters and Cooner (2001), it is reported that stress levels are

reduced in 50% of drivers when bottleneck and work zone improvements are made.

Researchers tested the late merge concept, originally developed in Pennsylvania, at a









work zone on Interstate 30 in Dallas, Texas. The report indicates that the Late Merge

concept is feasible on an urban freeway where three lanes are reduced to two (Walters

and Cooner, 2001). Further testing of this concept and other innovative merge strategies

such as Early and Zip Merging is recommended to determine the most efficient, safe, and

least stressful method of encouraging merging at lane closures.

The late merge strategy was also assessed by Beacher et al. (2005) in a field test

conducted over several months. Conducted on a primary route in Tappahannock,

Virginia, a 2-to-i lane closure was analyzed and the results compared with those of

traditional work zone lane closure strategies. Although an increase in throughput was

observed, the increase was not statistically significant. Similarly, time in queue

decreased, but the decrease was not statistically significant (Beacher et al., 2005). The

report concludes that despite the lack of statistical significance, more drivers were present

in the closed lane, indicating a positive response to the late merge signs. The authors

indicate potential statistical biases (such as driver population and site-specific

characteristics) may have had error-inducing effects on the analysis. In conjunction

with the above field evaluation, the late merge concept was evaluated by comparing it to

traditional traffic control using a full factorial analysis. Results of the computer

simulations showed that the late merge produced a statistically significant increase in

throughput volume versus the traditional merge for the 3-to-i lane closure configuration

across all combinations of analysis factors. Although the 2-to-i and 3-to-2

configurations did not show significant improvement in throughput overall, it was found

that as the percentage of heavy vehicles increased, the late merge did foster higher

throughput volumes than traditional traffic control. The simulation results indicated that









the late merge may not provide as much of a benefit as previous studies had indicated,

and that application of the late merge may be more appropriate in situations where heavy

vehicles comprise more than 20 percent of the traffic stream (Beacher et al., 2005).

Other Freeway Work Zone Literature

This section summarizes literature review findings related to other aspects of work

zone analysis, including safety, traffic diversion, and delay and queuing estimation.

Safety

Generally, crash rates are higher in work zones than on stretches of highway under

normal operation, and there are several articles in the literature assessing safety around

work zones. For example, Pal and Sinha (1996) developed a model that systematically

selects appropriate lane closure strategies based on predicted crash rates. Each lane

closure strategy was evaluated through consideration of the additional travel time,

additional vehicle operating cost, safety, traffic control cost, and contractors' needs.

Opinion surveys of the subcontractors at each of the project sites were conducted which

identify four subcomponents involved in their perceived need: worker safety, equipment

safety, work productivity, and work quality. The data used were collected from 17

Interstate 4R projects in Indiana. Information obtained from the INDOT included type of

lane closure strategy used, duration of closure, length of section closed, and traffic data:

average daily traffic, hourly variation in volume, directional splits, vehicle mix, and

project costs. Also, the number of crashes was obtained for several years during the

construction activities at each site as well as for normal operating conditions at the same

sites. Pal and Sinha implemented the analytic hierarchy approach to synthesize the study

results. Computer software was developed that can be used to select an appropriate lane

closure strategy based upon the described parameters. The user-required inputs are work









zone length, traffic volume, duration of the project, crash rate under normal conditions,

and total project cost. The software applies regression models to estimate the user-travel

time and vehicle operating cost, traffic control cost, and expected number of crashes.

This procedure is recommended for selecting between a partial or crossover lane closure

with statistically sufficient accuracy (Pal and Sinha, 1996).

Traffic Diversion

Ullman (1996) explored how natural diversion affects traffic volumes at the exit

and entrance ramps upstream of temporary work zone lane closures on high volume,

urban Texas freeways. Data collection was scheduled to begin before the start time of the

lane closure and continued through the time when the lane closure was removed and the

queues on the freeway were completely dissipated. These field studies were limited to

urban freeways with frontage roads, and of primary interest was observation of traffic

operations at the two facilities before, during, and after the work activity. Data were

collected and studies constrained to within the midday off-peak period (9:00am to

4:00pm), as lane closures are prohibited by law during peak traffic periods in Texas. The

following performance measures were obtained from the data collection activities:



* Changes in volumes on the freeway, frontage road, and ramp volumes hour by hour
during lane closure

* Freeway and frontage road travel times

* Propagation of queuing on the freeway upstream of the lane closure over time

Ullman discusses further the concept of natural diversion as well as the

requirements for a motorist to make a conscious decision in avoiding the congestion. The

results of the study show that queue stabilization can occur because flow conditions









within the queue are not uniform and tend to change as a function of the distance from

the beginning of the lane closure bottleneck. Ullman indicates that these changes can be

explained by shock wave theory within a traffic stream, and shows that the stabilization

results are consistent with this theory. Thus significant amounts of diversion at

temporary closures can have extensive effects upstream of the bottleneck. This queue

stabilization results in lower user delay values. Then, additional costs of usage can be

estimated using "using regular input-output or shock-wave analysis based on historical

traffic volumes." Another important result is that these temporary lane closures do not

only affect the entrance and exit ramps immediately upstream of the closure, but can

extend significantly further than previous models have predicted. Ullman recommends

that the potential effects of diversion on alternative routes should be considered a

significant distance upstream of the temporary work zone (Ullman, 1996).

Delay and Queuing

A large part of selecting an appropriate traffic management strategy is work-zone

related traffic delay. A study conducted by Chien and Chowdhurry (2002) indicates that

delays are always underestimated when using deterministic queuing theory. Therefore,

despite the costs associated with many simulation runs, the report recommends

simulation as a viable alternative, when combined with queuing theory. The authors

developed a methodology that approximates delays by combining CORSIM simulation

data and deterministic queuing while considering various geometric conditions and time-

varying traffic distribution. The traffic flow distribution over time and the work zone

capacity are the two major inputs to the model. The queuing delay is then calculated

from the estimated queue lengths of the previous time period. Delay values from work

zone traffic operations on a segment of 1-80 in New Jersey were predicted using









deterministic queuing, CORSIM simulations, and the proposed model. Because the

model is dependent on the accuracy of the CORSIM delay curve, extensive calibration

and validation of CORSIM may be required.

Ullman and Dudek (2003) describe a new theoretical method that more accurately

predicts the lengths of queues that develop under a temporary work zone lane closure.

The authors state that the queues and delays that develop upstream of closures in urban

areas are much shorter than those estimated using historical traffic volume data. Rather

than propagating, these queues often stabilize upstream over the duration of the lane

closure (Ullman and Dudek, 2003). The new formulation is based on a traditional

macroscopic perspective of traffic flow on a section in which flow, speed, and density are

known. A new, permeable pipe analogy is presented to represent the work zone's

creation of a stimulus for diversion. The mathematical components of the model include

the following in its algorithm:

* A shock wave theory to model the propagation of the traffic queue

* An energy model of traffic flow that illustrates the reduction in speed and its effect
on natural diversion tendencies

* A mathematical analogy of urban roadway section as fluid flow through a section
of permeable pipe

This macroscopic model predicts queue stabilization at some point, so

overestimation of queue lengths does not occur. However, Ullman and Dudek

recommend that more work is required to further comprehend the stimuli that affect

permeability of a corridor, and to develop a model that can estimate what this level of

permeability may be for a given set of conditions.

Chitturi and Benekohal (2005) performed a study on the effects of narrowing lanes

and reduced lateral clearances on the free-flow speeds (FFS) of cars and heavy vehicles









in work zone areas. The findings report that the reductions in FFS of vehicles in work

zones due to narrow lanes are higher than the reductions given in the HCM for normal

freeway sections, although the reduction due to narrow lateral clearance was comparable.

Because of the wider dimensions of heavy vehicles, the reduction in FFS of heavy

vehicles is greater than that of passenger cars. As a result, heavy vehicles are affected

more adversely than passenger cars, and it is recommended that the speed reductions due

to narrow lanes should take into account the percentage of heavy vehicles in the traffic

stream. The reductions for passenger cars and heavy vehicles have not been quantified

separately because of the limited data for heavy vehicles. Until such data become

available, it is recommended that 10, 7, 4.4 and 2.1 mph be used for speed reduction in

work zones for lane widths of 10, 10.5, 11 and 11.5 ft respectively (Chitturi and

Benekohal, 2005).

Summary and Conclusions

A review of the literature illustrates many ways of developing a model that

estimates capacity through a temporary work zone. No two procedures are alike,

differing in the ways that data are collected and analyzed as well as in the selection of

factors that affect capacity reduction. The following is a summary of those work-zone

capacity-reducing factors that have been included in existing models:

* number of closed lanes
* heavy vehicle presence
* grade of roadway segment
* intensity of work activity
* merge strategies such as late merge and early merge
* lane widths
* presence and location of ramps
* proximity of travel lanes to work zone activity









Work zone capacity base values obtained around the country have varied since the

introduction of Krammes and Lopez's Texas-based recommendation of 1600 pcphpl

(which is also used in the HCM 2000). The Iowa-based study by Maze produced a model

that recommended base values ranging from 1374 to 1630 pcphpl, depending on the

location within the state. Sarasua's model estimates a value of 1460 pcphpl for South

Carolina, and the QuickZone 2.0 software implements a conservative 1200 pcphpl in its

analyses. The current FDOT procedure only considers geometric factors in its capacity

reduction model and should be updated with factors that consider operational

characteristics of the traffic stream.














CHAPTER 3
METHODOLOGY

At the onset of this research, the goal was to locate 4 different freeway segments

with temporary lane closures. Two of these were to be lane closures on two-lane

segments reduced to one lane and the remaining two were to be three-lane segments

reduced to two lanes (from this point on, these will be referred to as a 2-to-i and 3-to-2

lane closures, respectively). The data were to be collected during daylight hours via

video recording devices installed at key locations throughout and upstream of the work

zone. Several sites were located, but complications quickly arose. The status of the

different projects (percent completed) were not known exactly, so coordinating with the

project managers to set up data-collection equipment was not possible. In addition,

contractors have been urged to move toward night construction on the incentive of higher

pay if freeway delays are minimized. Nighttime lane closures do not experience the same

volumes of traffic as during the peak hours of the day, so breakdown, a required

condition for capacity estimation, is typically not observed.

As a result of these obstacles to field data collection, computer simulation of the

lane closure incidents was selected as the next best tool for collecting the data.

Simulation modeling cannot replace field data collection; it can, however, offer insights

into the relative capacities under different geometric configurations and traffic stream

scenarios. A large matrix of scenarios was thus created that considered many of the

factors identified from previous research. Each scenario was input into the simulator and

run 15 times to ensure that the mean error was within the tolerance limit. Data such as









speeds, vehicle lane distributions, headways, and volumes were gathered from the output

files and combined with the input factors to develop significant relationships between

these variables and the capacity through the work zone lane closure.

Simulator Selection

The software package CORSIM was selected for use in the study for several

reasons. This software is available to the University of Florida through McTrans,

allowing for a high level of software support in understanding the algorithms. In

addition, CORSIM has the ability to simulate freeway sections with its integrated package

FRESIM, and the 5.1 edition has been updated with an improved FRESIM engine (Owen

et al., 2000). The following are the principal improvements that were made over past

versions of CORSIM:

* Errors in FRESIM collision avoidance were corrected

* Destination assignment and leader determination were eliminated

* Changes were made to the logic that deals with vehicles crossing interface nodes to
improve the car following between networks

* Errors in processing truck restriction lanes were corrected

* An error in the way vehicle counts on Record Type 53 were converted into entry
volumes was corrected.

In addition to these improvements, FRESIM allows for the analysis of incidents on

freeways as either lane closures, lane drops, or even a shoulder incident, which can be

simulated by the addition of a rubbernecking factor to the length of the segment affected.

Calibration of a FRESIM network is possible using techniques such as rubbernecking and

car-following sensitivity factors, allowing for a realistic representation of real-world

conditions.









Challenges with Previous Versions of CORSIM/FRESIM

Despite the obvious advantages, several problems arose when considering

CORSIM as the software package of choice. Literature from 1995 explains that FRESIM

was unreliable when simulating lane closures, as it did not account for slow-moving

vehicles that severely impacted the queue lengths in the field. According to Dixon et al.

(1995), the large queues observed in the field were due to the existence of one or two

vehicles in a data set that traveled inexplicably slow through the work zone-much

slower than the distribution of speeds in a simulation-and thus caused a queue buildup

that did not appear in the simulator. As a result, FRESIM underestimated the delay

because these vehicles did not exist in the simulation runs. Therefore, the behavior of

vehicles at the lane closure was not replicating actual conditions (Dixon et al., 1995).

The 1995 report used the software FRESIM version 4.5, and since then, improvements

such as those named previously have led to the version 5.1 release (McTrans).

Resolved Challenges with Current Versions of CORSIM/FRESIM

After several initial simulation trials, it was determined that even CORSIM 5.1 has

several processing problems with the FRESIM outputs. These problems with FRESIM

were forwarded to the software development team, and the developers ran the same *.trf

files so that the same outputs could be obtained and evaluated. It was concluded that

these are indeed problems with CORSIM 5.1 and that the newest version, CORSIM 6.0,

corrects all of these issues. Although not currently commercially available, the

University has obtained a Beta copy of the newest software for testing purposes only.

CORSIM 5.1 will be used for all analyses in this report.









The first problem is an inconsistency in the volumes of vehicles when reported by

link and by lane. The software developers quickly corrected this issue, identifying that

the correct outputs from 5.1 were those by lane. Therefore, only values of vehicles by

lane will be extracted from version 5.1. A second issue with CORSIM 5.1 involves the

output from the data stations. A data station is placed on several of the links of a freeway

and headway and speed data are collected at a specified point from the upstream node

(Note: this is not a detector, but a data station. Its only function is to collect speed and

headway statistics at a point of interest). The output files, however, report what seems to

be an incorrect distribution of headway values. There are too many values in the 0.4 to

1.2 second-range, which is not realistic for freeway operating conditions. This second

issue with vehicle headways and distributions, is a result of model input driver-type

parameters. Because no field data were available for calibration, these parameters are set

to the default values for all simulation runs. According to the software developers, the

default values for the car-following parameters are set up to maximize throughput in any

scenario; as a result, the distribution may seem unrealistically skewed (McTrans). This

will effectively lead to higher flow rates and thus higher capacity values through the work

zone, and this issue is discussed in model development results. The third issue is related

to the headway values reported by the data stations. The mean headways reported for

each link should be specific and different by lane. However, for each data station, the

headways are equal by lane. This issue was also resolved quickly. There is an error in

output processing in version 5.1 and the headways by lane per link should be calculated

from the average volume given by lane per link. Thus, the data collected for headways

by lane was calculated and not taken directly from the incorrect output.









CORSIM 5.1-with the described corrections-will therefore be used for all

analyses. Data were collected accurately from the 5.1 output based upon the corrections

from the software developers.

Modeling of Work Zones with CORSIM 5.1

This section presents the preliminary analyses needed to create appropriate

scenarios. The first section defines the operating conditions of the network simulated.

The way in which CORSIM can simulate a work zone is described second, followed by a

detailed justification of closing only the rightmost lanes. The last sub-section

summarizes the results, including the number of runs required per scenario, and the way

in which the work zone will be simulated.

Operating Conditions

A test network was created in order to evaluate whether a lane drop or incident

approach should be used for the study. With 15% trucks present on the freeway, the

breakdown volume for a 2-to-i lane closure was found to be 1900 veh/hr. The

breakdown volume, as defined for this study, is the minimum volume that will cause free-

flow speeds to be reduced by 30% or more at the link immediately upstream of the lane

closure. Furthermore, this decrease in speed leads to queue formation upstream of the

lane closure location (for the remainder of the document, the location of the lane closure

will be referred to as the bottleneck). As a result, the discharge of this queue into the

work zone lane closure is causing the facility to operate at capacity. This flow, 1900

veh/hr, is used throughout the following experiments. Another consideration in operating

conditions relates to the driving behavior of trucks. Throughout all experiments and

simulation runs, trucks will be biased to traveling on the rightmost lane of the freeway

segment. FRESIM provides three choices for truck behavior: not biased or restricted to









any lanes, biased to a set of lanes, and restricted to a set of lanes. The lanes to which a

truck is biased can be specified on the on-screen interface within FRESIM. For all 2-to-1,

3-to-2 and 3-to-i lane closures, trucks will be biased to traveling in the rightmost lane or

lanes.

Simulation of a Work Zone

There is no explicit simulation of a work zone in FRESIM; instead, there are two

techniques that allow FRESIM to approximate a work zone lane closure, and both are

built-in to the user-friendly interface. The first of these is identified as a lane add/drop.

The options allow up to three lane additions or drops to occur within the same link. So,

to simulate a right-lane closure, the rightmost lane would be dropped at a point specified

at a distance from the upstream node, and then it would be added at another specified

point designated again by a distance from the upstream node. The second technique that

can be used to simulate a lane closure is identified as an incident. The user can create

multiple incidents during different times of the simulation on the same link. Such

incidents include capacity reduction due to a shoulder incident (requires a rubbernecking

factor) and/or blockage at a point of incident. Each of these can occur simultaneously

and on several lanes if desired. Both techniques require an upstream distance for a

warning sign, signaling that a lane closure is approaching. It should also be noted that

neither technique has an input for a taper prior to the lane closure. The incident

technique was used in all analyses for this study and the justification is given in the

Results of Preliminary Analyses section.

Lane Closure Location

The next step was to evaluate whether closing the right lane produced the same

results as closing the left lane. The value that was selected as a performance measure









was network-wide average delay. The results of the first experiment comparing the delay

values between lane closure techniques as well as left and right closures are presented in

the following table, Table 3 1.

Table 3 1. Delay Values for Combinations of Lane Closures and Lane Distributions
(Ten Simulation Runs)
Trucks biased to rightmost lane
Results based on 10 simulation runs
All flows 1900 veh/hr


Lane
distribution
L% / R%


Trucks

%


Incident
Delay (veh-
hr)


Lane Drop
Delay (veh-
hr)


60/40 0 6.066 5.991
40/60 0 6.092 6.118
Right Lane Closure
60/40 15 68.703 75.899
40/60 15 61.552 65.217


40/60 0 6.028 5.967
60/40 0 6.216 6.288
Left Lane Closure
40/60 15 56.306 47.353
60/40 15 60.312 42.463


As can be seen from Table 3 1 above, the experiment was run for both the lane

drop and incident techniques. In order to accept that closing the left lane produces the

same results as closing the right lane, the first value of delay in the incident column for

Right Lane Closure should match the first value of delay in the incident column for Left

Lane Closure, and so on. The values are similar between right and left lane closures for

0% truck presence, but differ greatly when truck presence is increased to 15%. Also, the









values between the incident technique and lane drop technique show no consistency and

no intuitive reasoning can explain the differences between the numbers. Because of these

discrepancies, it is not possible to determine whether closing the right lane will produce

the same results as closing the left lane by using network-wide average delay as a

performance measure. This value does not describe what is happening per vehicle, which

causes an inconsistent result that is based upon how many vehicles enter the system,

which is dependent upon flow and breakdown conditions. Therefore, a different

performance measure was considered that does look at the value per vehicle. Average

speed per vehicle was considered and it was found that 9.71 runs are required for an error

tolerance of 15% (see below for calculation):

Table 3 2. Average Speeds per Vehicle (Ten Simulation Runs)
Run # Speed (mph)
1 36.62
2 29.80
3 32.02
4 54.82
5 54.48
6 51.10
7 48.14
8 42.30
9 32.24
10 33.58


Calculation of required number of simulation runs:

Sample Size, n = 10

Sample Mean, MN = 41.51

Sample Std. Dev., SN = 9.899

Error (15%), E = 0.15 MN = 6.227

95% Confidence Interval: +/- (1.96 SN) / (n2) = +/- (6.135)











Required number of runs for 15% error tolerance:

N = 1.962 SN2 / E2 = 9.709

Thus 10 runs are used to compare the values of speed. The results are displayed

below in Table 3 3.

Table 3 3. Average Speed Values for Different Combinations of Lane Closures and
Lane Distributions
Trucks biased to rightmost lane
Results of Ten Simulation Runs
All flows 1900 veh/hr


Lane
distribution
L% / R%


Trucks

%


Incident
Speed
(mph)


Lane Drop
Speed
(mph)


60/40 0 61.483 61.490
40/60 0 61.481 61.478
Right Lane Closure
60/40 15 41.289 40.015
40/60 15 42.773 42.503


40/60 0 61.536 61.520
60/40 0 61.410 61.403
Left Lane Closure
40/60 15 45.397 46.725
60/40 15 44.195 49.084


As seen in Table 3 3 above, the values of average speed per vehicle between left

and right lane closures as well as between incident and lane drop techniques are very

close and within the 15% tolerance error.









Results of Preliminary Analyses

From these results (10 runs based upon 15% error tolerance), it is concluded that

any simulation scenario analysis need not be performed on both a left and right lane

closure, but only on one, as the other will produce the same result. In addition, because

the two techniques of simulating a lane closure produce almost identical values within

15% error, the technique which offers more options in the simulation is selected.

Therefore, the use of an incident will be used in all simulation runs throughout the report,

and the lane drop technique will not be used. This decision is based on the versatility of

the incident technique, allowing for the effects of a rubberneck to be simultaneously

implemented with a lane closure.

Simulation Scenarios

This section will outline the network schematic, the input variables and fixed

values, the number of required simulation runs, and the simulated data that will be

collected.

Simulated Test Section

Figure 3 1 below shows the simulated test segment that is analyzed.

Sign location

I 0.5, 1.0, 2.0 mi.

1/2 mi. 1.0 3.5 miles 1/2 mi. 1
00 00 0 Cm0
2 3 4 5 6 7 8

150 ft. 150 ft. 0.5, 1.0, 1.5 mi.

Figure 3 1. Sketch of the freeway network used in data collection









There are a total of nine nodes (2-8 displayed). The feeder node is located 0.5

miles upstream of node 2. The following is a discussion of the function and

characteristics of each link:

* Link (2,3) 150 feet in length; created to verify headways values being collected
by the data station (located halfway between nodes 2 and 3)

* Link (3,4) Length is variable from 1 to 3.5 miles; created to give vehicles
adequate time for discretionary lane changes a far distance upstream of the work
zone; variable distance is due to variability in links (6,7) and (7,8) (see below).

* Link (4,5) 150 feet in length; created to verify headway values being collected by
the data station (located halfway between nodes 4 and 5)

* Link (5,6) Always 0.5 miles in length; created to observe the driver behavior
prior to the work zone warning sign.

* Link (6,7) Length is variable from 0.5 to 1.5 miles; this is the distance from the
work zone to the upstream warning sign. The changing of this distance is one
reason for the variability in the length of Link (3,4). The overall network length is
constant, so Link (3,4) is either lengthened or shortened when Link (6,7) is either
shortened or lengthened, respectively.

* Link (7,8) Length is variable from 0.5 to 2.0 miles; this is the link in which the
lane closure is in place. The changing of this distance is the other reason for the
variability in the length of Link (3,4). The overall network length is constant, so
Link (3,4) is either lengthened or shortened when Link (7,8) is either shortened or
lengthened, respectively. There is also a data station placed halfway between nodes
7 and 8, in order to verify headway data on that link.

Input Variables

The variables selected for the model development are listed below and their values

and limitations are described in detail following the list:

* Lane Configurations 2/1, 3/2, 3/1
* Volume Distributions (percentages)
o (2/1 closure) 50/50, 40/60, 30/70 (left/right)
o (3/2 and 3/1 closure) 20/40/40, 30/30/40, 30/40/30 (left/middle/right)
* Length of Work Zone 0.5 mi, 1.0 mi, 2.0 mi
* Distance of Sign Upstream of Work Zone 0.5 mi, 1.0 mi, 1.5 mi
* Presence of trucks (percentage) 0%, 10%, 20%
* Rubbernecking factor (percentage) 0%, 15%, 25%










The input volume distributions were determined by considering reasonable

operating conditions for a free-flowing freeway network. For example, a 20/80 input

distribution was not used because it is unlikely that such a distribution would be observed

in the field. The maximum length of the work zones is limited by the FDOT Design

Standards for 2006. These state that for any facility where the speed limit is greater than

55 mph, the length of the work zone shall not exceed a length of 2 miles (Design

Standards, Index 600, Sheet 2 of 10). Also, the warning sign placement upstream of the

work zone is to be at a distance no less than 0.5 miles for facilities where the posted

speed limit is 45 mph or more (Design Standards, Index 600, Sheet 4 of 10).

The analysis of the effect of the work zone length on the work zone capacity

showed no significant relationship between the two variables. Figure 3 2 below

illustrates the relationship between the work zone capacity and the length of the work

zone lane closure from the simulated data.


Work Zone Capacity vs. Work Zone Length


2600

2400

E 2200

2000

. 1800

S1600
o
N
. 1400

1200

1000


! y = 0.322x+ 1590.6 $
3 | R2 = 0.0000004 $
< !

!-------


0 0.5 1 1.5 2 2.5
Work Zone Length (mi)


Figure 3 2. Relationship between work zone flow and work zone length









From Figure 3 2 above, there exists no relationship between the length of a work

zone and the capacity throughput. As the length of the work zone increases, no

significant variation in vehicular flow exists through the lane closure. This variable will

therefore not be included in any of simulation runs for all lane closure configurations.

The presence of trucks ranges from zero to twenty percent, again limited by the

consideration of reasonable operating conditions. Similarly, the rubbernecking factor

ranges from zero to twenty-five percent, and will be used to model any type of incident

on the shoulder or the presence of law-enforcement vehicles or general road work

equipment. Because there is no literature on the effect of the rubbernecking factor,

several simulation runs were made to identify and understand the way this factor affects

the capacity of the roadway.

To view the effect of the rubberneck factor on a freeway work zone lane closure

segment, the capacity of that segment is compared to the rubberneck factor used. The

scenario tested is a 2-to-i lane closure with a flow rate of 2200 veh/hr distributed 40% in

the through (left) lane and 60% in the closed (right) lane. The length of the work zone is

0.5 miles, truck presence is at 20%, and the advance warning sign is located at 0.5 miles

upstream of the bottleneck. Table 3 4 below illustrates the effects of the rubbernecking

factor on the capacity through a work zone lane closure:









Table 3 4. Effects of Rubbernecking Factor on Capacity through Work Zone Lane
Closure
Trucks biased to rightmost lane
Results of Five Simulation Runs
All flows 2200 veh/hr

Rubberneck Average Headways through Average of Capacity
Factor (%) Work Zone (5 simulation runs) headways (veh/hr/ln)
0 1.65, 1.64, 1.64, 1.64, 1.64 1.64 2192.5
5 2.01, 1.98, 2.09, 2.02, 2.11 2.04 1763.0
15 2.24, 2.20, 2.25, 2.25, 2.26 2.24 1607.1
25 2.61, 2.51, 2.47, 2.61, 2.47 2.53 1420.7
35 2.79, 2.76, 2.78, 2.78, 2.79 2.78 1295.0
45 2.98, 2.99, 2.98, 2.97, 2.96 2.98 1210.0


The results indicate that an increase in the rubbernecking factor leads to a decrease

in capacity. The capacity through the work zone is calculated by dividing 3600

(seconds/hour) by the headway value (seconds/vehicle) in the previous column. This

relationship is not linear with values greater than 25%. In addition, the percentage of

rubbernecking does not reduce the capacity by the same percent. For example, the 5%

rubbernecking factor reduces capacity by nearly 20%, whereas a 25% rubbernecking

factor has only a slightly increased effect on capacity reduction. After this point,

increasing the rubbernecking factor does not have as large an effect and it is for these

reasons that 25% will be the maximum rubbernecking factor applied in this study.

Simulated Test Section Setup-Input Fixed Values

As limited by the FDOT Design Standards, the free-flow speed that will be used

throughout all analyses will be 55 mph through the work zone; this value cannot be lower

than 10 mph less than the mainline free-flow travel speed (Design Standards, Index 600,









Sheet 3 of 10). Because the facility being modeled can be a state highway or freeway

facility, a free flow speed for mainline traffic of 65 mph will be used throughout the

analysis.

A value of 2400 vehicles per hour per non-closed lane will be implemented as the

fixed flow rate. This is the flow rate at which breakdown occurs-speed reduction of at

least 30% immediately upstream to the lane closure-with nothing other than passenger

cars present in the traffic stream. This base case did not include any trucks and had a

rubbernecking factor of zero percent. The traffic entering the system was distributed

equally between lanes-50/50-and the upstream sign was placed at 1.5 miles upstream

of the beginning of the bottleneck. This flow rate of 2400 vehicles per hour per non-

closed lane will cause a queue to form and the discharge causes the downstream link to

operate at capacity. Therefore, for 2-to-i and 3-to-i lane closure configurations, the flow

rate will be fixed at 2400 veh/hr. For the 3-to-2 lane closure, the flow rate will be 4800

veh/hr.

The relationships that are shown in CHAPTER 4: MODEL DEVELOPMENT do

not appear to have any values of capacity between 2000 and 2200 veh/hr/ln. Each point

plotted is the average of 15 simulation runs. Therefore, the average of the runs do not

have values of capacity between 2000 and 2200, but the full dataset does include values

between these numbers. This apparent absence of data is a result of the type of

breakdown that is occurring based on the characteristics of the traffic stream for specific

scenarios. All values of capacity above 2200 veh/hr/ln have only passenger cars in the

traffic stream and both the rubbernecking factor and truck percentage is at zero. The type

of breakdown that occurs in this case is different than if any other factor is present. With









only passenger cars in the traffic stream, queues are building and recovering throughout

the simulation time period. There is a shockwave present that travels upstream, causing

breakdown conditions to occur and dissipate throughout the traffic stream. Therefore,

because free-flow speed is reduced by at least 30%, capacity is being observed

downstream through the work zone. Once a rubbernecking factor or a presence of trucks

is introduced into the traffic stream, however, capacity through the work zone is reduced

due to a breakdown in speeds that is greater than 50%, causing a permanent queue o form

that does not recover within the simulation time period. As a result, there is a constant

discharge of vehicles into the work zone, and capacity conditions are also observed, but

the values are lower than the passenger-car only traffic stream.

Required Number of Simulation Runs

As calculated previously, 10 simulation runs per scenario are required for a 15%

error tolerance in the sample mean. In order to obtain a higher number of data points

from the data collection (from simulation) and thus further increase final model precision,

15 runs per scenario will be simulated.

Total Number of Simulation Scenarios

There will be four variable input values per lane closure configuration, each with

three levels of variation. For thoroughness, every effect that each variable has on the

other will be analyzed, and thus a total of 243 different scenarios will be analyzed, each

simulated 15 times:

4 input values, 3 levels Nscen = 34 = 81 (per lane closure configuration)

Table 3 5 below illustrates the three levels of variation for each variable for all

lane closure configurations. Because there are two other configurations-3/2 and 3/1-

the total number of scenarios is 243.









Table 3 5. Variation of Input Parameters
Variable Level 1 Level 2 Level 3 units
lane2-to- 3-to-2 3-to total-to-open lanes in
S. 2-to-1 3-to-2 3-tol
configuration work zone

lane 50 / 50 40 / 60 30 / 70 left % / right %
lane
distributions 20 / 40 / 30 / 30 / 30 / 40 / left % / middle % /
40 40 30 right %
upstream sign
upstream sign 0.5 1 1.5 miles
distance
truck % 0 10 20 percentage
rubber % 0 15 25 percentage


As a result, the 243 files created will each be simulated for 15 runs, giving a total of

3,645 output files, or data points for model development.

Output Values

In addition to the variables described in the previous section, data from the

simulations will be collected to model the lane distributions and their effect on work zone

capacity. The following values will be taken from the output files and used for model

development:

* Volumes by lane through link (7,8)
* Vehicle lane distributions through all links
* Speeds by lane through all links
* Number of lane changes through all links

TRAFVU screen shots for each of the 3 different lane closure configurations are

provided in APPENDIX F.














CHAPTER 4
MODEL DEVELOPMENT

The software package STATISTICA (Release 7) was used in developing the

relationships between selected performance measures and the vehicular capacity through

the temporary lane closure. This section summarizes the process of data analysis and

model development for each of the three lane closure configurations. First, the

relationships between collected data and capacity will be outlined and identified. This

leads into the development of the models, using General Linear Regression techniques as

provided by the software. The final models consist of a combination of highly-

correlated, statistically significant parameters that are obtainable from field data

collection. As a result, the models are practical and easy to use. Finally, an example

outlining the steps in using each of the models will be provided for each respective lane

closure configuration.

Data Analysis

The network created for simulation has been designed so that field data may be

input into the appropriate location on the freeway segment. For this reason, many links

were created upstream of the lane closure in order to collect lane distribution data

significant distances upstream of the first lane closure warning sign. For the development

of the models, however, only those lane distributions from links (4,5), (5,6), and (6,7) are

considered. In CORSIM 5.1, lane distributions upstream of the warning sign are not

affected by the work zone. Therefore, the simulated behavior of vehicles just upstream of

the location of the warning sign is the same as that of vehicles three to four miles









upstream in CORSIM 5.1 (this is not expected in the field, which is the reason for the

existence of more upstream links not used in modeling). When simulated vehicles pass

the sign, they react and merge based on existing queues, gaps, travel speeds, and driver

aggression level. Because the warning sign is located at Node 6 for all simulation

scenarios, the parameters for lane distributions, speeds, and lane changes will only be

estimated for the aforementioned links in the model development.

In addition, the lane distributions that were input into the simulation are not used in

model development. Rather, the actual lane distribution data collected from links (4,5),

(5,6), and (6,7) were used. The program distributes the vehicles into lanes as designated

by the input percentages. However, immediately upon entry into the system, vehicles

begin to make discretionary lane changes that alter the input lane distribution

percentages. The degree to which vehicles make these lane changes can be controlled by

modification of the driver behavior parameters within the software. The lack of field data

made any modifications irrelevant and thus the default values for driver types were used.

These numeric default values range from not aggressive to very aggressive for ten driver

types, and 10% of each type comprise the traffic stream (the user cannot modify this).

For that reason, observed lane distributions are used in model development.

2-to-1 Lane Closure Model Development

For this test configuration, a two-lane freeway segment is reduced to one lane with

the rightmost lane closed. The following input variables and performance measures were

plotted against the value of capacity through the lane closure and are displayed in the

following order in this section:

Input Variables

S Upstream sign distance










Truck percentage
Rubbernecking factor


Performance Measures (from simulated data)

* Capacity through lane closure (shown plotted against all other data)
* Lane changes per link
* Speeds per vehicle
* Actual lane distributions

Figure 4 1 below shows the relationship between the work zone capacity and the

location of the upstream warning sign.


Work Zone Capacity vs. Upstream Sign Location


y = 9.4476x + 1581.5
R2 = 0.0002




*
I
( !


0.4 0.6 0.8 1
Upstream Sign Location (mi)


1.2 1.4 1.6


Figure 4 1. Relationship between work zone capacity and upstream warning sign
distance

As the distance of upstream warning increases, there is a small increase in capacity.

This relationship, though immediately not apparent, becomes more significant when


2600

2400

2200

2000

1800

1600

1400

1200

1000


0 0.2








44



viewed in combination with the lane distributions of link (6,7) as an interaction variable.


This is discussed later in this section with Figure 4 9.


The following two figures (4 2, 4 3) show the effects of an increase in the truck


percentage and an increase in the rubbernecking factor on the throughput capacity.


Work Zone Capacity vs. Truck percentage


y =-14.651x+ 1737.5
R2 = 0.1552











0 5 10 15 20 2!
Truck Percentage


Figure 4 2. Relationship between work zone capacity and truck presence in traffic
stream


Work Zone Capacity vs. Rubbernecking Factor


2600

S2400

I 2200

2000

C. 1800

g 1600
O
N
. 1400
O
1200

1000


5 10 15 20
Rubbernecking Factor (%)


25 30


Figure 4 3. Relationship between work zone capacity and truck presence in traffic
stream


y =-24.407x + 1916.4
R 2 = 0.6821










Both figures above show that an increasing percentage of trucks in the traffic

stream or an increasing rubbernecking factor leads directly to a decrease in capacity

through the lane closure. These variables are expected to be significant factors in the

final models.

The relationship between the number of lane changes in link (6,7) and the

throughput capacity is shown below in Figure 4 4.


Work Zone Capacity vs. Lane Changes in (6,7)


2600

2400

2200

2000

1800

1600

1400

1200

1000


y = 6.8601x 221.27


50 100 150 200 250 300
Lane Changes in (6,7)


Figure 4 4. Relationship between work zone capacity and lane changes in link (6,7)

There is a positive correlation indicating that an increasing number of lane changes

leads to a higher value of capacity. Although considered in model development, this

variable was ultimately not included in the final models. This variable cannot be

collected easily in the field. In addition, the effect of this variable is captured by other

included model parameters that are more important and could not be excluded. For







46


example, as shown below in Figure 4 5, the number of lane changes is correlated to the

length of link (6,7).


Lane Changes in Link (6,7) vs. Length of Link (6,7)


350

300
c
_j
a 250

- 200

a 150
._
-0
0- 100
I-

E 50

0


i I







y = 22.861x + 241.31
R2 = 0.0809


0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
Length of Link (6,7) (mi.)


Figure 4 5. Relationship between the number of lane changes in link (6,7) and the
length of link (6,7)

The number of lane changes did not show significance when included in the model

with other parameters that were more important (the effect of the number of lane changes

is captured by the percentage of vehicles traveling on lane 1 and lane 2, represented as

vehicle lane distributions in the final models). The distance of the link (6,7)-which also

captures the effect of the lane changes-however, is included in the final models

represented by the variable upstream sign distance.

Figure 4 6 illustrates the relationship between the speeds of vehicles in all lanes in

the link upstream of the placement of the warning sign. At this point, the vehicles have

not seen and thus have not reacted to any type of warning or work zone ahead.






47



Work Zone Capacity vs. Speed in link (5,6)


2400

2200

> 2000

8 1800 m m hu


0
o 1600

N 1400 ,.

1200

1000
0 10 20 30 40 50 60 70
Speed in link (5,6) (mph)

Lane 1 Lane 2 Linear (Lane 2) Linear (Lane 1)


Figure 4 6. Relationship between work zone capacity and the average speed per vehicle
in lanes one and two of link (5,6)

Both lanes show an increase in capacity with increasing link speeds. This variable

will thus be considered in model development.

The speeds in the link immediately downstream of the lane closure warning sign

were also considered, and their relationship to the throughput capacity is shown below in

Figure 4 7.










Work Zone Capacity vs. Speed in Link (6,7)

2600
200 y = 26.629x + 1302.7

2400 = 07737
I-P
S2200 "n
S220y = 10.489x + 1273.1

> 2000


o

I-
0
I 1200
N 1400 ------------------------


1000
0 10 20 30 40 50 60 70
Speed in link (6,7) (mph)

Lane 1 Lane 2 Linear (Lane 2) Linear (Lane 1)

Figure 4 7. Relationship between work zone capacity and the average speed per vehicle
in lanes one and two of link (6,7)

There is a strong relationship between these variables, which were also considered

in the final model development. An increase in speed in lane 1 (closed lane) does not

increase capacity as much as a higher speed in lane 2 (through lane). This is because a

higher speed in lane 1 implies less congestion and thus smoother merging into the

through lane. In this case of less congestion, there is a steady flow of vehicles traveling

in lane 2 which increases the capacity more significantly with an increase in traffic

stream speed.

Another important relationship that was identified is that of the distribution of

vehicles in links upstream and immediately downstream of the work zone warning sign.

These relationships are important if an agency wants to implement a particular traffic

management strategy. If higher capacities are a result of lower percentages of merging









vehicles, for example, then an early merge strategy is an effective option. Figure 4 8

below shows the effect that the vehicular lane distributions have on work zone capacity.


Work Zone Capacity vs. Lane Distributions

2600
S2400-
2200
1 2000 -

1800 x-
1600 -* )K 0
N 1400 ANAV% V), '%

O1200 -1412.8x + 2073.4
1000 R2 = 0.3943
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
Lane Distributions of Lane 1 (closed lane)

link (4,5) link (5,6) link (6,7)
Linear (link (6,7)) Linear (link (4,5)) Linear (link (5,6))


Figure 4 8. Relationship between work zone capacity and the vehicular distributions on
lane one of all links

The relationship is similar for links (4,5) and (5,6), and therefore only link (5,6)

will be considered in the model development. The effects of the vehicular lane

distributions immediately before and after a work zone sign will thus be considered in the

final models.

In model development, there was some interaction between the distribution of

vehicles in link (6,7) and the sign distance (this is also the length of link (6.7)). With an

increasing sign distance, a higher fraction of the traffic stream is present in the through

lane (lane 1) while a lower fraction is in the closed lane. Longer warning distances







50


upstream of a lane closure allow vehicles more time and space to merge into the through

lane. This relationship is illustrated below.


Lane Distributions of Link (6,7) vs. Upstream Sign Location


1 -----
p 0.9
0.8
3 0.7
o 0.6
.2 0.5
a0.4
0.3 0
0.2
0.1
I0 I


0 0.


2 0.4 0.6 0.8 1 1.2 1.
Upstream Sign Location (mi)

* Lane 1 Lane 2 Linear (Lane 1) Linear (Lane 2)


Figure 4 9. Relationship between vehicular lane distributions in lanes one and two of
link (6,7) and the location of the upstream warning sign

The interaction of these two terms-lane distributions of link (6,7) and upstream

sign distance-were plotted against capacity to verify that a relationship existed. These

results are illustrated below.












Work Zone Capacity vs. Lane 1 Distribution in Link (6,7) and
Sign Distance


ZOUU


c 2000


.' 1500

1000.
o 1000
flj


Sy = -1194.5x + 1977.7
N R2 = 0.3722
S500
o

0

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Lane 1 Distribution (6,7) Sign Distance (mi)



Figure 4 10. Relationship between work zone capacity and the interaction of lane
distributions in link (6,7) and upstream sign distance

As a result, the net effect of increasing sign distance and lane distribution is


negative for lane one. This factor is included in the final model.


Another interaction was observed between the location of upstream warning sign


and the average speeds of vehicles in link (6,7). Their combined effect on capacity is


shown below in Figure 4 11.


~
*C

'~~
~~ **~*
~
fLI~t
z
~ ** ~~ ~~











Work Zone Capacity vs. Speeds in Lane 1 (6,7) and Sign
Distance


ZOUU


c 2000
>

S1500

1000.
C 1000
flj


*o
------ ------------,----
t
f^^ ^ .*^ ^ *


a y = 4.8857x + 1416.9
N R2 =0.2341
S500
o
0
0 -
0 20 40 60 80 100 120
Speeds in Lane 1 (6,7) Sign Distance (mph*mi)


Figure 4 11. Relationship between the work zone capacity and the interaction of the
speeds in lane 1 of link (6,7) and the location of the upstream warning sign

The results indicate that there is a relationship between these variables, and the

interaction of speeds and sign distance is considered in the final model.

Another important interaction between variables was observed between the average

speeds in lane 1 and lane 2 of both links (5,6) and (6,7). As shown in the following

figures, the two lanes' speeds are highly dependent on each other; as a result, both lanes'

speeds cannot be used together in the model. Therefore, a polynomial regression-of

order 2-was performed on each link. The resulting equations predict the speeds of lane

2 for each link from the lane 1 speed data. These relationships are shown in the

following figures, Figure 4 12 and .4 13.







53



Speeds in Link (5,6)


60
y = 0.0188x2 0.3209x + 10.532
50-R2 0.9893
R = 0.9946
cA/
E 40 -

c 30

20

0 -J
10



0 10 20 30 40 50 60 7
Lane 1 (mph)


Figure 4 12. Relationship between the speeds in lane 1 and lane 2 of link (5,6)


Speeds in Link (6,7)


Figure 4 13.


10 20 30 40 50 60
Lane 1 (mph)


Relationship between the speeds in lane 1 and lane 2 of link (6,7)









From the results above, the polynomial expressions were used to calculate the

predicted values of the speeds in lane 2, and those predicted values were used in model

development when both lanes were considered.

3-to-2 Lane Closure Model Development

For this test segment, a three-lane freeway segment is reduced to two lanes with the

rightmost lane closed. The input variables and performance measures discussed in the 2-

to- model development were plotted against the value of capacity through the lane

closure. The graphical representations of these relationships are shown in APPENDIX

A: Model Development Relationships for a 3-to-2 Lane Closure Configuration. The

progression of relationships follows that of the 2-to-1 lane closure configuration and the

same variables will be considered in the final model development.

3-to-1 Lane Closure Model Development

For this test segment, a three-lane freeway segment is reduced to one lane with the

two rightmost lanes closed. The graphical representations of these relationships are

shown in APPENDIX B: Model Development Relationships for a 3-to-1 Lane Closure

Configuration.

Final Models

This section presents the final models for each of the three lane closure

configurations, with all variables within a 0.05 level of significance. The models are

based upon the relationships described in previous sections, with work zone capacity

through each lane closure as the dependent variable. One model that can be used to

predict capacity was developed for each of the three lane closure configurations studied.

The values of vehicle speeds and distributions in the closed lanes are the primary inputs

into the models. Sign distance, truck percentage, and rubbernecking factor also









contribute to the final value of work zone capacity. Speeds and lane distributions are

performance measures that can be controlled in a work zone. Signs or physical barriers

can encourage or require lane merges, maintaining traffic stream speeds and/or vehicle

travel lanes. Therefore, having both of these factors present in the models provides a

view of the effect of different types of management strategies to be imposed through the

work zone.

Variable Explanations and Example of Model Usage

The variables used in the developed models are defined in detail below. Their

limitations in range, based on collected data, and an example of capacity calculations for

each of the lane closure configurations are reported in APPENDIX E.

Capacity. This is the dependent variable and represents the maximum number of

vehicles that travel through the work zone lane closure given specific input values for the

model parameters. This traffic stream condition is a result of a drop in free-flow speed of

at least 30% in the link immediately upstream of the work zone. This value is given in

units of veh/hr/lane, and for the 3-to-2 lane closure configuration, this value is the

average of both open lanes.

Intercept. This is the value that is being adjusted by other parameters in each of the

models. By itself, it is not an estimate of the base capacity of any of the lane

configurations because inputting zeros for all other parameters is not reasonable. The

unit of this variable is veh/hr/lane.

SignDist. This variable represents the upstream distance of the work zone warning

sign. Because this variable is always the same length as link (6,7), some interactions

have been noted and accounted for in the model development. This variable is input into

the model as miles.









Truck%. This variable represents the percentage of heavy vehicles in the traffic

stream. It is input into the model as a whole number (e.g. 20 for 20%).

Rubber%. This variable represents the degree to which capacity is reduced due to

any additional factors within the lane closure. A higher rubbernecking factor leads to a

decrease in capacity throughput, and it is input into the model as a whole number (e.g. 20

for 20%). The value of this variable should be chosen carefully, since field data has not

yet been acquired to properly calibrate its effect. With a zero percent rubbernecking

factor, no additional events are causing capacity reduction other than the geometry of the

work zone and factors discussed previously. However, it may be possible that there is a

presence of law enforcement or heavy construction equipment and workers, which would

lead to additional decrease in capacity as drivers react to the more hazardous driving

conditions. Hence a rubbernecking factor ranging from 5 to 25 percent may be used to

simulate this additional reduction in capacity.

SpdLanl(5,6 ). This variable represents the speed of vehicles in lanes 1 of link

(5,6) (lane 1 being the rightmost lane). The units of this variable are in mph and should

be input into the model as such.

SpdLanl(5,6) x SignDist. This variable is an interaction term between the speed of

vehicles in lane 1 of link (5,6) and the upstream distance of the work zone warning sign.

This interaction is a result of the following logic: scenarios that have shorter sign

distances have higher likelihood of producing queues extending beyond the warning

sign into link (5,6). If queues in a particular lane extend into link (5,6) then the average

speeds per vehicle are thus affected (reduced) at this link.









CalcSpdLan2(5,6). There is a strong correlation between the actual speeds of lane

1 and lane 2 for link (5,6). This variable is a polynomial regression of order 2 that

estimates the speeds in lane 2 of link (5,6) using the speeds in lane 1 of link (5,6) as an

input.

SpdLanl(6,7), SpdLan2(6,7). These variables represent the speeds in lanes 1 and 2

of link (6,7) (lane 1 being the rightmost lane). The units of these variables are in mph

and should be input into the models as such.

SpdLanl(6,7) x SignDist, SpdLan2(6,7) x Sign Dist. These variables are

interaction terms between the speed of vehicles in lanes 1 and 2 of link (6,7) and the

upstream distance of the work zone warning sign. This interaction is a result of scenarios

that have shorter sign distances have higher likelihood of producing queues extending

beyond the warning sign into the upstream link (5,6). If queues in a particular lane

extend into link (5,6) then the average speeds per vehicle are thus affected (reduced) at

this link.

CalcSpdLan2(6,7). There is a strong correlation between the actual speeds of lane

1 and lane 2 for link (6,7). This variable is a polynomial regression of order 2 that

estimates the speeds in lane 2 of link (6,7) using the speeds in lane 1 of link (6,7) as an

input.

CalcSpdLan3(6,7). There is a strong correlation between the actual speeds of lane

2 and lane 3 for link (6,7). This variable is a polynomial regression of order 2 that

estimates the speeds in lane 3 of link (6,7) using the speeds in lane 2 of link (6,7) as an

input.









DistrLanl(6,7), DistrLan2(6,7). These variables represent the fraction (percent

divided by 100) of vehicles present in lane 1 of link (6,7). For example, if 10% of

vehicles are traveling in lane 1, the input value would be 0.10 into the model for this

variable.

DistLanl(6,7) x SignDist, DistLan2(6,7) x SignDist. These variables are

interaction terms between the distribution of vehicles in lanes 1 and 2, respectively, of

link (6,7) and the upstream distance of the work zone warning sign. The interaction is a

result of the relative ease for vehicles to merge into the non-closed lane, given specific

link lengths. A larger sign distance creates more space for vehicles to merge into the

through lane(s). The sign distance is input in units of miles, and the lane distributions as

a decimal (e.g. 0.1, 0.5, etc.).

Capacity Estimation Models for each Lane Closure Configuration

The following sections present the final models for each lane closure configuration.

For actual STATISTICA output screen shots (including p-stats and t-stats), please see

APPENDIX D.

2-to-1 lane closure configuration

The following model estimates the capacity for a 2-to-i lane closure configuration

(Equation 4-1). The dependent variable, Capacity2tol, represents the number of vehicles

per hour per lane that travel through the open lane given a set of input values. The model

is shown below:









Capacity2tol = 1623.02

+ (740.88) x SignDist

+ (-14.23) x Truck%

+ (-22.83) x Rubber%

+ (-409.76) x DistrLanl(6,7) x SignDist

+ (-13.513) x SpdLanl(6,7) x SignDist

+ (26.69) x CalcSpdLan2(6,7) (Eq. 4-1)

The adjusted R2 value for the relationship in Equation 4-1 is 0.971.



The variable CalcSpdLan2(6,7) is calculated from the input variable SpdLanl(6,7):

CalcSpdLan2(6,7) = 0.0143 x (SpdLanl(6,7))2

0.5816 x SpdLanl(6,7)

+ 9.213 (Eq. 4-2)

The R value for the relationship in Equation 4-2 is 0.9242.

3-to-2 lane closure configuration

The following model estimates the capacity for a 3-to-2 lane closure configuration

(Equation 4-3). The dependent variable, Capacity3to2Avg, represents the average

number of vehicles per hour per lane that travel through the work zone, given a set of

input values. To calculate the total number of vehicles per hour through the work zone,

the Capacit3to2Avg value should be multiplied by two. The model is shown below:









Capacity3to2Avg = 1595.84

+ (711.49) x SignDist

+ (-5.87) x Truck%

+ (-17.88) x Rubber%

+ (-1211.94) x DistrLanl(6,7) x SignDist

+ (-10.58) x SpdLanl(5,6) x SignDist

+ (9.30) x CalcSpdLan2(5,6) (Eq. 4-3)

The adjusted R2 value for the relationship in Equation 4-3 is 0.954.



The variable CalcSpdLan2(5,6) is calculated from the input variable SpdLanl(5,6):

CalcSpdLan2(6,7) = 0.0188 x (SpdLanl(5,6))2

0.3209 x SpdLanl(5,6)

+ 10.532 (Eq. 4-4)

The R value for the relationship in Equation 4-4 is 0.9946.

3-to-1 lane closure configuration

The following model estimates the capacity for a 3-to-i lane closure configuration

(Equation 4-5). The dependent variable, Capacity3tol, represents the average number of

vehicles per hour per lane that travel through Lane 3, given a set of input values. The

model is shown below.









Capacity3tol = 1665.42

+ (763.56) x SignDist

+ (-10.12) x Truck%

+ (-20.07) x Rubber%

+ (-1698.76) x DistrLanl(6,7) x SignDist

+ (-626.50) x DistrLan2(6,7) x SignDist

+ (-13.513) x SpdLan2(6,7) x SignDist

+ (26.84) x CalcSpdLan3(6,7)

The adjusted R2 value for the relationship in Equation 4-5 is 0.976


(Eq. 4-5)


The variable CalcSpdLan3(6,7) is calculated from the input variable SpdLan2(6,7):

CalcSpdLan3(6,7) = 0.0136 x (SpdLan2(5,6))2

0.5144 x SpdLan2(5,6)

+ 8.108 (Eq. 4-6)


Discussion of Results

Three models were developed for each of the three lane closure configurations on a

temporary freeway work zone. The parameters selected are easily visualized and can be

efficiently collected from the field. The first section below discusses the effect of the

three models' variables on the capacity throughput of the work zone lane closure. The

second section describes the way in which the models can be applied by the FDOT and

the assumptions that must be made in using the capacity estimates.









Effects of Model Variables on Capacity

The relationship of the link (5,6) speeds to capacity is important because the

vehicles in the traffic stream at this point have not yet seen the work zone warning sign,

which is located at node 6. The speeds in lane 1 (actual) and 2 (predicted) of link (5,6)

was modeled for the 3-to-2 lane closure configuration. Lane 2 is the first open through

lane (merging from right to left) of the work zone and lane 1 is the lane immediately to

the right of that (the lane from which vehicles are merging). The model parameters show

that for link (5,6), higher speeds in the to-be through lane lead to an increase in capacity

and thus lower speeds lead to lower values of capacity. Higher speeds imply that the

queue created at the lane closure does not extend upstream of the warning sign and

vehicles are merging and passing through the work zone smoothly. Higher speeds on the

to-be closed lane, however, lead directly to a decrease in capacity. This relationship is

also a function of queue length growth on the through lane. Depending on the level of

congestion (defined by the other user inputs), a queue may also form on the merging lane.

Because vehicles are merging into the through lane once they pass the warning sign, the

queue will grow much faster on the through lane than on the merging lane. This may

cause vehicles upstream in the to-be through lane to slow down (lower speeds lead to a

lower capacity) while vehicles in the to-be closed lane are maintaining free flow speed.

Thus, high speeds in the to-be closed lane lead to quick queue buildup in the through lane

and the congestion leads to a lower capacity value. Lower speeds in the to-be closed lane

imply that the queue has extended beyond the warning sign and all lanes are congested,

thus also leading to a reduction in capacity by both speed parameters. The 3-to-2 lane

closure configuration was the only one that was modeled using the speeds in link (5,6).

This is because the work zone has two open through lanes, and the vehicular behavior









was more correlated to that of vehicles before they first encounter the work zone warning

sign. Vehicles can make discretionary lane changes in this work zone configuration, as

they can in the upstream link (5,6). The other two lane closure configurations, 2-to-i and

3-to-1, are modeled using the speeds in link (6,7). This is the link after the vehicles have

seen the warning sign and are required to make a merging maneuver as soon as possible.

The speeds in link (6,7) were more closely correlated to the behavior of vehicles in the

work zones where only one through lane was open. The parameter estimates suggest that

the same trend exists that was observed in the upstream link (5,6). Higher speeds in the

to-be closed lanes lead to a decrease in capacity, while higher speeds in the to-be open

lanes lead to an increase in work zone capacity. The speeds in the lanes considered are

correlated (see Figure 4 13), and speeds as high as 50 mph on the to-be closed lane

show speeds of less than 20 mph on the to-be open lane. Therefore, high speeds of the

vehicles in the to-be closed lane are merging into a congested to-be through lane in which

the speeds are not high, thus overall reducing capacity through the work zone. Capacity

is lower because of the creation of a shockwave on the through lane every time a rapidly

moving vehicle slows down after merging into the through. This does not lead to a

constant queue discharge on the through lane. When vehicle speeds are higher than 50

mph on the to-be closed lane, however, vehicle speeds on the through lane are also

higher, indicating a much smoother merging pattern and thus overall increasing capacity

(with the increasing speeds in the to-be closed lane).

The second relationship developed is between work zone capacity and the vehicular

lane distributions upstream of the work zone. For all lane closure configurations, the

model parameters show that for link (6,7), a larger fraction of vehicles in the to-be closed









lane(s) leads to a decrease in capacity. This effect is a result of the quantity of merging

maneuvers occurring after the warning sign. A higher fraction of vehicles in the to-be

closed lane(s) leads to a higher required number of merging maneuvers. A lower number

of merging maneuvers is a result of a higher fraction of vehicles in the to-be through

lane(s)-and thus a lower fraction in the to-be closed lane(s)-which leads directly to a

steady traffic stream moving more efficiently through the work zone. A higher number

of merging maneuvers leads to an increase in the amount of shockwaves present in the to-

be through lane; these shockwaves disrupt the steady discharge of the queue into the

work zone and reduce the work zone capacity.

In addition to the speed and lane distribution relationships, the distance of the

warning sign, the presence of trucks, and a rubbernecking factor also contribute an effect

to capacity. The distance of the upstream warning sign increases the work zone capacity

with an increasing distance. This variable has some interaction with the lane distributions

and speed values, and is thus implemented in the models as an interaction term. The

increasing effect that warning sign distance has on capacity, however, is not greater than

the decreasing effect of the lane distribution factor or of the speed factor. Thus the

interaction of the two terms leads to a net decreasing effect on capacity, as seen in the

models' negative interaction term coefficients. As the models' parameters illustrate, a

higher percentage of trucks present in the traffic stream leads to a decrease in capacity.

The greatest decrease in capacity due to truck presence occurs for the 2-to-i lane

configuration; this is a result of the simulation scenarios having been set up with trucks

biased to travel in the rightmost lane. Because there are only two travel lanes in this

configuration, the truck presence has a strong effect. The smallest decrease in capacity is









observed with the 3-to-2 lane closure configuration. Again, because trucks are biased to

the rightmost lane, all trucks entering link (6,7) will be present in lane 1. However,

because there are three travel lanes and two of them are open, the effect of truck presence

is not as pronounced as with the 2-to-i or even 3-to-i configurations. A rubbernecking

factor ranging from 0 to 25% also reduces the capacity through the work zone in all three

lane closure configurations. The reduction is achieved by the software by increasing time

headways between vehicles traveling through the work zone, and the factor is included to

simulate any additional reason for a capacity reduction.

Model Application by the FDOT

The current method employed by the FDOT considers only the geometric

characteristics of a potential work zone in order to estimate the capacity. The new

models developed consider some geometric characteristics as well as traffic operating

conditions. These models are intended to be used as tools to estimate the capacity of a

work zone lane closure, given a set of geometric and traffic operating inputs with the

work zone already in place. To use the models effectively, the geometric and operational

conditions specified in capacity estimation should be implemented and regulated in the

field. The models developed for the new methodology require the input of some

geometric factors that are obtainable by engineers before the work zone is built, based on

the collection of field data. These are listed below:

* Distance of the upstream warning sign
* Truck percentage this value can be obtained for different times of day
* Rubbernecking factor this value is a result of the quantity of workers and/or
heavy equipment present throughout the work zone









The new models estimate a capacity value based on a combination of the above

factors and additional factors that are not obtainable before the work is built. These are

listed below:

* Vehicle lane distributions upstream of the work zone lane closure
* Average speed per vehicle upstream of the work zone lane closure


Because the current FDOT methodology uses only geometric factors, these can be

specified and a value for capacity is estimated before the work zone is built. With the

new models, the engineers should obtain the data for truck percentages and decide on the

location of the upstream warning sign and rubbernecking factors. When inputting the

values for the lane distributions and speeds, however, the user should be aware of several

issues. First, the speeds and lane distributions are the values for the work zone once it is

already in place. These are not the values for the regular operating conditions observed

with no work zones in place. Second, the models were developed based on simulated

data that ensured breakdown and the creation of a bottleneck at the location of the lane

closure. Therefore, the capacity estimates resulting from the models are for operating

conditions where a queue is formed at the start of the lane closure on the through lane(s).

Finally, the lane distributions and speed values input into the models for capacity

estimation must be maintained when constructing the work zone. Otherwise, the capacity

values estimated by the models will not be observed in the field operations. For example,

if a Lane 1 average speed of 30 mph is used when estimating capacity, the speed for that

link of actual roadway should be enforced at 30 mph for the capacity estimate to

replicated in the field. Speed limit signs and law enforcement can help to encourage the

desired traffic stream behavior. The same holds true for the lane distributions inputs. If






67


the models are applied with a Lane 1 vehicular distribution of 10%, for example, then

that value should be enforced in the field as well. Signs encouraging early merging or

even the use of additional barriers can help achieve the desired distribution of vehicles.














CHAPTER 5
CONCLUSIONS AND RECOMMENDATIONS

This section identifies the primary results and conclusions from the analyses

presented in this thesis. The effects of the model factors on work zone capacity are

summarized and discussed. In addition, the reasons for the differences in capacity

estimation between models and between lane closure configurations are outlined. The

models developed are only one step toward a full understanding of the effects of specific

factors on work zone capacity. For this reason, recommendations for further

investigation are also presented following the conclusions.

Conclusions

There are several factors that were identified to have significant effects on the

capacity of a temporary freeway work zone lane closure. The percentage of trucks

present in the traffic stream and the rubbernecking factor both decrease the capacity of

the lane closure. The rubbernecking factor has a greater effect on capacity reduction than

the presence of trucks-represented by the larger (more negative) coefficients for this

factor. Therefore, when considering lane closure schedules during peak hour traffic (high

volumes of vehicles), times of day (or night) should be avoided when high percentages of

trucks in the traffic stream are present. In addition, consideration should be given to the

quantity of workers and presence of heavy equipment and/or law enforcement vehicles in

the work zone. A large presence of a combination of these factors will contribute to a

high rubbernecking factor, thus reducing capacity further. To be able to correlate a









specific rubbernecking factor with a level of worker/equipment presence, field data for

this variable should be collected and analyzed.

The average of speeds of vehicles directly upstream of the work zone warning sign

had varying effects by lane. As noted in CHAPTER 4: MODEL DEVELOPMENT, the

speeds of lane 2 are correlated to those in lane 1, and the speeds in lane 3 are correlated to

those in lane 2 for congested conditions. A higher capacity is observed with higher

speeds in the to-be through lanes, and a lower capacity with higher speeds in the to-be

closed lanes. As a result, in order to increase the efficiency of a work zone lane closure,

higher speeds-in the range of 25 to 45 mph-should be maintained (upstream and

downstream of the warning sign) through the lanes that are not closed.

The model results also show the effect of vehicle lane distributions downstream of

the location of the warning sign. There is a strong reduction in capacity if a high fraction

of vehicles are present in the to-be closed lane(s). This is an important conclusion when

considering traffic management upstream of a lane closure on a freeway. A work zone

will operate much more smoothly-and thus have a greater capacity-if vehicles are

encouraged (or required) to merge into the through lane at a greater distance upstream

from the location of the warning sign.

The location of the upstream warning sign increases capacity throughput with an

increasing warning distance for all lane closure configurations. In all models, this

variable is included both as a stand-alone variable and as an interaction term. As an

interaction term, however, it's positive effect is not greater than the negative effect of the

variable with which it's interacting, so the net effect of the variables together is a

decrease in capacity. These interaction variables included the speeds of vehicles in link









(5,6) (upstream of the warning sign), the speeds of vehicles in link (6,7), and the lane

distributions of vehicles in link (6,7). As a result, a lane closure configuration will have

greater capacity throughput if advance warning is given to the vehicles in the traffic

stream.

Recommendations

Strong relationships between work zone capacity and upstream speeds and

upstream lane distributions were identified and presented in model form in this report.

The relative differences in capacity provided by different input traffic stream scenarios

are valuable but do not represent actual field capacities. Presented in this section are

recommendations to calibrate the existing models and to conduct further research in the

direction of early merge traffic management strategies.

Calibration

Between lane closure configurations, there are expected differences in capacity

results for the same input values into the models (see APPENDIX E). The variables have

the same effect on capacity (increase or decrease), but the relative effect (value of

coefficient) of each factor is different. This relative difference is valuable information

and can be used for capacity comparisons for different inputs. Even more valuable is the

ability to estimate actual capacity values for the different lane closure configurations. In

order to achieve actual capacity estimates, however, field data for each scenario must be

collected to accurately calibrate the models so that it replicates actual traffic stream

conditions.

The way in which vehicles behave on a freeway and with a lane closure present is

likely not the same as the default values of a simulator for this behavior. As a result, field

data for vehicular lane changing behavior and driving speeds is fundamental to model









calibration. Car-following parameters can also be adjusted within the software, so this

data will also have some impact on the actual capacity values.

In addition to driver behavior, the rubbernecking factor requires calibration. For

each field data collection scenario, information should be collected regarding the

presence of additional obstructions or congestion within the work zone (degree of

presence of law enforcement, heavy equipment, and workers). The data can be modeled

and a table can be developed that correlates a definite rubbernecking factor with a

specified scenario of field conditions. If there are many variables that affect the

rubbernecking factor, then a full model can be used in which the user specifies the inputs

and a rubbernecking factor is estimated. These factors will contribute to a reduction in

capacity represented by the rubbernecking factor in the models. Thus, the field data will

lead to a rubbernecking range defined to specific field conditions.

Without calibration, the output values reported by the models will be

approximately 10 to 20 percent too high, as typical work zone base capacities range from

1400 to 1700 passenger cars per hour per lane. The default driver behavior values are the

primary cause for this overestimate. These default values applied by the software

CORSIM .1 are chosen to maximize the flow through any facility, and any modification

will lead to a decrease in the output values (Schnell and Aktan, 2001).

Future Research and Applications

The effects of traffic stream speeds and vehicle lane distributions on capacity can

be considered in the selection of work zone traffic management strategies for lane

closures. With these results, further research is recommended with each of the scenarios

in order to determine their effectiveness as recommendations for management strategies.

The models give relative capacity estimates for different inputs, so it is a simple task to









identify the optimal flow conditions that maximize the capacity throughput. However, it

is a more difficult task to implement these optimized conditions in the field. An

important work zone consideration that is ignored by the models is safety, for example.

High speeds may be ideal but not feasible. A high fraction of vehicles in the through lane

will increase capacity, but this also leads to more lane changes far distances upstream of

the work zone and thus more of a possibility of accidents due to the higher operating

speeds at that location. In addition to safety concerns, there also exist implementation

difficulties with enforcement of specific conditions. If a predicted capacity is estimated

based on a desired traffic stream speed of 30 mph, for example, then some kind of

enforcement is required to ensure that this is the speed occurring in the field. The same

difficulty exists with the enforcement of a desired lane distribution, when estimating a

capacity value for a lane configuration.

The results of the lane distribution effects on capacity imply that an early merge

strategy is effective in increasing the flow of the traffic stream through a work zone.

However, this may only be effective (or possible) when there are few trucks, a low

rubbernecking factor, and a sign location greater than 0.5 miles, for example. The

effectiveness of an early merge has been investigated in some states, and future research

will allow a clearer understanding of when to use this strategy.

The goal of using an early merge strategy is to place as many vehicles as possible

into the through lane far distances upstream of the work zone. The possibility to model

this scenario with CORSIM 5.1 is limited; the work zone sign leads vehicles to merge,

but the bottleneck is still created at the point of the lane closure, as would be expected in

actual conditions. With CORSIM 6.0 (not released in its full version until the end of









April 2006), at the point of an incident, vehicles begin merging very aggressively at the

location of the incident warning sign. This way, the bottleneck forms around the sign

location, not at the location of the lane closure. The aggressive merging movements and

the creation of the bottleneck around the warning sign is intended to maximize the

throughput capacity in the work zone (see APPENDIX C for CORSIM 5.1 and 6.0 output

comparisons for the same input file). Therefore, even though this behavior is not

expected in field conditions, CORSIM 6.0 can be used to vary the locations of the

bottleneck formations upstream of the work zone, effectively simulating an early merge

scenario. CORSIM 6.0 places over 95% of the vehicles in the through lane just before the

lane closure location, whereas CORSIM 5.1, depending on the flow rate and truck

percentage, places anywhere from 50% to 90% in the through lane (see APPENDIX C

for output data from each version). Because the goal of the research, in part, is to verify

the relationship between the upstream distance of the warning sign and the work-zone

throughput capacity, the algorithm used in CORSIM 6.0 would skew these results. For

future early merge research, however, this may prove to be a valuable tool.

As stated previously in this thesis, simulation modeling cannot provide as much

information as field data collection. The relative capacities provided by the models,

however, provide valuable information to a user interested in optimizing the efficiency of

a lane closure operation. The speeds and lane distributions upstream of a warning sign

have an effect on capacity throughput and this information can be used in scheduling and

managing a temporary freeway lane closure.

















APPENDIX A
MODEL DEVELOPMENT RELATIONSHIPS FOR A 3-to-2 LANE CLOSURE
CONFIGURATION





Work Zone Capacity vs. Upstream Sign Location


2400


Z 2200

. 2000


S1800
o
Cr

o 1600
a)
o
N 1400

o
E 1200


1000


I






e




I I I


0 0.2 0.4 0.6


0.8 1 1.2 1.4 1.6


Upstream Sign Location (mi)







75



Work Zone Capacity vs. Truck Percentage


2400


- 2200
c
Ic
- 2000


S1800


0100
o
N 1400
o
1200


1000


Work Zone Capacity vs. Rubbernecking Factor


y = -22.082x + 1845.1
R2 = 0.7689





1 e-


10 15 20
Rubbernecking Factor (%)


y =-10.44x + 1655
R2 = 0.1085












0 5 10 15 20 2
Truck Percentage


2400

2200

2000

1800

1600

1400

1200

1000







76



Work Zone Capacity vs. Lane Changes in (6,7)


0 200 400 600 800 1000 1200


Lane Changes in (6,7)


Lane Changes in Link (6,7) vs. Length of Link (6,7)


y = 436.49x + 210.61
R2 = 0.4089


1^ ^


0 0.2 0.4 0.6


0.8 1 1.2 1.4 1.6


Length of Link (6,7) (mi)


2400


E 2200
I-

C 2000


- 1800
0

o 1600
o
N 1400

o
9 1200


1000


1400


1400


1200
-c
= 1000


I 800
O
S600
c

S400
I-
a)
E 200
Z







77



Work Zone Capacity vs. Speed in link (5,6)


a AL 0


AOL
AM% A &M0 AAA 1

MMA 00 AAA on
a OL do 40 ANA 0 0


10 20 30 40 50 60 70

Speed in link (5,6) (mph)

Lane 2 A Lane 3 Linear (Lane 2) Linear (Lane 1) Linear (Lane 3)


Work Zone Capacity vs. Speed in link (6,7)


y = 31.454x + 1266.4
R2 = 0.6739 /
II h* '


P A, "Z~--,Xy = 39.235x + 1025.9,
SR2 =0.8329 y = 11./x + 1266.3
'" / *R2 = 0.5726
*#/0
rr^ *i^^' ______
^^S*+ ^ m*
*^rT
;r*3 *


10 20 30 40 50 60 70

Speed in link (6,7) (mph)

Lane 2 A Lane 3 Linear (Lane 2) Linear (Lane 1) Linear (Lane 3)


2400

2200

2000

1800

1600

1400

1200

1000


0



* Lane 1 *


2600

2400

2200

2000

1800

1600

1400

1200

1000


* Lane 1 *







78



Work Zone Capacity vs. Lane Distributions


240

220

200

180

160

140

120

100


0 I
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
Upstream Sign Location (mi)

* Lane 1 Lane 2 A Lane 3 Linear (Lane 3) Linear (Lane 2) Linear (Lane 1)


0


0 A
0


0 A
y =-2189.4x +2121.2 1

0 R2 = 0,2092 4$ 4,o

0


0 -

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
Lane Distributions of Lane 1 (closed lane)

Link (6,7) Link (5,6) A Link (4,5)
Linear (Link (6,7)) Linear (Link (4,5)) Linear (Link (5,6))





Lane Distributions of Link (6,7) vs. Upstream Sign Location






t


- -


0.6


S0.5
c
S0.4
o
0
.2 0.3


" 0.2
.M
a
" 0.1
-1







79



Work Zone Capacity vs. Lane 1 Distribution (6,7) and Sign
Distance


**



y = -971.7x + 1711.6
R2 = 0.192


0.1 0.2 0.3 0.4
Lane 1 Distribution (6,7) Sign Distance (mi)


Work Zone Capacity vs. Speed in Lane 1 (6,7) and Sign
Distance





y = 5.0755x + 1309.3
R2 = 0.2835





0 20 40 60 80 100 12
Speed in Lane 1 (6,7) Sign Distance (mph*mi)


2500


2000


1500


1000


500


2500


2000


1500


1000


500







80



Speeds in Link (5,6)


y = 0.0087X2 + 0.4493x + 0.6699
R2 = 0.9937
R = 0.9968 _









0 10 20 30 40 50 60 7
Lane 1 (mph)



Speeds in Link (6,7)


10 20


30 40
Lane 1 (mph)
















APPENDIX B
MODEL DEVELOPMENT RELATIONSHIPS FOR A 3-TO-i LANE CLOSURE
CONFIGURATION





Work Zone Capacity vs. Upstream Sign Location


2600

- 2400

2200

2000

1800

0
^ 1600
N
S1400
o
S1200

1000


$ $
0






y= 12.236x+ 1579.5 s
R2 = 0.0003
I **
*



I I I II I


0 0.2


0.6 0.8


1 1.2 1.4 1.6


Upstream Sign Location (mi)







82



Work Zone Capacity vs. Truck Percentage


2600

2400

2200

2000

1800

1600

1400

1200

1000


10 15 20
Rubbernecking Factor (%)


y = -14.614x + 1737.8
R2 = 0.1542












0 5 10 15 20 2
Truck Percentage






Work Zone Capacity vs. Rubbernecking Factor






y = -24.29x + 1915.6
R2 = 0.6745






,


2600

2400

2200

2000

1800

1600

1400

1200

1000







83



Work Zone Capacity vs. Lane Changes in (6,7)


100 200 300 400 500 600


Lane Changes in (6,7)


Lane Changes in Link (6,7) vs. Length of Link (6,7)


*


y = 107.96x + 415.42
R2 = 0.417


0 0.2


0.4 0.6 0.8 1

Length of Link (6,7) (mi)


1.2 1.4 1.6


2600

- 2400
I-
C 2200
a>
2000


0
. 1800

D 1600
0
N
. 1400
o
S1200


1000


700


600
c
c 500


c 400


D 300
c

- 200
o
I-

E 100
z







84




Work Zone Capacity vs. Speed in link (5,6)


* Lanel Lane 2


2600

Z 2400
I-
I 2200

2000

S1800
o

0
a 1600
c
NO

0
N 1400

1200

1000


Work Zone Capacity vs. Speed in link (6,7)


2600

Z 2400
c-
I 2200

2000

0
( 1800

a 1600
c
S1400

E 1200

1000


y = 25.667x + 1308.4
R2 = 0.7924 A 4* *
A* **

y = 10.211x + 1283.4
^ R2 = 0.5011







y = 3.7751x + 1420.7
R2 = 0.0912
II


0 10 20 30 40 50 60 70

Speed in link (6,7) (mph)
* Lane 1 Lane 2 A Lane 3 -Linear (Lane 3) Linear (Lane 2) Linear (Lane 1)


20 30 40 50 60 70

Speed in link (5,6) (mph)
Lane 3 Linear (Lane 3) Linear (Lane 2) Linear (Lanel)


A '%I
4k A+
Aaim A AS
,







85




Work Zone Capacity vs. Lane Distributions


2600


r 2400

2200

2000

C 1800A A A--
y=-1440.1x + 2142.4
0 0




q 1200

1000
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
Lane Distributions of Lanes 1 and 2 (closed lanes)

SLink(4,5) Link(5,6) Link (6,7)
Linear (Link (6,7)) Linear (Link (5,6)) Linear (Link (4,5))


Lane Distributions of Link (6,7) vs. Upstream Sign Location


1 -
A

0.8 i


0.6


0.4 I
II

0.2 I


0 -
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.
fl o,


* Lane 1 Lane2 A Lane3 -


Upstream Sign Location (mi)

Linear (Lane 3) Linear (Lane 2) Linear (Lane 1)







86



Work Zone Capacity vs. Lane 1 Distribution (6,7) and Sign
Distance

2500


- 2000


> 1500 --

0.
1000
y =-4618.4x + 1724
N R= 0.1779
1 500
o


0
0 0.02 0.04 0.06 0.08 0.1
Lane 1 Distribution (6,7) Sign Distance (mi)






Work Zone Capacity vs. Lane 2 Distribution (6,7) and Sign
Distance

2500
*

2000


S1500 -
0
U-
- 1000
N y =-1157.8x + 1964.8
R2 = 0.3614
S500


0 -
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
Lane 2 Distribution (6,7) Sign Distance (mi)








87




Work Zone Capacity vs. Speed in Lane 2 (6,7) and Sign
Distance
















y = 4.7924x + 1420.5
R2 = 0.2318


Speed in Lane 2 (6,7) Sign Distance (mi)








Speeds in Link (5,6)


Lane 2 (mph)


2500



' 2000



r 1500

(0



0
N
1 500
0


70


60


50


E40


r 30
(I0

20


y = 0.0529x2 3.8355x + 93.881
R2 = 0.9475
R = 0.9734







88



Speeds in Link (6,7)


50



40



3 30 2
3 y = 0.0136x -0.5144x +8.1082
c) R2 = 0.8497
,- R= 0.9218
S20




0
o ------------------

0 10 20 30 40 50 60 70
Lane 2 (mph)














APPENDIX C
SAMPLE OUTPUT FILES FROM CORSIM 5.1 AND CORSIM 6.0

The outputs shown below illustrate the differences between two identical input

scenarios simulated by two different versions of CORSIM. The two versions being

compared are the current one being used in this report, CORSIM 5.1, and a Beta version

to be released in April 2006, CORSIM 6.0.

Sample Output from CORSIM 5.1

On the following pages is a sample output for the following scenario (base case):

* 2-to-1 Lane Closure
* Work Zone Length at 0.5 miles
* Input Lane Distributions at 50/50
* Sign Distance at 0.5 miles
* Truck Percentage at 0%
* Rubbernecking Factor at 0%











90




INPUT FILE NAME: S:\Students\DiegoArguea\Research\CORSIM\2 to 1 La
RUN DATE : 01/16/06


TTTTTTTTTTT
TTTTTTTTTTT
TTTTTTTTTTT
TTT
TTT
TTT
TTT
TTT
TTT
TTT
TTT
TTT


RRRRRRRRR
RRRRRRRRRR
RRRRRRRRRRR
RRR RRR
RRR RRR
RRRRRRRRRRR
RRRRRRRRRR
RRR RRR
RRR RRR
RRR RRR
RRR RRR
RRR RRR


AAAAAA
AAAAAAAA
AAAAAAAAAAA
AAA AAA
AAA AAA
AAAAAAAAAAA
AAAAAAAAAA
AAA AAA
AAA AAA
AAA AAA
AAA AAA
AAA AAA


VERSION 5.1 (BUILD #301)
RPRLRAS DnAT FBRPUARY 2003


TRAF SIMULATION MODEL

DEVELOPED FOR

U. S. DEPARTMENT OF TRANSPORTATION
FEDERAL HIGHWAY ADMINISTRATION
FHWA OFFICE OF OPERATIONS RESEARCH, DEVELOPMENT AND TECHNOLOGY

1 CARD FILE LIST
OSEQ.# : ---+----1---+----2---+--- 3------- 4 --+ 5 --+-6 --+--7----- -8


1 0 0 20 7981

60
0 0 0 0 0
7 8 26400 2
8 9 26400 2
98002 26400 2
1 2 0 2
6 7 26400 2
5 6 1500 2
2 3 26400 2
3 4 1500 2
4 5181800 2
7 0 0 0 11065 1 1
8 0 0 0 11055
9 0 0 0 11065 1 1
1 0 0 0 11065 1 1
6 0 0 0 11065 1 1
5 0 0 0 11065 1 1
2 0 0 0 11065 1 1
3 0 0 0 11065 1 1
4 0 0 0 11065 1 1
7 8 100
8 9 100
98002 100
1 2 100
6 7 100
5 6 100
2 3 100
3 4 100
4 5 100
8 2
12400 0 0 100

35000 0
0 0
33280 0
28000 0


10 102005 0 1
0000 0 8 700 7781 7581 2
3
4
0 0 0 0 0 5
1 19
1 19
1 19
1 19
1 19
1 19
1 19
1 19
1 19
100 20
1320 100 20
100 20
20
100 20
75 100 20
100 20
75 100 20
100 20
25
25
25
25
25
25
25
25
25
0 2640 099999 0 2640 29
50 50 50
170
195
195
195
195


FFFFFFFFFF
FFFFFFFFFF
FFFFFFFFF
FFF
FFF
FFFFFFF
FFFFFFF
FFF
FFF
FFF
FFF
FFF


1 :
2 :
3 : 900
4 :
5 : 0
6 : 6
7 : 7
8 : 8
9 :8001
10 : 5
11 : 4
12 : 1
13 : 2
14 : 3
15 : 6
16 : 7
17 : 8
18 :8001
19 : 5
20 : 4
21 : 1
22 : 2
23 : 3
24 : 6
25 : 7
26 : 8
27 :8001
28 : 5
29 : 4
30 : 1
31 : 2
32 : 3
33 : 7
34 :8001
35 : 0
36 :8002
37 :8001
38 : 9
39 : 7