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Development of an Arterial Link Travel Time Model with Consideration of Mid-Block Delays


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DEVELOPMENT OF AN ARTERIAL LI NK TRAVEL TIME MODEL WITH CONSIDERATION OF MID-BLOCK DELAYS By ALEXANDRA KONDYLI A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2005

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Copyright 2005 by ALEXANDRA KONDYLI

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This document is dedicated to the graduate students of the University of Florida.

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iv ACKNOWLEDGMENTS The author would like to thank her graduate advisor, Dr. Lily Elefteriadou of the University of Florida for her insights a nd guidance throughout this thesis and her valuable support. The author wishes to thank the remaining members of the thesis committee, Dr. Scott Washburn and Dr. Ruth Steiner, fo r their assistance a nd their advices. Finally, the author expresses her sincere th anks to TransAssociates Consultant Firm at State College, PA, for their as sistance during the data collection.

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v TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES............................................................................................................vii LIST OF FIGURES.........................................................................................................viii ABSTRACT....................................................................................................................... ..x CHAPTER 1 INTRODUCTION........................................................................................................1 Background...................................................................................................................1 Problem Statement........................................................................................................1 Objectives..................................................................................................................... 2 2 LITERATURE REVIEW.............................................................................................5 Highway Capacity Manual...........................................................................................5 Right Turns from Arterial.............................................................................................8 Left Turns from Arterial.............................................................................................13 Access Management and Driveway Spacing..............................................................14 The Significance of Mid-Block Effects......................................................................16 Summary of the Literature Review.............................................................................17 3 METHODOLOGY.....................................................................................................19 Data Collection...........................................................................................................19 Simulation Model Development and Calibration.......................................................19 Design of Experiments...............................................................................................20 Database Expansion....................................................................................................21 Data Analysis and Formulation of Regression Models..............................................22 4 DATA COLLECTION...............................................................................................25 Data Requirements......................................................................................................25 Description of Study Site s ..........................................................................................26 Data Collection Methods............................................................................................27

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vi 5 SIMULATION MODEL DEVELOPMENT..............................................................31 Simulation Package....................................................................................................31 Model Development...................................................................................................32 Model Calibration.......................................................................................................35 Summary and Conclusions.........................................................................................39 6 ANALYTICAL MODEL DEVELOPMENT.............................................................41 Database Expansion....................................................................................................41 Selection of Simulation Output Performance Measures.............................................44 Database Organization................................................................................................45 Data Analysis..............................................................................................................46 Selection of Candidate Variables................................................................................46 Regression Models......................................................................................................54 Regression Model for Two-La ne One-Way Arterials.........................................59 Regression Model for Two-La ne Two-Way Arterials........................................63 Discussion—Description of Independent Variables...................................................68 Conclusions.................................................................................................................73 7 CONCLUSIONS AND RECOMMENDATIONS.....................................................76 APPENDIX A PHASING—TIMING DIAGRAMS..........................................................................81 B TURNING MOVEMENT AND LOOP DETECTOR DATA...................................88 C TRAVEL TIME STUDY...........................................................................................97 D EXAMPLE PROBLEM............................................................................................100 LIST OF REFERENCES.................................................................................................102 BIOGRAPHICAL SKETCH...........................................................................................104

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vii LIST OF TABLES Table page 4-1 Determination of number of vehicle runs based on field measured travel time.......28 5-1 Calibration parameters for Park Avenue midday and p.m. models.........................37 5-2 Calibration parameters for Sparks p.m. model.........................................................37 5-3 Calibration parameters for Pugh midday and p.m. models......................................37 5-4 Field measured vs. simulation travel time after calibration.....................................38 6-1 Two-lane two-way simulation mode l inputs for database expansion......................43 6-2 Two-lane one-way simulation model i nputs for database expansion. First group of scenarios (from Beaver Av enue at Sparks Street)...............................................43 6-3 Two-lane one-way simulation model inputs for database expansion. Second group of scenarios (from Beaver Avenue at Pugh Street)........................................44 6-4 Selected performance measur es extracted from CORSIM......................................45 6-5 Mid-block delay equations for tw o-lane one-way uncongested conditions.............60 6-6 Mid-block delay equations for tw o-lane one-way congested conditions.................62 6-7 Mid-block delay equations for tw o-lane two-way uncongested conditions.............64 6-8 Mid-block delay equations for tw o-lane two-way congested conditions.................66

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viii LIST OF FIGURES Figure page 1-1 Illustration of midblock delay phenomena...............................................................3 4-1 Two-lane two-way arterial (Park Avenue)...............................................................26 4-2 Two-lane one-way arterial (Beaver Avenue)...........................................................27 4-3 First two-lane one-way arterial link (Beaver Avenue between Sparks St and Atherton St)..............................................................................................................29 4-4 Second two-lane one-way arterial li nk (Beaver Avenue between Pugh St and Garner St).................................................................................................................29 4-5 Two successive two-lane two-way arte rial links (Park Avenue between N. Atherton St and N. Allen Rd and Park Avenue between N. Allen Rd and Shortlidge Rd)..........................................................................................................30 6-1 Sketch of variable Vdr/N........................................................................................48 6-2 Sketch of variable Vart/N........................................................................................50 6-3 Measurement of discharge to demand ratio.............................................................54 6-4 Dataplot of mid-block de lay vs. discharge to demand ratio for two-lane one-way arterials.....................................................................................................................5 6 6-5 Dataplot of mid-block de lay vs. discharge to demand ratio for two-lane two-way arterials. (A) Mid-block delay and discharge to demand ratio for low volume level. (B) Mid-block delay and discharg e to demand ratio for high volume level...57 6-7 Relationship between mid-block delay and independent variables for the twolane one-way uncongested model. (A) Average arterial turning volume, Vart/N. (B) Average driveway turning volume, Vdr/N.......................................................61 6-8 Relationship between mid-block delay and independent variables for the twolane one-way congested model. (A) Average arterial turning volume, Vart/N. (B) Average driveway turning volume to demand ratio, ( Vdr/D)/N......................63

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ix 6-9 Relationship between mid-block delay and independent variables for the twolane two-way uncongested model. (A) Inte raction between total arterial leftturning volume and total ar terial opposing volume, ( Vart-L* Vopp)/104. (B) Number of driveways per 1000 ft, Ndr.....................................................................65 6-10 Relationship between mid-block delay and independent variables for the twolane two-way congested model. (A) Arterial volume to demand ratio, Vup/Demand. (B) Interaction between total arterial left-turning volume and total arterial opposing volume, ( Vart-L* Vopp)/104........................................................67

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x Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science DEVELOPMENT OF AN ARTERIAL LI NK TRAVEL TIME MODEL WITH CONSIDERATION OF MID-BLOCK DELAYS By Alexandra Kondyli December 2005 Chair: Ageliki Elefteriadou Major Department: Civil and Coastal Engineering This thesis presents analytical models for estimating arterial travel time with consideration of delays at mid-block locati ons. The mid-block delays are defined as the delays that through drivers expe rience due to turning maneuver s of either the other major stream vehicles ahead that exit from the arte rial or the minor stream vehicles that enter the arterial. These delays typically occur at the intersections with driveways. Volume and travel time data are collected in two-lane two-way and two-lane one -way arterials. These data are used for the development of simulati on models and the expans ion of the database through simulation. The generated data are us ed for the development of analytical equations of mid-block delay through regres sion. The final regressi on equations provide estimation of arterial mid-block delay depe nding on the conditions that the arterial operates (congested – uncongested conditions).

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1 CHAPTER 1 INTRODUCTION Background Arterial roadways are designed to provi de both accessibility and mobility to the users. Those two contradictory f unctions define the level of c ontrol of the arterials. There can be variable combinations of these, w ith respect to the land use and roadside development. According to the Highway Capacity Manual [HCM 2000] (Transportation Research Board, 2000) and the Policy on Ge ometric Design of Highways and Streets (AASHTO, 2001) arterials can be designated as high-speed, suburban, intermediate and urban based on their design and as principa l or minor, based on their functionality. Arterials play a very important role in the roadway system. A quantitative assessment of the factors that affect mid-block performance on urban arterial streets is impor tant for the determination of the total link delay, as it is perceived by the drivers. The estimation of mi d-block arterial delay is important for the following reasons. First, it provides a comp lete delay estimation procedure that is partially and not wholly dependent on the intersection delay. Sec ond, it is closer to reality, and it can assess the significance of the various mid-block phenomena into the overall arterial link performance. Problem Statement Several traffic studies have focused on the operational characteristics of the arterial streets and their capacity, indi cating that the estimation/pred iction of delays and travel times is important information for the users. However, no study has developed a model

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2 for estimating travel time on arterial links by including various parameters of arterial mid-block performance. Objectives The main objective of this research is to develop an analytical model that will predict arterial link travel time considering the delays of mid-block phenomena. The midblock delays are defined as the delays that through drivers experience due to turning maneuvers of either the other major stream vehi cles ahead that exit from the arterial or the minor stream vehicles that enter the ar terial. These delays typically occur at the intersections with driveways. The model will also include delays due to parking activities and bus stops. Figure 1 – 1 illustrates the mid-block phenomena that contribute to the increase of arterial travel time and that are e xplored in this researc h. The upper part of the figure shows a vehicle from the arterial (vehic le 1) reducing speed to make a right turn on driveway #1. This maneuver will force the onc oming vehicle to decelerate as well in order to maintain a safe distance. Similarly, ve hicle 2 enters the arterial with lower speed than the oncoming vehicle. This will possibl y cause the oncoming vehicle to decelerate. The lower part of Figure 1 – 1 illustrates an arte rial vehicle (vehicle 3) about to perform a parking maneuver and by doing so, it will decelerate and cause the following vehicle to decelerate as well. Lastly, as vehicle 4 perf orms a left-turn maneuver from the driveway onto the arterial it can force oncoming vehicl es from both directions to reduce their speeds to avoid a collision.

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3 Vehicle turning right onto the drivewayDRIVEWAY 1 Oncoming vehicle decelerates Oncoming vehicle decelerates Vehicle turning right onto the arterialDRIVEWAY 2 DRIVEWAY 3Vehicle performes parking maneuver Oncoming vehicle decelerates Oncoming vehicles from both directions decelerate parking bay DRIVEWAY 4Vehicle turning left onto the arterial 2 1 Figure 1-1 Illustration of mid-block delay phenomena The tasks of this research are as follows: Critically review all pertinen t literature that involves ar terial travel time estimation as a function of the arterial turning maneuvers. Conduct field measurements of volumes and travel times in urban arterials to be used for the development and calibrati on of the simulation models. The study streets are two-lane, two-way a nd two-lane, one-way arterials. Generate new data according to a prespecified design of experiments. Use the expanded dataset for the developmen t of analytical mode ls using regression analysis. The final models express the mi d-block delay as a function of various parameters such as the arterial through and turning volume, the driveway volume, the arterial degree of saturation and others. The next chapter of this thesis summarizes the literature review. The third chapter presents the methodology to be followed in this research. The fourth chapter presents the data collection effort and the arterials under study. The fifth chapter discusses the simulation model development and the model calibration. The sixth ch apter describes the experimental design and the development of the analytical models through regression.

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4 The last chapter discusses the findings of th is thesis and presents recommendations for further research.

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5 CHAPTER 2 LITERATURE REVIEW The literature review includes several components. First, th e respective chapters of the HCM 2000 have been reviewed. Additionally studies that estimate delays due to right or left turn maneuvers ar e presented. Also, studies that report the effect of driveway spacing as a result of access management tec hniques to the overall ar terial operations are also reviewed. Finally, literature that s uggests that arterial delay models should incorporate the effects of mid-block phenomena is presented. Highway Capacity Manual The HCM 2000 provides methodologies for the evaluation of urban streets by determining arterial link Level of Serv ice and computing th e intersection delay. Information related to the urban stre ets methodology can be found in Chapter 15. The first step of the methodology of urban streets is to define the arterial’s classification, which is based on both the design and functional characteristics. The design characteristics of the ar terials are related to the arterial posted speed limit, the signal density, the driveway/ access point dens ity and other design features. Based on these characteristics, the arteri al street classification is de termined, which, in the next step, affects the arterial running speed, along with other paramete rs, such as the free-flow speed, and length of the segment. The arterial running speed is very important for the LOS analysis, because when combined with th e intersection control delay, it is used for the calculation of the ar terial through-vehicle travel speed for the segment or for the entire link under considerat ion. According to the travel speed and the arterial

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6 classification, the level of service of the ar terial segment is calculated. Evaluating the arterial analysis methodology of the HCM 2000 (C hapter 15) it is conc luded that it does not explicitly account for phenomena such as driveway density, cross-street traffic blocking the through movement, and arterial turning maneuvers that impede the oncoming vehicles. This chapter mentions that the arterial running time is affected by the presence of parking; however the me thodology does not quantify this effect. The HCM 2000 also includes methods for es timating delays at both signalized and unsignalized intersections. These methods are widely used and are accepted among engineers. For signalized intersections, the HCM 2000 (Chapter 16) provides a procedure for estimating the delay and the level of serv ice. The methodology cal culates the control delay, which includes movements at lower speed s and stops at intersection approaches as vehicles move up in a queue or slow down at an intersection. The delay formula accounts for uniform arrivals, for random arrivals and oversaturated queues and for initial queues. The formula that estimates the delay assuming uniform arrivals is based on the Webster’s formula and it is widely accepted and used in practice. The presence of buses and parking freque ncy is taken into consideration for the saturation flow rate methodology (HCM 2000, Chap ter 16). For the determination of the saturation flow rate two adju stment factors are introduced; the bus-blockage adjustment factor and the adjustment factor for existe nce of a parking lane and parking activity adjacent to the lane groups. However, these f actors are considered to affect the traffic stream only within 250 ft from the signal and not at mid-block locations.

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7 For two-way stop-controlled intersections a nd T-intersections with a single minor street approach, the HCM 2000 (Chapter 17) provides methods for estimating delays and levels of service for the minor approaches caused by the priority a pproaches. The delays are calculated for minor approach vehicles th at are crossing the majo r street, turning right on the major street, turning left on the major st reet, and turning left from the major street, depending on the minor street volume, major street volume, and follow-up time. Delays are also calculated for the ma jor street through or right turn vehicles that are impeded by the left turning vehicles when there is a sh ared lane on the majorstreet approach and no exclusive left-turn pockets are provided. Th e HCM 2000 also notes that these delays usually ‘have very small effect because th e major street usually provides enough space for the blocked (through) vehicle to sneak by or bypass the le ft-turning vehicle’. Based on the HCM 2000, it can be concluded th at a more detailed analysis of the segment (on the level of individual major-mi nor street intersection) would require the application of the unsigna lized intersections methodol ogy, which, according to the aforementioned, provides the delays of the minor approaches and only of the major approach left turn. The HCM 2000 does not provide a methodology for calculating the delays that the major street vehicles may incur due to right-turn maneuvers from the arterial, right-turn maneuvers and left turn maneuvers from the minor street, and such delays are not taken into consideration for th e analysis of the entire arterial segment. More specifically, the HCM 2000 mentions that “. . in special cases, there might be midblock delays caused by vehicle stops at pede strian crosswalks, or other delays caused by bus stops or driveways.” These mid-block dela ys can be directly incorporated into the methodology provided that the user already has an esti mate of their value.

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8 Right Turns from Arterial Two studies that were conducted in 1970 are focused on the impact of right-turn vehicles to the delay of the through vehicles. In the first study, Stove r et al. (1970) used simulation to quantify the effect of right-t urning vehicles. For th e calibration of the model, deceleration and right-turn speed data from aerial time-lapse photographs were used. The simulation analysis considered the effect of major-street flow rate, proportion of right-turns, and driveway entrance speed. The authors’ simulation results show that through vehicle delays increased with increasing major-street flow rate, and are higher under low right-turning speed, but their findings may not be valid nowadays due to changes in the driving behavior. The second study was conducted by Alex ander (1970). He observed traffic operations at seven mostly urban intersections on two-lane two-way highways in Indiana to determine the delay to through traffic due to right-turning vehicles. The following equation was developed based on a regression an alysis of the field-measured delay and flow rates: Dt = -219 + 2.OSQr + 0.37Q + 4.33u (2 – 1) Where: Dt = total through vehicle delay, Qr = right-turn flow rate, vph, Q = major-street flow rate, vph, u = major-street running speed, m/s. The R2 for this equation was reported to be 0.76. The author’s findings are that the total delay caused by the right-turn vehicles is related to the volume of the right-turn maneuver, the volume and the speed of the through vehicles. McShane (1995) used the TRAF/NETSIM ( 1995) simulation mode l to quantify the effects of right-turn maneuvers on through ve hicle travel speed. The author took into

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9 consideration the driveway flow rate, drivew ay spacing, major-street flow rate, driveway location, number of driveways, number of la nes on the major street, and free-flow speed. The computed delays are comparable to those reported by Stover et al. (1970) and Alexander (1970), although, an ex act comparison is not possibl e due to different ranges of major-street flow rate and speed. Bonneson (1998) developed a deterministic model for predicting the delays to major-street through drivers due to a right-turn maneuver fr om the outside through lane of the major street. The author did not cons ider the number of through lanes on the major street or the distribution of its flow rate to these lanes. The proposed model requires as input the flow rate in the outside through lane and it is formed for both single and multilane approaches where right-turns are assumed to occur from the outside through traffic lane. The author modeled the delay of the through vehicles that starts with the right-turn maneuver of one vehicle and ends with anothe r right-turn maneuver. In this model it is assumed that lane changing by through drivers to avoid a slowing right-turn vehicle is negligible during the event, due to the fact that the event has rela tively short duration and the delays are basically because of the acceleration/dece leration process (only a few seconds). The model describes first the delay incurre d by the first throu gh vehicle and then the delay of the following vehicles by repres enting the trajectories of the turning vehicle and the through vehicles, under the assumpti on of low flow conditions (1000 vph/ln), constant running speeds and constant accelera tion/deceleration rates. Bonneson’s desired

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10 turn speed was related to the curb return radius and the driveway width, based on research by Richards (reported by Stover and Koepke, 1988). The formula used is C rtR u 196 0 59 3 (2 – 2) where urt is the right-turn speed and RC is the turn radius. The author also determined the minimum speed and the delay of the first delayed through vehicle. Based on shock wave theo ry, the author developed a procedure for calculating the delay of the following vehicles. The verification of the model entailed co mparison of the proposed model with the findings of other researchers (Alexander, 1970; Stover et al., 1970; McShane, 1995) which yielded overall agreement, as well as a comparison of it to the TRAF/NETSIM model, but no validation with field data wa s conducted. The author’s findings indicate that the through vehicl es’ delay increases w ith increasing flow ra te, increasing majorstreet running speed, with an increase in ri ght-turn vehicles proporti on or a decrease on right-turn speed. It is also shown that the de lay per right-turn vehicle decreases as the proportion of the right-turn vehi cles increases, due to the fa ct that the proportion of through vehicles that are following is smaller. In NCHRP Report 420, Gluck et al. (1999) analyzed type s of access management techniques and their impacts. On the a ssessment of unsignalized access spacing, the authors performed an operat ional analysis for identifyi ng how right turns entering a driveway affect other drivers following in th e same travel lane. These findings are also shown in a paper review of Gluck et al. ( 2000). In their study, information was gathered on the number and percentage of through vehicl es impacted by right turns. The impact lengths of through vehicles impacted were determined, and, influence areas were

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11 computed. Their results were used to quantify the effects of multiple driveways and to develop inputs for establishing unsignaliz ed access spacing guidelines. The field measurements include traffic volumes and imp act characteristics such as the number of incidents caused by the activation of the br ake lights and evasive maneuvers of through vehicles following a right-tu rning vehicle. Moreover, the authors gathered information and computed the following input parameters: The number and percentage of through vehicles in the right lane that were impacted by right-turn-in at a single driveway. The percentage of through vehicles in th e right lane that were impacted by right turnin over a series of driveways. The distances back from a single driv eway entrance that cars began to be impacted—the impact length—and the spatia l distributions of impacted vehicles. The “influence areas” or influence dist ances before (upstream of) a driveway entrance. This involved adding perceptionreaction distance and car length to the impact length. The proportions of through vehicles in th e right lane whose influence lengths extended to or beyond at l east one upstream driveway over a section of road (spillback rate). The variations of spillback rate by roadway operating speed The single driveway analysis was extended to multiple driveways’ analysis through probability analysis. The findings denote a li near relationship between the percent of right lane through vehicles impacted and the right-turn volume, irrespective of speeds. The driveway impact lengths (and infl uence lengths) analysis revealed the relationship between the percenta ge of through vehicles in th e right lane that would be impacted by right-turn traffic for various dist ances form a driveway for each range of right-turn volume.

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12 The analysis also revealed that the infl uence distance increases as speed increases. A relationship between speed, distance from upstream traffic signal and impact length is also established. Another study presented by Wolfe and Pi ro (2003) describes a methodology for determining the delay to through vehicles due to the right turning traffic. The study involves both signalized and uns ignalized intersections, but th e authors eliminated from their study the vehicles that were not under the operating speed due to being in a queue or decelerating at the amber traffic light. The methodology is based on total volume, right lane volume, right turning volume and the difference in the through vehicle opera ting speed and the right turning speed due to geometric constraints. The methodologies that were developed calculated three different forms of delay, such as the total dela y of all through vehicles the delay to traffic in the right lane and the delay to all through vehicles that fo llow a right turning vehicle. The following delay equation was derived based on the total volume, the right lane flow, the right-turning volume, and the algebraic difference between the right-turning vehicle speed and the through operating speed. R T RT RLane total totalu V V V D 97 5 99 4 729 0 352 0 (2 – 3) The right-turning speed was calculated by using an equation of a previous study (Wolfe and Lane, 1999) that correlates the turn ing speed with the tu rning radius of the curb. The authors collected data at 15 intersections, either signalized or unsignalized, with various right curb radii, ranging from 1.6 m to 20 m. The authors determined the speed of the turning vehicles based on the time they required for the maneuver and the respective geometry of the in tersection. Based on their data the following formula was derived.

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13 S 2.2678809 0.451631 R 0.078901 R2 0.007308 R3 0.0001811 R4 (2 – 4) Based on that speed, the authors determined the time difference between the rightturning vehicles that enter th e arterial and the through vehicl es, which holds for the delay that the through vehi cles experience. Left Turns from Arterial Bonneson and Fitts (1999) discussed the dela y on the major street due to vehicles that perform a left-turn maneuve r at two-way stop-controlled intersections. This delay is incurred when major street left-turn de mand exceeds the available storage area and blocks the adjacent through lane (undivided cross section with no left-turn bay). In this situation, the through drivers wi ll merge with vehicles in the adjacent through lane if there is an adequate gap for them to safely merge into, or they will remain in the inside lane until the queue dissipates (if there is no merge opportunity). This paper is part of the NCHRP Project Report 395 (Bonneson and McC oy, 1997) that evaluates the adequacy of midblock left-turn treatments such as TWLTL, raised-curb median and undivided cross section, based on the operational, safety and access effects of these treatments. In this paper the authors combined four models for determining the average delay to through vehicles in a blocked inside lane. The four models include: a lane flow rate model, a merge capacity model, a merge de lay model, and an overflow probability model. The authors also discuss the work of Kyte et al., (1996) who proposed a model to estimate the delays to through vehicles by a ssuming equal distribution of through traffic to lanes and the probability of having a left-turn queue. The lane flow rate model was developed to predict the through ve hicle flow rate in each approach lane just upstream of the left-t urn location, when there is at least one left-

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14 turn vehicle present (for two or more la ne approaches). The distribution of through drivers on the lanes follow the assumption that they will choose the traffic lane that minimizes their travel time (and thus their delay), and this is accomplished by equating the demand-to-saturation flow ratios among th e alternative through lanes. Additionally, the authors developed capacity models for th e inside lane through vehicles, for either non-merge or merge situations. The non-merge s ituations occur when a driver decides to remain to the inside lane until the queue ah ead of him dissipates and does not perform a lane change maneuver. The merge situations take place when the driver merges into the adjacent through lane, instead of waiting in the back of the queue. Lastly, the probability of a left-turn bay overflow is calculated, whic h represents the probability of one or more left-turn vehicles being queued in the inside through lane in an undivided cross section. The combination of the models yields that the delay of the through vehicles increases with left-turn percentage for low to moderate flow rates; however, for high flow rates this is not the case. The authors’ belief is that there may be a left-turn percentage associated with the maximum delay for high fl ow rates such that left-turn percentages higher or lower would yield lower delay. This methodology was verified using the TWLTL-SIM simulation model and it was found th at the two models generally agree, although the proposed model predicts lower delays than the TWLTL-SIM in the ‘low delay’ range. Access Management and Driveway Spacing According to the Access Management Manual, published by the TRB (2003) access management is “the systematic control of the location, spacing, design, and operation of driveways, median openings, in terchanges, and street connections to roadway”. In other words, access management is a tool for providing vehicle access to

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15 the abutting land development, in a way that the traffic safety and transportation efficiency are met in balance. There are several studies that develope d guidelines for selecting the desirable spacing between unsignalized access point s (Stover and Koepke 2002, AASHTO 2001, Gluck et al. 1999, TRB 2003). Thes e guidelines were based on di fferent criteria, such as safety, stopping sight distance, intersection sight distance, functional area, right-turn conflict overlap, influence distan ce and egress capacity (TRB, 2003). Based on a study by S&K Transportation Cons ultants Inc. (2000), as appears in Access Management Manual (TRB, 2003), the re lative crash rates are expected to increase if the driveways’ spacing is reduce d. For example, a decrease of access spacing from 1056 ft to 264 ft would yield crash rate s 2.1 times higher. Another study (Stover and Koepke, 2002) suggests that long spacing betw een driveways is more desirable, since auxiliary lanes can be designed to reduce the conflicts between the arterial through vehicles and the turning ve hicles and provide safety. The AASHTO Green Book (2001) provides su ggestions for stopping sight distance and intersection sight distance, which can be applied as access sp acing criteria. Stopping sight distance is the minimum sight distance re quired to allow driver s to come to a stop. This criterion is very useful for access sp acing guidelines, as there are many potential conflicts between the arterial through a nd turning drivers. AASHTO (2001) provides tables that give the minimum stopping sight distance, depending on the arterial grade. Additionally, intersection sight distance is the minimum sight distance required for a vehicle stopped at an inters ection to enter or cross the major approach. AASHTO (2001)

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16 also provides minimum intersection sight di stances as a function of the major street speed, number of lanes and grade. The functional area criterion (AASHTO, 2001) demonstrates that all access connections such as intersecti ons (signalized or unsignalized ), driveways and arterials, define a functional area upstream of their location. The criter ion dictates that no other connection should be placed w ithin this functional area. The right-turn conflict overlap occurs when the major street through driver has to monitor more than one driveway at a given time. The speed difference between the turning vehicle and the through vehicle define the minimum distance required to reduce collision due to overlappi ng right-turn maneuvers (Stover and Koepke, 2002). The Significance of Mid-Block Effects Lin et al., (2003) developed a model which includes the effect of the vehicles entering an arterial from a cross street. Acco rding to the authors, the total delay on an arterial includes Link Delay and Intersect ion Delay. Link Delay can be caused by two factors. The first factor is the intersection delay, in the sense that a vehicle will slow down while approaching the queue at the inte rsection, hoping that the queue will start to dissipate as soon as he will r each the intersection. In essence, this represents an early deceleration, which does not affect delay. Th e second factor is the increase in flow, which can be questionable, because it has been shown that travel time is not sensitive to link flow, for medium or high flows, due to the metering of the upstream intersections. For this reason, the authors assume that the de lay of the link that is due to the internal link flow can be considered zero. Under th is assumption, the authors approximate the Link Delay with the estimation of only the delay at intersections.

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17 The model is based on discrete Markov chai n properties. The key parameters of the model are the ratio of the overall flow level to the service capacity for the intersection in question, the net turning movement percentages into the arterial from the cross street at the upstream intersection, and the traffic si gnal coordination level with its upstream intersection. As it is seen, the authors acknowledge that th e vehicles that turn into the arterial can produce delays to the through vehicles. The model that is developed, exhibits some desirable properties in pr edicting the arrival time at down stream arterial links, but it has some limitations and it has not ye t been validated with field data. Olszewski (2000) compared the HCM 2000 methodology for estimating intersection delay and another speed-flow model that requ ires intersection spacing and minimum signal delay as input parameters. This model was developed primarily for planning applications. The author compared th e travel speeds that are predicted by both models, for a range of parameters such as intersection spacing, tr affic flow and signal characteristics. The results yielded that the pattern of travel speeds is similar to both models; eventhough the HCM 2000 m odel predicts lower speeds. Summary of the Literature Review Several research efforts have been reviewed to establish the curre nt state-of-the-art that relates the arterial travel time and dela y estimation with mid-bl ock effects. Most of the literature is devoted to access control management and evaluation of alternative median treatments. Other studies try to quantif y the effects of either right turns or left turns from the arterials to the travel time of the through vehicles, by developing analytical models. Moreover, studies were found to ac knowledge the significan ce of the mid-block effects but without proposing a ny delay model that incorporates these effects. Thus, it can

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18 be concluded that a comprehens ive model that incorporates all the parameters that can cause arterial link delay to the through vehicl es is lacking from the current literature.

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19 CHAPTER 3 METHODOLOGY This chapter presents the methodology followe d to develop the analytical models of mid-block delay of arterial streets. The following tasks were undertaken: 1. Collect volume and travel time data fo r two-lane, two-way and two-lane, one-way arterials. 2. Simulate the arterials in CORSIM and calibrate the models. 3. Conduct a factorial design experiment. 4. Expand the database and perform multiple simulation runs. 5. Extract the mid-block delay data and de velop mid-block delay equations as a function of the independent variables of interest using regression analysis. A more detailed description of the process followed by this research is presented in the remaining of this chapter. Data Collection Data were collected at two two-lane, one -way arterials and one two-lane, two-way arterial. The data involve arterial through and turning volumes, driveway turning volumes, heavy vehicles, parking frequency, a nd bus dwell time, as well as travel time measurements. These data were used for the ne xt step of the resear ch (simulation model development and calibration). A more detailed description of the da ta collection process and the study sites is presented in Chapter 4 of this thesis. Simulation Model Development and Calibration One of the main purposes of this study is to use real-life traffic conditions for the development of the regression models. Howeve r, this would require a large amount of

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20 field measurements, which is outside of the scope of this research. Collecting a certain amount of field data and using a simulation tool for expanding the dataset would reach the research objectives. The study arterials were simulated in CORS IM and calibrated with the travel time measurements. The criterion used for deciding whether the models n eed to be calibrated or not is that the simulation travel time s hould be within a 10% range of the actual field measured travel time. The calibration of the models includes adjustments of the simulation parameters such that the resulting travel time would approach the field measured travel time and the real-life conditions would be replicated effectively. The calibration parameters considered are the discharge headway, the mean start-up delay, the dr iver’s reaction time, the driver familiarity, the probability of spillback, the duration of lane change maneuver, the parking maneuver duration, and the free-f low speed. Details regarding the simulation model development and calibration are given in Chapter 5. Design of Experiments Once the simulation models are created and calibrated, they were used as a basis for generating more data and expanding the database in CORSIM. The database expansion process requires the development of a factorial design in which several simulation model inputs are identi fied as varying factors with different levels, to attain variability in traffic and geometric conditi ons. The varying combinations between the levels of the simulation input parameters form different scenarios. The simulation input parameters were sele cted based on the arterial type, depending on whether it is a two-lane, two-way or a two-lane, one-way arterial. The simulation input parameters for the two-lane, two-way ar terial are the arterial through volume (two

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21 levels), the percentages of tu rning traffic from the arterial (six levels), the driveway volume (two levels), the percen tages of turning traffic from the driveway (three levels), the number of driveways per directions (four levels), the arterial free-flow speed (two levels), and the parking activity frequency (two levels). A mo re detailed description of the varying factors and leve ls are given in Table 6-1 of the corresponding chapter (Chapter 6). It is important to note that since there were two hours of data available for the two-lane, two-way simulation model (Par k Avenue – midday and p.m. peak-hour) it was decided to use the p.m. peak-hour mode l for the high-level ar terial through volume and the midday model for the lowlevel arterial through volume. For the two-lane, one-way models there were two arterial links, thus the simulation model inputs vary depending on the base model. As such, the first group of scenarios is formed for the Beaver Avenue at Sparks St reet, where the simulati on input variables are the arterial through volume (thr ee levels), the percentage of turning traffic from the arterial (six levels), the driveway total volume (two levels – the values depend on the arterial through volume level), th e number of driveways by link (four levels), the arterial free-flow speed (two levels) and the parki ng activity and duration (two levels). The second group of scenarios corresponds to th e base model of Beaver Avenue at Pugh Street and the simulation model inputs are the same as those of the first group plus one additional model input, which is the bus dwell time (two leve ls). Tables 6-2 and 6-3 of the corresponding chapter illustrate th e selected values for each level. Database Expansion The next step of the methodology is to perform multiple simulation runs for each different scenario developed. All scenarios created through the fact orial design would run for seven times to account for the variability in the simulator.

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22 For each new scenario, the appropriate data for the analysis were extracted. First, the mid-block delay was calculate d based on the following equation: FFS LinkLength CD TT MBD (3 – 1) Where: MBD = Mid-block delay of each link TT = Arterial link travel time (p rovided by the simulation output) CD = Intersection control delay at the ar terial downstream signal (provided by the simulation output) Link Lengh = The link length of the study arterial link FFS = Arterial free-flow speed (a ccording to the simulation input) Additionally, other information such as the arterial through and turning volume, and the driveway turning volume were extracted from the simulation output. Data Analysis and Formulation of Regression Models The final step of the methodology includes the data analysis and the development of the analytical models. To achieve this, th e extracted data and th e calculated mid-block delay were grouped depending on the arteri al configuration under study. Thus, two datasets were created for the case of the two-lane two-way arterials and the two-lane oneway arterials. Each dataset was further divided according to the traffic operations of the arterial to congested and uncongested. The notion behind th is decision is that the mid-block delay under uncongested conditions is affected si gnificantly by the driv eway turning volume and the arterial turning volume which contri butes to frequent vehicle frictions; however, under congestion, the mid-block delay is prim arily a result of the overall congestion.

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23 For each of the datasets, one regression e quation is formulated, which estimates the calculated mid-block delay of Equation 3 – 1 with respect to the selected independent variables. There were several variables initia lly considered to be incorporated into the regression models (candidate variables), which are descri bed briefly below. A more detailed description of the ca ndidate variables is included in the relevant chapter of the data analysis (Chapter 6). Vup: Arterial volume (vph/ln): This variable represents the volume that f eeds the arterial segment and is measured at the beginning of the arterial link. Vdr/N: Average Driveway volume (vph/ln): This variable represents the amount of tra ffic that enters the arterial through the driveways, divided by the num ber of driveways per link. Vart/N: Average Arterial Turning Volume (vph/ln): This variable describes the average tra ffic that exits the arterial through all driveways, either by a left-turn or right-turn maneuver. Vopp/N: Average Arterial Volume that is Opposed to Arterial Left Turns (vph/ln) (for two-lane two-way models): This variable is expressed as the sum of the arterial volume that opposes to left turns from the other direction divide d by the number of driveways involved. Xc: Arterial Degree of Saturation at Downstream Intersection: This variable is the arterial demand to cap acity ratio measured just upstream of the signal. FFS: Arterial Free Flow Speed (mph)

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24 Ndr: Number of driveways per 1000 ft DT: Bus Dwell Time (s) P: Parking Activity (vph/ft): This factor describes the parking freque ncy of the arterial segment for every available parking space of 20 ft. The final regression models include these variables as they are described above. However, some of them are altered in order to describe more effectively the mid-block delay. Last, the mid-block delay equations are used for the travel time estimation of twolane two-way or one-way arterials that operate under congested or uncongested conditions according to the following equation: FFS LinkLength CD MBD TT (3 – 2) Where all measures are as defined earlier.

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25 CHAPTER 4 DATA COLLECTION Data collection is an important step of th is research and it is used to generate variable arterial traffic conditions in the si mulation environment. Thus, sufficient data for the development of the analytical tr avel time models can be generated. Data Requirements Two types of data are required in this st udy. The first type includes the input data used for the development of the simulation models: 6. Arterial through volumes. 7. Arterial right and left-turni ng volumes in the driveways. 8. Driveways turning movements’ volumes (right turns, left turns and through movements). 9. % of heavy vehicles on the arterial and on the driveways. 10. Parking and departing maneuvers volumes on the arterials. 11. % time that the segment is occupied by buses when stopped (dwell time). 12. Number of passengers in and out of the buses. 13. Study site geometry (number of drivew ays per link, driveway spacing, driveway turning radii, number of lane s per link, total link length) 14. Number of bus stops per link and parking bay lengths. 15. Phasing and timing plans for th e signalized intersections. The second type of data, namely travel ti me, were collected c oncurrently with the input data. The travel time information of th e arterials is used for verifying that the

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26 developed simulation models replicate effici ently the real-life tr affic conditions (model calibration. Description of Study Site s The data collection plan considers two ar terials which are located in urban and residential environment. The first one is a two-lane two-way arteri al (Park Avenue) and the second one is a two-lane one-way arteri al (Beaver Avenue). The two arterials are shown in the following figures. Both stre ets are located in State College, PA. Park Avenue does not provide a TWLT lane or left turn pockets, thus each lane per direction serves as a shared lane for through/ right-turn and left-turn movements. For Park Avenue two successive links were analyzed. Each link contains two, two-way driveways, which form T-intersections with the arterial Two separate links are also studied in Beaver Avenue, both of which include a co mbination of T-intersections and TWSC intersections, with a total of si x and eight intersections for e ach arterial link respectively. Descriptive sketches of the two arterial c onfigurations are presented in Figures 4-3 through 4-5. Figure 4-1 Two-lane two-way arterial (Park Avenue)

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27 Figure 4-2 Two-lane one-way arterial (Beaver Avenue) Data Collection Methods The field data required for this research were collected during peak and non-peak hours to cover a wide range of flow conditions. 1. Loop detectors and cameras were used fo r the arterial through vehicle data collection. The loop detectors were located at the approaches of the signalized intersections and also at mid-block loca tions. The cameras were used for those approaches that traffic volumes were not available from loop detectors. 2. Manual recording and cameras were used fo r the collection of driveway turning and through volumes, the presence of heavy vehicles and the parking activity. 3. Manual recording of the number of passengers that use th e buses at the study area and of the respective time the bus deceler ates, remains stopped and accelerates at the bus stations. 4. The travel time study was conducted with the floating car technique. The travel time measurements occurred concurren tly with the volume measurements. The required number of vehicle runs was cal culated based on the standard deviation (S) of the field measured travel time and the margin of error (e) according to the following equation. 2 2) 96 1 (e S n (4 – 1)

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28 The calculation was performed for all three study arterials and the summary of the results are given in the following table, where the margin of error wa s selected at 30 sec. Table 4-1 Determination of number of vehicl e runs based on field measured travel time. PARK EB WB SPARKS PUGH MID P.M. MID P.M. A.M. MID P.M. A.M. MID P.M. Travel Time (s) 78.6 88.6 112.3 366.0 71.6 75.4 98.9 37.1 35.3 35.4 St.Dev. (s) 14.6 19.1 27.3 55.8 25.2 43.1 18.8 8.9 4.2 5.6 Number of Vehicle Runs Performed 7 5 7 4 8 7 7 8 7 7 St.Dev (s) 16.52 39.18 30.51 6.66 Number of Vehicle Runs Required 1 7 4 1 Since the maximum number of required vehicl e runs is less or equal to the number of performed vehicle runs, it is concluded that the field measured travel time data are sufficient.

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29 driveway 6 driveway 2 Sparks Street driveway 1 Beaver Avenue First Link driveway 7driveway 8 driveway 5 driveway 3driveway 4 Atherton Street Figure 4-3 First two-lane one-way arterial link (Beaver Avenue betw een Sparks St and Atherton St) driveway 1 Pugh Street Beaver Avenue Second Link driveway 4 driveway 6 driveway 5 driveway 2driveway 3 Garner Street Figure 4-4 Second two-lane one-way arterial link (Beaver Avenue between Pugh St and Garner St)

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30 Park Avenue Successive Links driveway 1 N. Atherton Street driveway 3 driveway 2 N. Allen Rd parking bay driveway 4 Shortlidge Rd Figure 4-5 Two successive two-lane two-way ar terial links (Park Avenue between N. Athe rton St and N. Allen Rd and Park Avenue between N. Allen Rd and Shortlidge Rd)

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31 CHAPTER 5 SIMULATION MODE L DEVELOPMENT An important step of this research is to use simulation to replicate the real time traffic conditions during the data collection study periods, and subsequently use them as the basis for the database expansion. The remaining of this chapter describes the simulation software used, the modeling of the study sites, and their ca libration with real field data. Simulation Package For the purposes of this research effort CORSIM (FHWA, 2003) was selected as the appropriate simulation tool for the model development as it can provide control delay in the output. The gap acceptance algorithm of CORSIM and its ab ility to replicate turning maneuvers are reviewed in the following paragraph. The gap acceptance model in CORSIM is based on default values of the distribution of the acceptable gaps, depending on the driver type category. The program uses 10 behavioral categories and the gap for near-side cross-street traffic ranges from 56 to 20 tenths of a second, based on a decile distribution. These default values can be altered by the user. The same logic is used for left-turn or rightturn gap acceptance, where the gaps are selected ba sed on the driver characteristic s code. If a far side cross street exists, additional gap time is require d as input, which depends on the number of lanes. Similarly, the additional time is given by a decile distribution. CORSIM does not have the capability of e xplicitly defining the speed of a turning maneuver; however, the user can implicitly aff ect the turning speed of the vehicles on the

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32 arterial by defining the free-flow speed of th e driveway. Alternativ ely, it is possible to define the maximum allowable turning speeds in CORSIM, but this option applies to the whole network and not to each individual intersection. The intersection control delay estimation in CORSIM includes the initial deceleration of the vehicles, the stopped delay, and the delay due to acceleration back to the full operating speed. The simulator considers the time difference between the actual travel time of the vehicles versus the trav el time had there been no signal (approximated by the ratio of the link length and the operati ng speed). When the arterial volume is low the operating speed is approximated by the free-flow speed. However, when the flow is significant the operating speed of the vehicl e cannot be approximated by the free flow speed and a smaller speed is considered. Although there are other simulation tools av ailable, CORSIM is a broadly used traffic engineering tool and it was selected for this research mostly because it can provide control delay information. Nevertheless, as every software, CORSIM has its own limitations, which will be discussed in a later section. Model Development The simulation of the study sites starts with the input of all the available and appropriate data that were co llected in the field. According to the data collection effort there were a total of eight hours of available volume and travel time data; two hours for both two-lane two-way arterial links (midday and pm peak hour) and three hours for both two-lane one-way arterial links (am peak hour, midday and pm peak hour). Since the two two-way arterial links are adjacent they were modeled into CORSIM together and therefore, each hour was modele d as a separate CORSIM file with the particular volumes.

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33 The data used for the simulation model de velopment include geometric data, signal phasing and timing data and volume data. The geometric data for each arterial segment consist of the arterial link length, the locati on of the driveways, the number of lanes on the arterial and on the driveway s, the turning speed into th e driveway, the parking space location and the bus turnouts. However, not all geometric data were able to be modeled into CORSIM explicitly due to program limitations. The turning speed of the arterial vehicles on the driveways was modeled by considering the curb radius of the driveways and the driveway throat width. Based on a study performed by Rich ards (1980) and reported by Stover and Koepke (1988) the speed of a vehicle that ente rs a driveway is significantly low for all combinations of curb radii and throat wi dths. The author developed a nomograph that provides the driveway turning speed based on these two measures. Therefore, based on the measure of the curb return radii and the throat width, the turning speed on the driveways was calculated for all cases. Howe ver, this type of speed cannot be input directly in CORSIM, as the program allows only the input of a link’s free-flow speed. To address this issue, the driveway links were modeled with free-flow speed equal to the corresponding “driveway entry speed” for a length of 100 ft near the intersection. It should be noted that ther e is an option in CORSIM where the user can specify the maximum allowable turning speed, either left or right-turn, but this option is networkwide. Therefore, it is not po ssible to have different maximum turning speeds on the same arterial street. Additionally, since there is bus activity on one of the arte rials, data such as bus frequency and mean dwell time were collected and modeled into th e simulator. Although

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34 in the study site the buses do not stop at a turnout but at the shoulder lane, this representation could not be done realistically in CORSIM, as the simulator provides only bus turnouts for modeling the bus stops. Howe ver, it can be assumed that the effect on traffic operations between a bus stopping at a turnout in CORSIM and a bus stopping at the shoulder lane in the study site is similar. The signal phasing and timing data of the intersections involved were also modeled in CORSIM. All signalized in tersections of the study site s operate under semi-actuated control. All pertinent data of the semi-actuated control such as minimum and maximum green intervals, vehicle passage times, dete ctor locations and size, and offsets were available and modeled appropria tely. Moreover, different ph asing and timing schemes are available throughout the day for the same inte rsections, therefore, the modeling of the signal control changed accordingly in all models. At this point it is important to mention that although there we re pedestrian phases on the control, ped-related data were not avai lable. Also, the study sites do not have many pedestrians, but there are a few as the arterials are located in the periphery of the campus. As such, it was assumed that the pedestri an intensity was 20 pedestrians per hour. Additionally, in one case the si gnal plan considered an exclusive pedestrian phase, but CORSIM cannot model this directly. For this reason, a “dummy” phase replaced the pedestrian phase where the “dummy” vehi cle volume would equal the pedestrian intensity and their route would be to a direction that does no t interfere with the arterial traffic. Volume data along with proportions of turn ing movements at the intersections and the driveways and heavy vehicle percentages we re also input in the models. Lastly, field

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35 measurements of the free-flow speed did not occur in the study corridors thus, it was approximated by the arterial speed limit as in these locations police enforcement of the speed limit is very regular. At this point it should be noted that mode ling arterial links into CORSIM requires a large amount of information, only a portion of which were actually collected in the field for this study. For other traffic characteri stics such as driver behavior, saturation headway, and start-up lost time the default values of the software were initially used, and these were altered as appropriate during the model cal ibration process. Model Calibration The model calibration process includes first, the comparison of the simulated models with field measurements of travel time, and second, the adjustment of the simulated models that do not match well with the real-life conditions. Once the simulation models were create d, the number of the simulation runs needed to be specified to account for the vari ability in the simulator. As such, several simulation runs were performed and the travel time standard deviation for each arterial model was calculated. By using the following eq uation and considering a margin of error (e), 15 sec, the number of required s imulation runs was calculated as seven. 2 2) 96 1 (e S n (5 – 1) The resulting number of simulation runs was very small; thus to simplify the process, the maximum number of required vehi cle runs was considered as the number of simulation runs to be performed in every m odel. Therefore, each of the eight models would run for seven times.

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36 The next step of the process is to verify that the simulation models would replicate traffic operations on the arterials and this is accomplished by comparing the average travel times derived from the simulation with th e field measured travel times. If the travel time calculated from the simulation would range between 10% of the travel time collected in the field then th e respective models would not need any further adjustment. The models that yielded acceptable travel ti me prediction are the Sparks a.m. and mid model and the Pugh a.m. model. However, if the simulation models yiel ded travel time outsi de of the 10% acceptable range they would then be calibra ted by making reasonable adjustments in those characteristics that were not collected in the field. Typically, the adjustments that are made in the simulation environment include changes to the saturation (discharge) h eadway, the mean start-up delay, the driver reaction time etc. The models that their predicted travel time was beyond the acceptable range where the Park mid and p.m., the Sparks p.m. and the Pugh mid and p.m. models. The calibration parameters for those models that were adjusted include the mean start-up delay, and discharge headway, the time reac tion to deceleration, th e duration of lane change maneuver, the parking maneuver duration, the driver familiarity, the free-flow speed and the spillback probability of discharg ing with respect to the discharge position. The default and calibration valu es of these parameters are given in tables 5-1 to 5-3.

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37 Table 5-1 Calibration parameters for Pa rk Avenue midday and p.m. models Calibration Parameter CORSIM Default Value Calibration Value Mean start-up delay (s) 2 1 (EB), 2.5 (WB) 1.5 driveways Mean discharge headway (s) 1.8 1.4 (EB), 2 (WB) 1.5 driveways 1 2 3 4 1 2 3 4 Probability (%) of a vehicle joining spillback with respect to the number of vehicles in the spillback 80 40 0 0 0 5 0 0 Driver familiarity (% drivers that know 1 turn movement in advance) 10 50 Time reaction to deceleration (s) 1 0.8 Duration of lane change maneuver (s) 3 2 Parking maneuver duration (s) 4 3.5 Table 5-2 Calibration parameters for Sparks p.m. model Calibration Parameter CORSIM Default Value Calibration Value Mean start-up delay (s) 2 3-4 arterial 3 driveways Mean discharge headway (s) 1.8 3 Driver familiarity (% drivers that know 1 turn movement in advance) 10 80 Time reaction to deceleration (s) 1 3 Duration of lane change maneuver (s) 3 2 % drivers who cooperate with lane changes 50 20 Parking maneuver duration (s) 4 3.5 Table 5-3 Calibration parameters for Pugh midday and p.m. models Calibration Parameter CORSIM Default Value Calibration Value Mean start-up delay (s) 2 1.5 Mean discharge headway (s) 1.8 1.5 Time reaction to deceleration (s) 1 0.7 Free flow speed (mph) 30 Parking maneuver duration (s) 4 3.5 The probability of a vehicle joining a “spill back” queue with respect to the number of vehicles in the spillback (Table 5-1) is an important variable which was used for the models that had congestion and yielded high travel times. It was observed that due to the congestion, the arterial through vehicles would not leave gaps, even if the queues were extended beyond the unsignalized intersecti on. This would lead to an unrealistic

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38 representation of field conditions and to increased travel times as the vehicles blocking the intersections would impede the left tu rns from the opposing direction. In reality, queued vehicles usually leave the unsignali zed intersection clear for the driveway vehicles to enter or cross or for the left-t urn vehicles on the opposing direction. However, it is possible to define in CORSIM the probabi lity of a vehicle joining a spillback and in this case this probability was reduced (see Table 5-1) to account for vehicles waiting until the spillback ahead dissipates. The particular combination of probabilities of joining spillback (0% if there is 1 vehicle in the spi llback and 5% if there are 2 vehicles in the spillback) resulted in lower travel times for th e simulation models, as the vehicles from the opposing directions would leav e gaps at the intersections for the left-turning vehicles to cross. By implementing these calibration parameters the travel times from simulation were in agreement with the field measured travel times. The following table presents the simulated and field measured travel times for all models. Table 5-4 Field measured vs. simula tion travel time after calibration Field Measurements Model Travel Time Acceptable Range Simulation Travel Time EB 78.57 [70.71,86.43] 78.16 Park mid WB 112.29 [101.06,123.52] 104.13 EB 88.60 [79.74,97.46] 96.73 Park pm WB 355.40 [319.86,390.94] 345.76 Sparks a.m. 71.63 [64.47,78.79] 74.76 Sparks mid 75.43 [67.89,82.97] 74.49 Sparks p.m. 98.86 [88.97,108.75] 97.43 Pugh a.m. 37.13 [33.42,40.84] 39.83 Pugh mid 35.29 [31.76,38.82] 36.01 Pugh p.m. 35.43 [31.89,38.97] 33.82

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39 Summary and Conclusions The simulation model development process in cludes two important steps. The first step was to recreate the geom etry of the arterials and th e traffic conditions into the simulation environment during the data co llection period. The second step was to calibrate the models in order to ascertain th at they can replicate satisfactorily traffic operations on the arterials. During the simulation model development several weaknesses of CORSIM were revealed. Although CORSIM is a widely used to ol there are some issues that the program does not address directly. For this reas on this section also provides some recommendations for improving/extending the capabilities of the software. CORSIM does not provide the option for defining the arterial turning speed for individual intersections. A lthough the program allows for network-wide input of the maximum rightor left-turning speed, it may be mo re useful that this option is applied to individual intersections based on the research purposes. In this research the issue of modeling rightor left-turn ma neuvers’ speed was addressed by “forcing” the vehicles to enter the driveway with low free-flow speed, which corresponded to the site specifications of throat width and curb radius. Additionally, CORSIM does not allow for modeling exclusiv e pedestrian phases. In the simulation modeling process this issue was addressed by crea ting a “dummy” phase for an approach that does not interfere with the arterial network a nd traffic flow that equals to the pedestrian intens ity. Nevertheless, it may not al ways be possible to create a “dummy” phase for a traffic movement that does not affect the network; thus, this capability of modeling exclusive pedestrian phases should be provided by the software.

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40 Another important limitation that was observe d is the gap acceptance algorithm that the software applies. As already me ntioned, CORSIM builds the gap acceptance algorithm upon the driver characteristics code, bu t in reality this is not the only basis for the gap acceptance. It is recommended that th e software accounts for the fact that drivers become impatient while waiting a long time for a gap.

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41 CHAPTER 6 ANALYTICAL MODEL DEVELOPMENT The effects of mid-block phenomena are ex plored with the help of the CORSIM simulation package. The analysis includes two basic steps; the expans ion of the available field data set through simulation and the deve lopment of the analyt ical models through regression analysis. For the e xpansion of the database, alte rnative scenarios are built in the simulation environment, according to the prespecified design of experiments. These scenarios are formed for both cases of twolane, two-way and two-lane, one-way urban arterial streets, thus two s ubsets of data are generated th rough CORSIM. These data are eventually used for the mid-block delay mode l development, with the help of MINITAB statistical analysis package. Database Expansion Due to the limited amount of field data av ailable, it was decided to use simulation in order to generate enough data to be used for the analytical model development. After building the models into CORSIM and calibrati ng them with the field measured travel time data, they were used as a basis for ge nerating more data and expanding the database. For each calibrated model, several simulation model inputs were selected, to be used as varying factors of the factoria l design of the experiment. In an effort to attain large variability in traffic conditions an d arterial geometry, each factor was designed with different levels For example, arterial traffic flow ranges between 800 vph/ln and 1000 vph/ln for the tw o-lane, two-way scenarios and between

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42 400 vph/ln and 800 vph/ln for the two-lane, oneway scenarios. Each combination of the varying levels of the fact ors represents a different scenario in the database. The simulation model inputs depend on the calibrated model. Thus, all cases of two-lane, two-way arterials de rive from the model of Park Avenue (two adjacent links). However, the two-lane one-way cases come from both calibrated models of Beaver Avenue at Sparks Street and at Pugh Street; thus, the select ed simulation model inputs are different. Additionally, since there are different levels of volume for each scenario, it was decided to match these with the calibrated models that had similar volume throughput (am, mid and pm models). Tables 6 1 and 6 2 summarize the diffe rent inputs and their levels that were used for the database expa nsion. Table 6 1 corresponds to the cases of two-lane, two-way arterials and Tables 6 2 and 6 3 correspond to the cases of twolane, one-way arterials. The selection of the simulati on model inputs that appear in tables 6.1 through 6.3 is based on (1) the anticipated form of the midblock delay analytical models, and (2) the CORSIM modeling capabilities. That is, the generated data would be used for applying regression analysis and modeli ng the arterial mid-block dela y as a function of several parameters of influence. Thus, the simulation model inputs used for generating these data should be such that affect the arteri al travel time a nd mid-block delay. Moreover, since the simulation model i nputs are in fact CORSIM inputs, the limitations and capabilities of the software s hould also be considered. As an example, although the actual amount of tra ffic that exits the arterial through right-turn or left-turn maneuvers is a more straight-forward i nput than the turning percentage at the

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43 intersection, this is not feasible to model in CORSIM, since only the turning percentages can be modeled in the simulator. Table 6-1 Two-lane two-way simulation model inputs for database expansion Two-Lane Two-Way Arterials Simulation Model Inputs # of Levels Level Value 1 Arterial Through Volume VA (vph/ln) 2 800 1000 % of Arterial Traffic Performing Right/Left Maneuver VA-R /VA-L (%) 20/5 30/5 20/15 20/25 30/15 30/25 2 Driveway Total Volume VDR (vph) 6 200 300 3 % of Driveway Traffic Performing Right/Left Maneuver at TIntersections VDR-R VDR-L (%) 3 30/70 50/50 70/30 4 # of Driveways by Link per Direction 4 0* 1 2 4 5 Arterial Free Flow Speed (mph) 2 30 45 6 Parking Activity Frequency (mph) 2 20 0 Number of Runs per Case 7 Number of Data Points per Link 872 Subtotal 12208 For zero number of driveways the simu lation model inputs 2, and 3 do not apply. Table 6-2 Two-lane one-way simulation mode l inputs for database expansion. First group of scenarios (from Beaver Avenue at Sparks Street) Two-Lane One-Way Arterials Simulation Model Inputs # of Levels Level Value 1 Arterial Through Volume VA (vph/ln) 3 600 650 800 % of Arterial Traffic Performing Right/Left Maneuver VA-R/ VA-L (%) 20/5 30/5 20/15 20/25 30/15 30/25 2 Driveway Total Volume VDR (vph) 6 180/ 200/ 220 260/ 300/ 370 3 # of Driveways by Link 4 0** 2 3 4 4 Arterial Free Flow Speed (mph) 2 30 45 5 Parking Activity Frequency (mph) 2 20 0 Number of Runs per Case 7 Number of Data Points 220 Subtotal 1540 The driveway total volume depends on the arterial volume and tu rning percentage; for 600 vph/ln arterial volume and for the first three levels of arterial turning percentages, the driveway volume is 180 vph. ** For zero number of driveways the si mulation model input 2 does not apply.

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44 Table 6-3 Two-lane one-way simulation mode l inputs for database expansion. Second group of scenarios (from Beav er Avenue at Pugh Street) Two-Lane One-Way Arterials Simulation Model Inputs # of Levels Level Value 1 Arterial Through Volume VA (vph/ln) 3 400 800 1000 % of Arterial Traffic Performing Right/Left Maneuver VA-R VA-L (%) 20 5 30 5 20 15 20 25 30 15 30 25 2 Driveway Total Volume VDR (vph) 6 150/ 300/ 300 200/ 400/ 470 3 # of Driveways by Link 4 0** 2 3 4 4 Arterial Free Flow Speed (mph) 2 30 45 5 Bus Activity – Dwell Time (s) 2 30 0 6 Parking Activity Frequency (mph) 2 20 0 Number of Runs per Case 7 Number of Data Points 448 Subtotal 3136 The driveway total volume depends on the arterial volume and tu rning percentage; for 400 vph/ln arterial volume and for the first three levels of arterial turning percentages, the driveway volume is 150 vph. ** For zero number of driveways the si mulation model input 2 does not apply. Each new scenario created in CORSIM was run seven times to account for the variability in the traffic simulator. Note that the number of runs and the seed numbers of each run is the same as the ones used during the model calibration procedure. Selection of Simulation Output Performance Measures After performing the required simulation runs in CORSIM, it was desired to select appropriate simulation output s of performance measures, which would be used for the analytical model development in a later ste p. The selection of th e simulation outputs was made with the notion that severa l of these outputs would be used for the calculation of the mid-block delay, while others would repres ent the regression model’s independent variables. As such, the outputs that were extrac ted directly from CORSIM, for each simulation run are listed in the following table.

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45 Table 6-4 Selected performance m easures extracted from CORSIM CORSIM Performance Measures Control delay at downstream signal (sec/veh) Link travel time (sec/veh) Arterial volume by link (vph) Driveway volume (vph) Based on the methodological framework deve loped, the control delay and arterial link travel time information are used for the determination of the mid-block delay (Equation 6 – 1). FFS LinkLength CD TT MBD (6 – 1) Where all measures are as defined earlier. The arterial volume by link and the driv eway volume are used as candidate variables for the regression model devel opment. The extracted driveway volume information includes the total volume that en ters and exits the arterial link (i.e., the CORSIM output file provides the total outgo ing driveway volume and the total incoming arterial volume). Database Organization The expanded database is organized in an appropriate format to be used for the analytical model development. The database is divided primarily into two sets; the twolane two-way dataset and the two-lane one-way dataset. Th is separation is done due to the fact that not all para meters of mid-block delay are common to both arterial configurations. Some parameters that influe nce mid-block delay are not the same for both models. The final models estimate the mid-bl ock delay that is e xperienced by arterial through drivers within a single arterial link (between two tr affic signals), as a function of the selected independent variables.

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46 For the two-lane two-way arterials, the mid-block delay of Equation (6 – 1) is calculated for each approach of the arteri al link (eastbound/westbound). The generated data (arterial volumes, driveway volumes et c) are transformed into the independent variables to be used for the regression m odel, and are organized with respect to the approach that they influence the most. A mo re detailed description of the candidate variables of the model is given in the data analysis section. The total number of datapoi nts that were generated fr om the simulation runs and used for the regression models is 4,676 for the two-lane one-way arterials and 12,208 for the two-lane two-way arterials. Data Analysis The data analysis involves primarily the se lection of the approp riate variables that should describe adequately the regression model to be developed, and finally its formation. Additionally, it is essential that the data are organized in a way that all possible conditions are covered; such as congested vs. not congested conditions, and twolane one-way vs. two-lane two-way arterials. Selection of Candidate Variables In this step of the data analysis the vari ables that best explai n arterial mid-block delay are selected. A detailed description of the candidate variables to be used in the rest of the analysis is presented in the following section. Vup: Arterial volume (vph/ln) This variable represents the volume that f eeds the arterial segment and is measured at the beginning of the arteri al link. It is speculated that the arterial upstream volume affects positively the mid-block delay; as the amount of traffic entering the arterial

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47 increases, the arterial and driveway vehicle interactions are more intense and thus the mid-block delay increases. Nevertheless, there is a limit in the infl uence of the arterial volume on mid-block delay. For instance, if the arte rial is congested, the amount of traffic that enters the arterial segment is impeded by the downstr eam queued vehicles, which means that the actual throughput could be less than that under uncongested conditions. Note that when trying to model the arte rial mid-block delay, the actual arterial throughput is more useful information than th e demand, since the latter is not always met (congested conditions). Also, the prevai ling throughput can better explain vehicle interactions within a segment than the projected demand obtained from upstream segments. Vdr/N: Average Driveway volume (vph/ln) This represents the amount of traffic that enters the arterial through the driveways. In the case of two-lane one-way arterial this is the sum of all driveways volume divided by the number of driveways per link. In the case of two-lane tw o-way arterials this variable is defined as the sum of traffic that enters the particular direction, divided by the total number of driveways involved. An exampl e of this is illust rated in the following figure.

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48 Vdr-L Driveway 2 Driveway 1Vdr-R N Figure 6-1 Sketch of variable Vdr/N In Figure 6 – 1, one could consider that the vehicles traveling eastbound are mostly affected by the left-turning ve hicles of driveway 1 and th e right-turning vehicles of driveway 2. The through vehicl es would likely decelerate to maintain a safe distance from the entering vehicles. With this assumption, the sum of Vdr-L and Vdr-R divided by the two driveways would represent the selected variable that affects the mid-block delay for the eastbound approach. More generally, the sum of vehicles that enter a specific direction of the arterial from the drivew ays, divided by the number of driveways involved, would likely influence the mid-block delay that the drivers of that direction experience. The following equation illustrates the average driveway volume used for the two-lane two-way model. N V V N V N V V N Vi WB R dr i WB L dr WB dr i EB R dr i EB L dr EB dr) ( ) () ( ) ( ) ( ) ( ) ( ) ( Where: Vdr(EB) /N = average driveway volume that affects EB direction. Vdr(WB) /N = average driveway volume that affects WB direction.

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49 Vdr-L(EB)i = driveway volume that enters the ar terial EB direction through left-turn maneuver at the ith intersection. Vdr-R(EB)i = driveway volume that enters the ar terial EB direction through right-turn maneuver at the ith intersection. Vdr-L(WB)i = driveway volume that enters the ar terial WB direction through left-turn maneuver at the ith intersection. Vdr-R(WB)i = driveway volume that enters the ar terial WB direction through right-turn maneuver at the ith intersection. N = Total number of driveways involved within the segment. The effect of this variable is mostly a pparent in non-congested conditions, as it can increase the mid-block delay that the arterial vehicles experience. In congested conditions, however, this variable may not aff ect mid-block delay si gnificantly, since the arterial vehicles’ speed would be most likely very low and th ey would not decelerate for the oncoming traffic. Vart/N: Average Arterial Turning Volume (vph/ln) This variable describes the average tra ffic that exits the arterial through all driveways, either by left-turn or right-turn ma neuver. The logic behind this variable is the same as with the average driveway volum e as the through vehicles would likely be delayed by the arterial turning vehicles ahead. The average arterial turning volume is the sum of the right turns and left turns from the arterial that are moving to a particular direction divided by the number of driveways that are involved. A schematic illustration of the variable description is shown in Figure 6-2.

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50 Vart-L Driveway 2Vart-L Driveway 1Vart-R Vart-R Vart-L N Figure 6-2 Sketch of variable Vart/N In this case, the average ar terial turning volume that a ffects mid-block delay in the eastbound direction should be th e sum of the left-turning volum e at driveway 1 and rightturning volume at driveway 2, divided by the two driveways. Simila rly, the right-turning vehicles at driveway 1 and the left-turning ve hicles at driveway 2 affect the mid-block delay that the vehicles of th e westbound approach experience. In general, this variable can be described by the following equations: N V V N V N V V N Vi WB R art i WB L art WB art i EB R art i EB L art EB art) ( ) () ( ) ( ) ( ) ( ) ( ) ( Where: Vart(EB) /N = average arterial turning volume that affects EB direction. Vart(WB) /N = average arterial turning volum e that affects WB direction. Vart-L(EB)i = arterial left-turning volume that exit s the arterial EB direction at the ith intersection. Vart-R(EB)i = arterial right-turning volume that ex its the arterial EB direction at the ith intersection.

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51 Vart-L(WB)i = arterial left-turning volum e that exits the arterial WB direction at the ith intersection. Vdr-R(WB)i = arterial right-turning volume that exits the arterial WB direction at the ith intersection. N = Total number of driveways involved within the segment. Vopp/N: Average Arterial Volume that is O pposed to Arterial Left Turns (vph/ln) The main goal of this variable is to capt ure the influence of the arterial left-turning vehicles on the arterial through vehicles of the opposing direction. This variable is expressed as the sum of the arterial volume th at is just upstream of each driveway divided by the number of driveways involved, and th is affects the mid-block delay of the opposing direction vehicles. Generally, this variable can be expressed as: i i EB up art EB oppN V N V ) () ( ) ( for WB Mid-Block Delay calculation j j WB up art WB oppN V N V ) () ( ) ( for EB Mid-Block Delay calculation Where: Vopp(EB) /N = average EB arterial volume opposed to left tu rns from WB direction. Vopp(WB) /N = average WB arterial volume opposed to left turns from EB direction. ) () ( i EB up artV = sum of the EB arterial volume that is upstream of each ith intersection.

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52 ) () ( j WB up artV = sum of the WB arterial volume that is upstream of each jth intersection. Ni = number of driveways that serve EB left turns. Nj = number of driveways that serve WB left turns. Xc: Arterial Degree of Saturation Another variable that is c onsidered for the final analytical model is the degree of saturation of the arterial segment. This variable is estimated based on the HCM methodology (Chapter 16), according to the following equation: C g s v Xc Where: v = the arterial demand (vph) s = the saturation flow rate (vph) g = the effective green time of the signal (s) C = the cycle length (s) The degree of saturation is a candidate va riable for the model because it represents the unmet demand at the traffic signal and thus the arterial congestion. When the degree of saturation increases, it is expected th at the mid-block delay would increase due to frequent vehicle interactions. FFS: Arterial Free Flow Speed (mph) The arterial free flow speed is another candi date variable for the analytical model. It is speculated that when the FFS is high, sudden vehicle decelera tions due to turning maneuvers would yield additiona l delays to through vehicles than for lower FFS. This candidate variable may be important for uncongested conditions where vehicles can

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53 achieve desired travel speeds, while in conge sted conditions all vehicles travel with relatively the same (low) speed and thus, the FFS may not be an explanatory variable. P: Parking Activity (vph/ft) This factor describes the parking freque ncy of the arterial segment for every available parking space of 20 ft. The parking activity may affect the travel time as the vehicles that perform such a maneuver (eithe r by parking or by leaving the parking space) would cause the arterial oncomi ng vehicles to decelerate. Ndr: Number of driveways per 1000 ft An important variable for the regression model is the number of driveways per 1000 ft, which can be considered as the driveway density. It is expected that the more the driveways, the more the opportunities for turn ing maneuvers, either from the arterial vehicles or from the driveways’ vehicles, l eading to more chances for vehicle frictions. For the model development, it is assumed th at the mid-block delay of each direction is affected by the number of driveways that are adjacent to that direction. DT: Bus Dwell Time (s) The final variable to be considered for the model is the bus dwell time. This variable represents the time that the bus is st opped at the bus stop and it is used only for the two-lane one-way model, as this is the only simulation model available with this kind of input. The effect of buses on vehicle delays is considered from the aspect that vehicles generally tend to decelerate when passing through a bus-st op in the presence of a bus. Apart from the variables described here several interaction terms were also considered as candidate variables for the regression models. Some of the candidate interaction terms are the interactions betw een the total arterial left-turning volume ( VartL) and the total arterial opposing volume ( Vopp), (for two-way ar terials only),and the

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54 interaction between the driveway density (Ndr) with the total arterial turning volume ( Vart-L+ Vart-R) or with the total driveway turning volume ( Vdr-L+ Vdr-R). These interactions were tested in terms of their applicability and their significance. Regression Models For the development of the analytical m odels, both datasets of two-lane one-way and two-lane two-way arterials are further divided into congested and non-congested, depending on the conditions under which they operate. The distinc tion between the two traffic conditions is not an easy task, since th e selected criterion should be as clear as possible. In both cases, the cr iterion for separating the data is the discharge to demand ratio at the downstream arte rial signal since this perfor mance measure can effectively distinguish between the two states. A schematic of the measurements required for the calculation of the discharge to demand ratio is illustrated in Figure 6-3. The measurement of the discharge is the average flow that traverses the down stream arterial segment, VAR (i.e., the segment between the last driveway and the stop line). The demand measurement equals the discharge from the previous segment, VAR-TH, minus the arterial turning volume, VAR-TURN, plus the demand entering from the driveway, DDR. VAR-THVAR-TURN Downstream Arterial Segment DDR VAR ARTERIAL LINK N Figure 6-3 Measurement of discharge to demand ratio.

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55 It is expected that in undersaturated c onditions, the discharge to demand ratio is near one, since in those cases all the demand is accommodated and the vehicles are discharged unimpeded at the signal. On the contrary, under congestion, the discharge is significantly lower than the demand, and as such the ratio is expected to be significantly lower than one. Thus, all datapoints with a low discharge to demand ratio were considered to represent congested conditi ons. The threshold for determining the two operating conditions was set by vi sually inspecting the graphs of the discharge to demand ratio versus the mid-block dela y that are presented in Figures 6-4 and 6-5 (a) and (b). The graph that corresponds to the two-lane one-w ay cases appears in Figure 6-4 while the two-lane two-way graphs for the low volum e and high volume data are shown in Figure 6-5 parts (a) and (b), respectively. As is appears from the graphs that follow, the boundary value was selected to be 0.95, to account for variability of the simulation model. The discharge to demand ratio appears to be slightly higher than 1.00 for some datapoints, which intuitively is incorrect; however this is a result of volume fluctuations during the simulation period.

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56 TWO-LANE ONE-WAY ARTERIALS0 20 40 60 80 100 120 140 160 0.500.550.600.650.700.750.800.850.900.951.001.051.10Discharge/Demand RatioMid-block Delay Figure 6-4 Dataplot of mid-bl ock delay vs. discharge to demand ratio for two-lane oneway arterials. TWO-LANE TWO-WAY ARTERIALS LOW VOLUME LEVEL0 20 40 60 80 100 120 140 160 0.500.550.600.650.700.750.800.850.900.951.001.051.10 Discharge/Demand RatioMid-block Delay A

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57 TWO-LANE TWO-WAY ARTERIALS HIGH VOLUME LEVEL0 20 40 60 80 100 120 140 160 0.500.550.600.650.700.750.800.850.900.951.001.051.10 Discharge to Demand RatioMid-block Delay B Figure 6-5 Dataplot of mid-bl ock delay vs. discharge to demand ratio for two-lane twoway arterials. (A) Mid-block delay a nd discharge to demand ratio for low volume level. (B) Mid-block delay and discharge to demand ratio for high volume level. It is important to note that congestion a ppeared to take place for different reasons and with different effects. Several datapoint s were identified to describe congested conditions although the ratio of discharge over demand would be high, near the threshold of 0.95. This usually occurred when the arte rial discharge is low but also the demand measured at the downstream segment is redu ced, due to congestion further upstream (i.e., the measurement of the demand is a functi on of the number of vehicles discharged upstream on the same link; if this value is reduced due to congestion then the demand would also be low). This type of congestion has different e ffects depending on the type of facility. In both cases of two-lane one-wa y and two-lane two-way models the arterial turning volume at the most downstream driv eway is replenished by the volume that Low discharge and demand rates Reduced discharge upstream congestion Uncongested datapoints

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58 enters the arterial through the driveway, l eading to a discharge rate that approaches demand and for this reason the ratio remains high. However, for the two-lane two-way scenario s, another type of congestion is also observed which yields diminishing discharge over demand ratio associated with very high delays. This usually occurred (Figure 6-6) when the vehicle discharge at the eastbound or westbound upstream segment was im peded by arterial le ft-turning traffic that was blocking the through movement (due to high opposing traffic). In these cases, the downstream segment of the arterial (where demand and discharge is measured) does not accommodate a large number of vehicles and thus, the discharge to demand ratio is reduced. Furthermore, driveway vehicles cannot enter the arterial eastbound direction as they would also have to perfor m a left-turn maneuver, but they lose the priority due to the arterial left-turning vehicles or vehicles from the west bound direction. This type of congestion is also referred to as demand starvation. N Figure 6-6 Congestion occurrence in two-lane two-way arterials It was also observed that in several cases the discharge to demand ratio was lower than 0.95 but the mid-block delay ranged w ithin low levels. These cases typically occurred when the demand at the driveways had very high values and not all of that demand could discharge. However, that does no t necessarily mean th at the arterial is Reduced discharge rate Reduced discharge rate

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59 operating under congested conditions. As such these datapoints were considered as uncongested conditions. Lastly, it should be noted that a few simula tion runs yielded very low discharge to demand ratios, discharge to capacity ratios long queues on the arterial and extremely large mid-block delay. These datapoints repr esent gridlock and they were eliminated from the database since there is no distinguish able mid-block delay in stop-and-go traffic. Regression Model for Two-Lane One-Way Arterials The distinction of the data in the two da tasets yields two different regression models that are presented in the follow ing table, along with the respective ANOVA tables. The derivation of the final independent variables of the models is based on the candidate variables described in an earlier section. The candidate variables were transformed appropriately, in order to ach ieve a better model f it, depending on their relationship with the mid-block delay. Charact eristic trends of the independent variables versus mid-block delay are plotte d in the figures that follow.

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60 Table 6-5 Mid-block delay equations for two-lane one-way uncongested conditions Mid-Block Delay Equation fo r Uncongested Conditions N V N V DT FFS N e MBDart dr dr XC 00488 0 00551 0 0161 0 261 0 73 1 266 0 08 8 (6-2) Predictor Constant e *Xc Ndr FFS DT Vdr/N Vart/N Coef -8.0783 0.265828 1.72951 0.261140 0.016097 0.005505 0.004879 SE Coef 0.3026 0.004175 0.06496 0.006249 0.003633 0.001351 0.001010 T -26.70 63.68 26.63 41.79 4.43 4.08 4.83 P 0.000 0.000 0.000 0.000 0.000 0.000 0.000 S = 3.104 R-Sq = 63 .8% R-Sq(adj) = 63.8% ANOVA Table Source Regression Residual Error Total DF 6 4441 4447 SS 75569 42794 118364 MS 12595 10 F 1307.04 P 0.000 Independent Variables: e *Xc --exponentiate Xc = arterial degree of saturation, = 3.951 Ndr --number of driveways per 1000 ft FFS --arterial free-flow speed (mph) DT --bus dwell time (s) Vdr/N --average turning volume that enters the arterial link (vph) Vart/N --average turning volume that exits the arterial link (vph) It was decided to use the exponential Xc instead of the candidate variable Xc (arterial degree of saturation) as this variable appeared to describe better to the dataset. The parameter estimate was obtained with regression through the origin of the independent variable Xc and the dependent variable ln(MBD). The result of the regression yielded a parameter estimate = 3.951. The effect of parking activity was found to be non-significant and for this reason it was removed from the final equation of mid-block delay (Equation 6-2).

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61 0 10 20 30 40 50 60 70 0100200300400500600 Average Arterial Turning VolumeMid-block Del a A 0 10 20 30 40 50 60 70 050100150200250300350400450500 Average Driveway Turning VolumeMid-block Del a B Figure 6-7 Relationship between mid-block de lay and independent va riables for the twolane one-way uncongested model. (A) Average arterial turning volume, Vart/N. (B) Average driveway turning volume, Vdr/N

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62 Table 6-6 Mid-block delay equations for two-lane one-way congested conditions Mid-Block Delay Equation for Congested Conditions N V N D V FFS N e Demand V MBDart dr dr X upC 262 0 172 359 0 4 10 184 0 2 66 285 (6-3) Predictor Constant Vup/Demand e *Xc Ndr FFS ( Vdr/D)/N Vart/N Coef 285.41 -66.24 -0.1842 10.383 0.3592 -172.317 -0.26250 SE Coef 74.50 71.40 0.1646 2.097 0.1185 8.314 0.02111 T 3.83 -0.93 -1.12 4.95 3.03 -20.73 -12.43 P 0.000 0.355 0.264 0.000 0.003 0.000 0.000 S = 13.15 R-Sq = 82 .3% R-Sq(adj) = 81.8% ANOVA Table Source Regression Residual Error Total DF 6 222 228 SS 178171 38394 216565 MS 29695 173 F 171.70 P 0.000 Independent Variables: Vup/Demand --arterial volume to demand ratio e *Xc --exponentiate Xc = arterial degree of saturation, = 4.352 Ndr --number of driveways per 1000 ft FFS --arterial free-flow speed (mph) ( Vdr/D)/N --average turning volume that enters the arterial link to demand ratio (vph) Vart/N --average turning volume that exits the arterial link (vph) Similarly to the previous model the exponential Xc was used instead of the candidate variable Xc (arterial degree of satura tion). The parameter estimate was obtained with regression through the orig in of the independent variable Xc and the dependent variable ln(MBD). In this case, th e result of the regression yielded a parameter estimate = 4.352. In the case of congested conditions the pa rking activity was found to have no effect on mid-block delay; thus it was removed fr om the respective equation (Equation 6-3).

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63 0 20 40 60 80 100 120 140 160 050100150200250300350 Average Arterial Turning VolumeMid-block Del a A 0 20 40 60 80 100 120 140 160 0.000.100.200.300.400.500.600.700.800.901.00 Average Driveway Volume to Demand RatioMid-block Del a B Figure 6-8 Relationship between mid-block de lay and independent va riables for the twolane one-way congested model. (A) Average arterial turning volume, Vart/N. (B) Average driveway turning volume to demand ratio, ( Vdr/D)/N. Regression Model for Two-Lane Two-Way Arterials The distinction of congested vs. unconge sted data at the low volume dataplot (Figure 6-5 (a)) appears to be straightforward, if one considers the discharge to demand ratio threshold of 0.95. However, the datapl ot of Figure 6-5 (b) appears more complex but this is understandable given the complexity of tra ffic operations on high volume two-

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64 way arterials. In Figure 6-5 (b), the datapoi nts with a low ratio but also low mid-block delay were considered for the uncongested model. The regression analysis performed on both databases of congested and uncongested conditions yields the results presented in the following table. Repres entative dataplots of mid-block delay versus the independent variab les of each model are also available. These dataplots are useful for dis tinguishing trends among the variables, depending on the prevailing traffic conditions. In both congested and uncongested twolane, two-way models the presence of buses is not considered as an independent va riable as the simulation input models do not include such a variable. Table 6-7 Mid-block delay equations for two-lane two-way unc ongested conditions Mid-Block Delay Equation fo r Uncongested Conditions 410 105 0 608 0 00502 0 0128 0 0310 0 126 0 0126 0 9 13opp L art dr dr opp art upV V N N V N V N V FFS V MBD (6-4) Predictor Constant Vup FFS Vart/N Vopp/N Vdr/N Ndr ( Vart-L* Vopp)/104 Coef -13.9070 0.0125814 0.125672 0.030951 0.0128054 0.005021 0.60799 0.104880 SE Coef 0.6274 0.0005931 0.009947 0.002189 0.0002931 0.002875 0.05581 0.001704 T -22.17 21.21 12.63 14.14 43.69 1.75 10.89 61.56 P 0.000 0.000 0.000 0.000 0.000 0.081 0.000 0.000 S = 6.174 R-Sq = 64 .7% R-Sq(adj) = 64.7% ANOVA Table Source Regression Residual Error Total DF 7 6860 6867 SS 479460 261462 740922 MS 68494 38 F 1797.09 P 0.000 Independent Variables: Vup --arterial volume at the beginning of the link FFS --arterial free-flow speed Vart/N --average turning volume that exits the arterial link (vph) Vopp/N --average arterial traffic th at opposes to left turns (vph) Vdr/N --average turning volume that enters the arterial link (vph) Ndr --number of driveways per 1000 ft ( Vart-L* Vopp)/104 --Interaction between arterial right turns and opposing volume (vph*vph)

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65 The parking frequency was found to be not significant in the de termination of midblock delay for uncongested conditions in two-lane, two-way arterials. 0 10 20 30 40 50 60 70 050100150200250300350 ( Vart-L* Vopp)/104 Mid-block Delay A 0 10 20 30 40 50 60 70 0123456 NdrMid-block Delay B Figure 6-9 Relationship between mid-block de lay and independent va riables for the twolane two-way uncongested model. (A) Inte raction between total arterial leftturning volume and total arterial opposing volume, ( Vart-L* Vopp)/104. (B) Number of driveways per 1000 ft, Ndr.

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66 Table 6-8 Mid-block delay equations for two-lane two-way congested conditions Mid-Block Delay Equation for Congested Conditions 410 0832 0 331 0 91 2 0123 0 0265 0 0418 0 00428 0 250 0 0983 0 / 6 25 99 6 8 17opp L art dr opp dr l art r art Xc dr upV V P N V V V V FFS e N D V Demand V MBD (6-5) Predictor Constant Vup/Demand Vdr/N*D e Xc FFS Vart-r Vart-l Vdr Vopp Ndr P ( Vart-L* Vopp)/104 Coef 17.760 -6.991 -25.635 0.098332 0.24979 0.004277 0.041828 -0.026469 0.0123063 2.9092 0.3307 -0.083248 SE Coef 1.945 2.044 1.291 0.005147 0.02408 0.002156 0.002893 0.001888 0.0004202 0.4439 0.1775 0.005995 T 9.13 -3.42 -19.85 19.11 10.38 1.98 14.46 -14.02 29.29 6.55 1.86 -13.89 P 0.000 0.001 0.000 0.000 0.000 0.047 0.000 0.000 0.000 0.000 0.062 0.000 S = 12.94 R-Sq = 52 .9% R-Sq(adj) = 52.8% ANOVA Table Source Regression Residual Error Total DF 7 5283 5290 SS 99395118 83291 1877241 MS 90359 167 F 540.03 P 0.000 Independent Variables: Vup/Demand --ratio of arterial volume at the beginning of the link and demand ( Vdr/D)/N --average turning volume that enters the arterial link to demand ratio (vph) e *Xc --exponentiate Xc = arterial demand to capacity, = 3.977 FFS --arterial free-flow speed Vart-r --total arterial right-turning volume (vph) Vart-l --total arterial left-turning volume (vph) Vdr --total turning volume that enters the arterial link (vph) Vopp --total arterial traffic that opposes to left turns (vph) Ndr --number of driveways per 1000 ft P --parking frequency per 20 ft of available space (vph/ft) ( Vart-L* Vopp)/104 --Interaction term between arte rial right turning vehicles and arterial opposing volume (vph2) The arterial degree of saturation was transformed to exponential Xc, and the calculation of the parameter was done as described previously. The derived parameter estimate in this case is 3.977.

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67 The parking frequency for every 20 ft of av ailable parking space is found to be not significant considering a conf idence level of 95%; however, as the p-value slightly exceeds the threshold of 0.05 fo r that confidence level, it was decided to include the parameter in the final model. 0 20 40 60 80 100 120 140 160 00.20.40.60.811.2 Vup/DemandMid-Block Delay A 0 20 40 60 80 100 120 140 160 0100200300400500600 ( Vart-L* vopp)/104Mid-Block Delay B Figure 6-10 Relationship between mid-block delay and independent variables for the two-lane two-way congested model. (A) Arterial volume to demand ratio, Vup/Demand. (B) Interaction between total arterial left-turning volume and total arterial opposing volume, ( Vart-L* Vopp)/104.

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68 Discussion—Description of Independent Variables A more detailed description of the selected independent variables and a discussion of their relationship to mid-block de lay is presented in this section. CXe For the two-lane one-way model, the ar terial mid-block delay under uncongested conditions is found to increase exponentially with th e degree of saturation measured at the downstream signal. When the v/c ratio is lo w, the flow levels even at the downstream signal are low, which means that vehicles do not interact freq uently within the arterial segment. But as the v/c ratio increases and queues are formed at the downstream signal, the arterial operation is moving towards mo re congested conditions. This would mean that the vehicles are traveling in smaller h eadways and thus, their frequent interactions are expected to increase the mid-block delay. However, when a two-lane one-way ar terial is operating under congested conditions the degree of saturation affects nega tively the mid-block delay. When already in oversaturated conditions, an increase of the v/c ratio at the downstream signal means that the number of vehicles discharged is incr eased. This is an indication that the arterial operation moves towards uncongested states and to reduced mid-block delay. In the case of two-lane two-way arteri als that operate under congested conditions, as the degree of saturation increases then th e mid-block delay also increases. This is contrary to the congested model of the one -way arterial; however the two arterial configurations display differences in the tre nd of the degree of saturation. The arterial degree of saturation is not included in th e two-lane, two-way uncongested conditions model because it was not found to be significant.

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69 upV The arterial mid-block delay, under unc ongested conditions, increases as the feeding volume increases. This means that when the arterial volume is high and it operates under undersaturated conditions, there are more opportunities for vehicle interactions and more maneuveri ng actions that affect drivers’ speed and thus their travel times. On the other hand, under low flow c onditions, the mid-block delay is reduced. Demand Vup In congested conditions, the flow-to-dema nd ratio is significantly less than 1. Furthermore, as operations move towards more congested conditions, the actual throughput is reduced, and thus the ratio is al so reduced, which leads to an increase of arterial mid-block delay as a result of th e overall congestion. On the other hand, an increase of the ratio yields more arterial throughput and theref ore less congestion and delay for the through vehicles. This variable is used for the congested models of both arterial configurations. drN This variable represents the number of driveways involved in the study link per 1000 ft of arterial link length, as it is described in an earlier section. The driveway density is found to be positively relate d to the mid-block delay in all four models; as the number of driveways increases there are frequent occurre nces of vehicles that decelerate to either exit or enter the arterial segment; thus the through vehicles may encounter more delay within the link. FFS

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70 The arterial free-flow speed is also in cluded in all final models. Based on the analysis, it was shown that when the arteri al free-flow speed increases, then the midblock delay also increases. For example, if the free-flow speed is relatively high, then the through vehicles will have to decelerate more in order to reduce th eir speed significantly and avoid the turning vehicles that are traveling with low speeds. N Vdr The mid-block delay depends on the averag e number of vehicles that enter the arterial segment from the driveways through left or right turns. By increasing the number of incoming vehicles, the delay that the thr ough drivers experience is increased, due to additional vehicle interference. This variable appears in the uncongested models for both cases of two-lane one-way and two-way arterials with the same trend. In the two-lane, two-way congested model the total number of vehicles that enter the arterial segment from the driveways ( Vdr) is considered instead. N D Vdr/ The ratio of the driveway turning volu me to the respective demand, averaged through the corresponding drivew ays is found to be an im portant variable for both congested models. In congested conditions this ratio is reduced as there are not enough available gaps in the traffic stream for the dr iveway vehicles to discharge. However, an increase of the ratio means that the driv eway vehicles have more opportunities to discharge, thus the arterial operation moves towards less congested conditions with overall less mid-block delay. N Vart

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71 The average number of vehicles that exit the arterial through ri ght or left turns affects positively the mid-bloc k delay in uncongested conditi ons. As the average number of outgoing vehicles increases, there are more decelerations of the following vehicles and thus their delay is also increased. In congested conditions however, where th e mid-block delay ranges to high levels, the arterial throughput is reduced and conseque ntly the number of vehicles that perform right or left-turn maneuvers reduces. However, as congesti on dissipates and the arterial throughput gradually increases the number of turning vehicles also increases. L artV, R artV In the two-lane, two-way ar terial model (congested condi tions) the total arterial traffic that performs left and right turns is considered instead of the average turning activity. N Vopp The average arterial volume of the opposite di rection that conflicts with left-turning vehicles affects the mid-block delay of the through drivers of that direction. This parameter is very important for the two-wa y model particularly because the arterials under study have only one lane per direction; thus, a vehicle that performs a left-turn maneuver would have to search for available gaps and the following vehicles would have to stop. 410opp L artV V

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72 The total arterial left-turning traffic is interacted with the total volume from the opposing direction, in the two-lane two-wa y models, for both uncongested and congested conditions. In Equation (6-4) (two-way uncongested conditions) the in teraction impacts positively the mid-block delay. Additionally, as the total arterial left turning volume Vart-L, or the total opposing volume Vopp, increase, the mid-block delay also increases. In the two-way congested model (Equation (6 -5)) the interaction term parameter has negative value, but the overall effect of th e two independent variables involved remains positive. Note that the marginal effect of an arterial turning vehicle remains positive as long as the total opposing traffi c is less than 5,000 vph. This assumption is valid if one considers that there is only one lane per direction and a maximu m of four driveways involved. Similarly, the total opposing traffic has a pos itive effect in mid-block delay if the sum of the arterial left-turning traffic is less than approximately 1,500 vph. This is reasonable if the boundary condition of four driveways is taken into account. D T The bus dwell time is included in the an alytical model for the two-lane one-way only. The analysis shows that the bus dwell time indeed affects the through vehicles’ mid-block delay, as the arte rial through vehicles generally tend to decelerate when passing through a bus-stop. This variable is not included in the two-way models because buses were not present at the arteri als from the field data collection. P The parking activity influences positively the mid-block delay. Increasing parking activity for every 20ft of available parking sp ace yields an increase on the delay that the

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73 arterial through drivers experience. This variable appeared to be important only for the congested model of the two-lane, two-way arterials. Conclusions In this study four analytical models fo r estimating arterial mid-block delay are presented. These models estimate mid-block delay for two-lane two-way and two-lane one-way arterial streets that operate unde r both congested and uncongested conditions. Arterial travel time can be estimated as a f unction of the mid-block delay, the intersection control delay and the arterial running time when operating under free-flowing conditions. The final arterial travel time model for all four cases is expressed as: FFS LinkLength CD MBD TT (6 – 6) Where: TT is the arterial travel time (sec/veh). MBD is the mid-block delay and it is calcu lated from equations 6-2 through 6-5, according to the arterial configuration and the operating conditions (sec/veh). CD is the intersection control delay which can be obtained from the HCM methodology (Chapter 16) (sec/veh). Link Length/FFS is the arterial runni ng time under free-flowing conditions (ft/(ft/sec)). A descriptive example problem of the calcula tion of an arterial link travel time is presented in Appendix D. The example prob lem calculates the mid-block delay, the control delay and the arteri al running time under free-flow ing conditions components of the model for a two-lane one way arterial link.

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74 Different regression models are used dependi ng on whether the arterial is congested or not. To distinguish between the two conditi ons the ratio of the ar terial discharge over the demand needs to be calculated. This calcu lation is performed at the arterial segment just upstream of the traffic signal (between th e last driveway and the traffic signal). The discharge to demand ratio is expected to be approaching 1 as operations move towards uncongested conditions. This criteri on should be used with cau tion, as it is possible that the discharge to demand ratio is approaching 1, but the measured demand is reduced due to congestion on the upstream segment. In this case the models that correspond to congested conditions should apply. An important conclusion of this research is that both uncongested and congested models of the two-lane one-way arterials have better goodness-of-fit measures than the two-lane two-way models. The equations de veloped for the two-lane one-way models have reasonable R2 values; however in the twolane two-way models the R2 values are lower. This is mostly due to the different degree of complexity between the operations of the two arterial configurations. From the an alysis it can be seen that the distinction between the congested and uncongested states is very clear in th e case of the one-way arterials and the regression equations fit fairly well to the data. Additionally, the dataplots shown in Figures 6-7 and 6-8 are very desc riptive of the relati onship between the midblock delay and the independent variables. However, the vehicle interactions that ta ke place in the two-way arterials are much more complicated and for this reason the data plots in Figures 6-9 a nd 6-10 are scattered. Moreover, the two-lane two-way models we re developed from data taken from two

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75 adjacent arterial links; therefore, they also capture the effect of spillbacks, which is a realistic representation frequen tly observed in the field.

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76 CHAPTER 7 CONCLUSIONS AND RECOMMENDATIONS The research conducted for this thesis resu lted in some important conclusions and recommendations concerning the modeling lim itations of CORSIM and the analytical models of mid-block delay. The research findings associated with the modeling limitations of CORSIM are as follows: CORSIM allows for network-wide input of the maximum rightor left-turning speed but it does not allow for individual m odification of the turning speed at each driveway. The input of the turning speed at each driveway can be an important factor when analyzing the impact of right or left-t urning vehicles on the ar terial through traffic. In this research this issue was addressed by “forcing” the vehicles to enter the driveway with low free-flow speed, which corresponded to the site specificati ons of throat width and curb radius. The gap acceptance algorithm of CORSIM is based on the type of sign (stop or yield), the driver characteristic code (measure of aggressiveness) and the number of lanes that the driver has to cross. However, in real-life conditions, these factors are not the only basis for gap acceptance. It is more realistic that the drivers become impatient when waiting for a long time for a gap and this impatience usually reduces their safety margins. Thus, when waiting for a long time for a gap, it is possible that the drivers’ aggressiveness increases.

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77 The major results of the development of the analytical models that estimate arterial mid-block delay are summarized below: In uncongested conditions, the mid-block dela y is affected by the turning vehicles from the arterial or the driveway. The turni ng maneuvers increase the vehicle interactions and thus, they force the arterial through vehicl es to decelerate, which leads to an increase of their mid-block delay. On the other hand, under congested conditi ons turning vehicles from the arterial do not contribut e to the mid-block delay, as the arterial speed is already decreased. Also, vehicles turning into the arte rial do not affect midblock delay, as they have fewer opportunities to enter the arterial. In these situations, there is high delay on the arterial. In summary, the mid-block de lay under congested conditions is primarily influenced by the mainline volume and its degree of saturation. Since different variables influence mid-block delay under congested and uncongested conditions the two datasets of two-lane one-way and two-lane two-way arterials were divided in congested and uncongested. By observing the data it was perceived that for the same arterial thr oughput the arterial was operating under either free-flowing conditions or congestion. The selected criteri on for separating the data is the discharge to demand ratio at the downstream arterial signal. When the discharge to demand ratio is higher than 0.95 then the ar terial operates under uncongested conditions. When the ratio is below the 0.95 threshold th e arterial is congested. However, if the reduced discharge to demand ratio is the result of high demand at the downstream driveway while the overall arterial performan ce is free-flowing, then these conditions can also be considered as uncongested conditions. It is shown that this criterion conforms

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78 well to the data for the two-lane one way arte rials. For the two-lane two-way arterials the distinction is not as clear due to th e increased complexity of the system. The final regression models for the two arte rial configurations of study are given by the equations below: For two-lane one-way arterials under unc ongested conditions the mid-block delay (MBD) is: N V N V DT FFS N e MBDart dr dr XC 00488 0 00551 0 0161 0 261 0 73 1 266 0 08 8 R2 = 63.8% For two-lane one-way arterials under c ongested conditions the mid-block delay (MBD) is: N V N D V FFS N e Demand V MBDart dr dr X upC 262 0 172 359 0 4 10 184 0 2 66 285 R2 = 82.3% For two-lane two-way arterials under unc ongested conditions the mid-block delay (MBD) is: 410 105 0 608 0 00502 0 0128 0 0310 0 126 0 0126 0 9 13opp L art dr dr opp art upV V N N V N V N V FFS V MBD R2 = 64.7% For two-lane two-way arterials under c ongested conditions the mid-block delay (MBD) is: 410 0832 0 331 0 91 2 0123 0 0265 0 0418 0 00428 0 250 0 0983 0 / 6 25 99 6 8 17opp L art dr opp dr l art r art Xc dr upV V P N V V V V FFS e N D V Demand V MBD

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79 R2 = 52.9% where all variables as described in Chapter 6. An important result of the analysis is th at the models for the two-lane one-way arterials have reasonable goodne ss-of-fit measure. In the cas e of the two-lane two-way arterials the goodness-of-fit measur e of the models is low. This is mostly due to the enclosed degree of complexity in two-way ope rations, which results from the interactions between the two opposing traffic st reams. In two-way arterials it is not always possible to isolate traffic operations by move ment of direction, especially in the case of two-lane arterials, where the left turns have greater influence to the oncom ing through vehicles. The final travel time models derive from substituting the mid-block delay equations into the equation presented in Chapter 6 (E quation 6 – 6). Thus, the necessary steps for estimating the arterial travel time with this method are: Calculate the intersection control delay (can be calculated by applying the HCM methodology (Chapter 16)). Estimate the arterial link travel time under free-flowing conditions. Calculate the discharge to demand ratio at the downstream segment of the arterial link under study to determine its operational characteristics – define whether it is operating under congested or uncongested conditions and apply the corresponding equation. Refer to Chapter 6 for guidelines re garding the measurement of the discharge to demand ratio. Obtain all pertinent variables that apply to the mid-block equation. These may be the turning movements to and fr om all the driveways, the ar terial degree of saturation (measured just upstream of the intersection) the arterial throughput (measured at the

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80 beginning of the arterial link) with the corresponding dema nd, the arterial free-flow speed, the driveway density (per direction of travel), the average arterial volume that opposes left turns from the arterial, the park ing frequency for every 20 ft of available parking space, and the bus dwell time. The research findings provide a good insight to the various parameters that affect the travel time within an arte rial link, however it is important that the equations presented above are validated with additional field data. Furthermore, it is recommended that the development of travel time models with consideration of mid-block dela ys are extended to other arteri al configurations such as four-lane or six-lane two-way arterials.

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81 APPENDIX A PHASING—TIMING DIAGRAMS INTERSECTION OF NORTH ATHERTON STREET AND PARK AVENUE, STATE COLLEGE, PA PHASE 2+5 PHASE 2+6 PHASE 4 PHASE 3 Maximum 38 20 Minimum 2 19 10 Yellow 3 3 3 All-Red 2 2 Passage 2 2 Pedestrian 8 7* 10* Memory Non-Locking Ped. Recall Non-Locking Cycle 1 25 43 7 25 Cycle 2 31 32 7 20 Cycle 3 22 49 7 22 *Upon pedestrian actuation only Plan No. Time Cycle Offset Remarks 1 1:00 Flash 2 6:00 100 sec 86 sec Cycle 1 3 7:00 90 sec 39 sec Cycle 2 4 10:00 100 sec 86 sec Cycle 1 5 16:00 100 sec 68 sec Cycle 3 6 18:00 100 sec 86 sec Cycle 1 Offsets referenced to start of Phase 2+6 yellow

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82 INTERSECTION OF PARK AVENUE AND NORTH ALLEN STREET, STATE COLLEGE, PA PHASE 1 PHASE 2 PHASE 3 Maximum Minimum 2 2 Yellow 3 3 3 All-Red 1 1 Sec/act. 2 Max init. 23 Passage 1.5 5.4 2 To reduce 10 Before red. 23 Min gap 3 Pedestrian 16 16* Memory Non-Locking Ped. Recall Non-Locking Cycle 1 7 47 20 Cycle 2 7 43 24 Cycle 3 11 43 20 *Upon pedestrian actuation only Plan No. Time Cycle Offset Remarks 1 6:00 Free Cycle 1 2 7:15 85 sec 10 sec Cycle 1 3 8:15 Free Cycle 1 4 11:30 85 sec 10 sec Cycle 2 5 13:30 Free Cycle 1 6 14:15 85 sec 13 sec Cycle 3 7 17:45 Free Cycle 1 8 24:00 Offsets referenced to start of Phase 2 yellow

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83 INTERSECTION OF PARK AVENUE AND SHORTLIDGE ROAD, STATE COLLEGE, PA PHASE 1 PHASE 2 PHASE 3 Maximum Minimum 2 2 Yellow 3 3.5 3.5 All-Red 1.5 1.5 Sec/act. 2 Max init. 16 Passage 1.5 3.6 2 To reduce 10 Before red. 16 Min gap 3 Pedestrian 15 15* Memory Non-Locking Ped. Recall Non-Locking Cycle 1 7 45 20 Cycle 2 7 45 20 Cycle 3 7 39 26 *Upon pedestrian actuation only Plan No. Time Cycle Offset Remarks 1 6:00 Free Cycle 1 2 7:15 85 sec 22 sec Cycle 1 3 8:15 Free Cycle 1 4 11:30 85 sec 26 sec Cycle 2 5 13:30 Free Cycle 1 6 14:15 85 sec 26 sec Cycle 3 7 17:45 Free Cycle 1 8 24:00 Offsets referenced to start of Phase 2 yellow

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84 INTERSECTION OF WEST BEAVER AVENUE AND SPARKS STREET, STATE COLLEGE, PA PHASE 1 PHASE 2 Max G Min G 3 Yellow 3 3 All-Red 1 1 Passage Pedestrian7 14 Memory Non-Locking Ped. Recall Cycle 1 32 25 Cycle 2 60 30 Cycle 3 64 36 Plan No. Time Cycle Offset Remarks 1 1:00 Flash 2 6:00 65 sec Free Cycle 1 3 7:00 90 sec 1 sec Cycle 2 4 9:30 65 sec Free Cycle 1 5 16:00 100 sec 71 sec Cycle 3 6 18:00 65 sec Free Cycle 1 Offsets referenced to start of Phase 1 yellow

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85 INTERSECTION OF SOUTH ATHERT ON STREET AND WEST BEAVER AVENUE, STATE COLLEGE, PA PHASE 1+6 PHASE 2+6 PHASE 4 Maximum Minimum Yellow 3 3 3 All-Red 1 1 Passage 3 Pedestrian 11 19 Memory Max recall Min recall Ped. Recall Cycle 1 20 42 38 Cycle 2 15 35 40 Cycle 3 19 40 41 Plan No. Time Cycle Offset Remarks 1 0:00 100 sec 56 sec Cycle 1 2 7:00 90 sec 32 sec Cycle 2 3 10:00 100 sec 56 sec Cycle 1 4 16:00 100 sec 90 sec Cycle 3 5 18:00 100 sec 56 sec Cycle 1 Offsets referenced to start of Phase 2+6 yellow

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86 INTERSECTION OF EAST BEAVER AVENUE AND SOUTH PUGH STREET, STATE COLLEGE, PA PHASE 2 PHASE 4+8 Maximum 22 Minimum 32 Yellow 3 3 All-Red 1.5 2.5 Passage 3 Pedestrian7 14 Memory Min recall Ped. recall Cycle 1 41 39 Cycle 2 22 23 Cycle 3 58 42 Plan No. Time Cycle Offset Remarks 1 3:00 Flash 2 6:00 80 sec 3 sec Cycle 1 3 7:00 45 sec 20 sec Cycle 2 4 10:00 80 sec 3 sec Cycle 1 5 16:00 100 sec 75 sec Cycle 3 6 18:00 80 sec 3 sec Cycle 1 Offsets referenced to start of Phase 2 yellow

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87 INTERSECTION OF EAST BEAVER AVENUE AND SOUTH GARNER STREET, STATE COLLEGE, PA PHASE 2 PHASE 4+8 Maximum 18 Minimum 37 3 Yellow 3.5 3.4 All-Red 1.5 1.6 Passage 3 Pedestrian15 14 Memory Min recall Ped. recall Cycle 1 52 28 Cycle 2 59 31 Cycle 3 63 37 Plan No. Time Cycle Offset Remarks 1 0:00 80 sec 33 sec Cycle 1 2 7:00 90 sec 56 sec Cycle 2 3 10:00 80 sec 33 sec Cycle 1 4 16:00 100 sec 90 sec Cycle 3 5 18:00 80 sec 33 sec Cycle 1 Offsets referenced to start of Phase 2 yellow

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88 APPENDIX B TURNING MOVEMENT AND LOOP DETECTOR DATA PARK AVENUE, 04/20/2004, 11:45AM-12:45PM INTERSECTION DATA ST: Atherton Street ST: Atherton Street Northbound Southbound Time Left Thru Right Truck Total Left Thru Right Truck Total 11:45 12:00 0 212 35 9 247 88 198 0 7 286 12:00 12:15 0 249 34 8 283 97 204 1 9 302 12:15 12:30 0 223 32 16 255 99 211 0 8 310 12:30 12:45 0 199 42 15 241 99 196 0 6 295 SUM 0 883 144 47 1027 384 809 1 29 1194 % 0.00 0.86 0.14 0.05 0.32 0.68 0.00 0.02 ST: Park Avenue ST: Park Avenue Eastbound Westbound Time Left Thru Right Truck Total Left Thru Right Truck Total Total Int. 11:45 12:00 0 0 0 0 0 33 1 92 9 126 659 12:00 12:15 0 0 0 0 0 45 0 85 7 130 715 12:15 12:30 0 0 0 0 0 47 1 136 8 185 750 12:30 12:45 0 0 0 0 0 32 1 94 4 127 664 SUM 0 0 0 0 0 157 3 407 28 568 % 0.28 0.01 0.72 0.05 ST: Allen Street ST: Allen Street Northbound Southbound Time Left Thru Right Truck Total Left Thru Right Truck Total 11:45 12:00 13 3 18 1 34 0 1 4 1 5 12:00 12:15 26 2 30 2 57 1 1 1 0 3 12:15 12:30 25 2 20 1 47 0 1 0 0 1 12:30 12:45 11 0 19 3 31 1 1 0 0 2 SUM 75 7 88 7 169 2 4 5 1 11 % 0.44 0.04 0.52 0.04 0.19 0.38 0.43 0.09 ST: Park Avenue ST: Park Avenue Eastbound Westbound Time Left Thru Right Truck Total Left Thru Right Truck Total Total Int. 11:45 12:00 2 95 4 3 101 33 112 0 5 145.01 284 12:00 12:15 0 102 11 4 113 25 107 1 4 132 306 12:15 12:30 6 73 11 2 90 20 156 0 12 176 314 12:30 12:45 2 104 10 3 116 24 118 1 6 143 292 SUM 10 374 36 13 420 102 493 2 28 597 % 0.02 0.89 0.09 0.03 0.17 0.83 0.00 0.05

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89 ST: Shortlidge Road ST: Grove Alley Northbound Southbound Time Left Thru Right Truck Total Left Thru Right Truck Total 11:45 12:00 15 0 10 8 25 1 0 0 0 1 12:00 12:15 14 0 8 5 22 1 0 1 0 2 12:15 12:30 16 0 10 5 27 0 0 0 0 0 12:30 12:45 11 0 11 4 21 0 0 1 0 1 SUM 57 0 38 22 95 2 0 2 0 4 % 0.60 0.00 0.40 0.23 0.46 0.00 0.54 0.00 ST: Park Avenue ST: Park Avenue Eastbound Westbound Time Left Thru Right Truck Total Left Thru Right Truck Total Total Int. 11:45 12:00 1 100 13 4 113 13 133 0 7 146 285 12:00 12:15 0 125 6 5 131 5 121 0 4 126 281 12:15 12:30 0 82 10 3 93 13 164 0 14 177 296 12:30 12:45 0 117 8 3 126 13 135 1 8 150 298 SUM 1 424 38 15 463 44 553 1 33 598 % 0.00 0.92 0.08 0.03 0.07 0.92 0.00 0.06 DRIVEWAY DATA FIRST LINK: Between Atherton and Allen Driveway 3 (Lischer Rd) EB-R WB-L NB-L NB-R Time veh HV veh HV veh HV veh HV 11:45-12:00 30 4 0 8 2 12:00-12:15 23 1 2 2 12:15-12:30 15 2 0 6 2 12:30-12:45 30 3 1 1 7 TOTAL 98 11 3 27 Driveway 4 (N. Borrowes Str) EB-L WB-R SB-L SB-R Time veh HV veh HV veh HV veh HV 11:45-12:00 2 2 2 1 3 12:00-12:15 6 0 0 1 12:15-12:30 2 1 1 2 1 2 1 12:30-12:45 4 2 0 1 TOTAL 14 6 6 8 SECOND LINK: Between Allen and Sortlidge Driveway 1 (McKee) EB-L WB-R SB-L SB-R Time veh HV veh HV veh HV veh HV 11:45-12:00 1 5 1 5 12:00-12:15 3 1 4 3 1 4 12:15-12:30 5 5 1 5 5 1 12:30-12:45 4 4 4 4 TOTAL 14 19 11 8

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90 Driveway 2 (Holmes) EB-L WB-R SB-L SB-R Time veh HV veh HV veh HV veh HV 11:45-12:00 1 1 0 0 12:00-12:15 1 3 1 2 12:15-12:30 1 5 2 1 1 12:30-12:45 0 2 1 0 TOTAL 3 11 4 4 LOOP DETECTOR DATA BURROWES & ALLEN LISCHER & ATHERTON HOLMES & SHORTLIDGE EB WB EB WB EB WB 11:45-12:00 101 129 123 126 113 148 12:00-12:15 113 128 140 130 131 134 12:15-12:30 90 186 99 185 93 184 12:30-12:45 116 130 143 127 126 148 TOTAL 420 573 505 568 463 615 TURNING MOVEMENT AND LOOP D ETECTOR DATA FOR PARK AVENUE, 04/20/2004, 04:30PM-05:30PM INTERSECTION DATA ST: Atherton Street ST: Atherton Street Northbound Southbound Time Left Thru Right Truck Total Left Thru Right Truck Total 04:30-04:45 0 314 45 3 360 94 256 0 8 350 04:45-05:00 0 326 61 4 386 78 243 0 6 321 05:00-05:15 0 343 70 9 413 69 240 0 5 309 05:15-05:30 0 376 65 6 440 73 243 0 2 316 SUM 0 1359 241 22 1600 314 983 0 21 1297 % 0.00 0.85 0.15 0.01 0.24 0.76 0.00 0.02 ST: Park Avenue ST: Park Avenue Eastbound Westbound Time Left Thru Right Truck Total Left Thru Right Truck Total Total Int. 04:30-04:45 0 0 0 0 0 37 2 148 5 187 897 04:45-05:00 0 0 0 0 0 47 4 164 8 214 922 05:00-05:15 0 0 0 0 0 41 1 160 7 202 924 05:15-05:30 0 0 0 0 0 36 1 159 4 196 953 SUM 0 0 0 0 0 160 8 631 24 799 % 0.20 0.01 0.79 0.03

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91 ST: Allen Street ST: Allen Street Northbound Southbound Time Left Thru Right Truck Total Left Thru Right Truck Total 04:30-04:45 56 0 77 1 133 1 0 3 0 4 04:45-05:00 47 0 46 2 93 1 2 3 1 6 05:00-05:15 67 1 105 1 174 8 1 2 1 11 05:15-05:30 47 0 71 3 118 2 1 3 0 6 SUM 217 1 300 7 518 12 4 12 2 28 % 0.42 0.00 0.58 0.01 0.43 0.15 0.42 0.08 ST: Park Avenue ST: Park Avenue Eastbound Westbound Time Left Thru Right Truck Total Left Thru Right Truck Total Total Int. 04:30-04:45 4 123 21 3 148 37 137 3 3 177 463 04:45-05:00 4 94 11 0 108 41 161 1 6 203 411 05:00-05:15 4 123 13 1 140 34 130 7 7 171 496 05:15-05:30 7 123 11 1 141 29 147 3 4 179 444 SUM 19 462 56 5 537 141 575 14 21 731 % 0.04 0.86 0.10 0.01 0.19 0.79 0.02 0.03 ST: Shortlidge Road ST: Grove Alley Northbound Southbound Time Left Thru Left Thru Left Thru Left Thru Left Thru 04:30-04:45 25 0 25 0 25 0 25 0 25 0 04:45-05:00 19 0 19 0 19 0 19 0 19 0 05:00-05:15 24 0 24 0 24 0 24 0 24 0 05:15-05:30 22 0 22 0 22 0 22 0 22 0 SUM 90 0 90 0 90 0 90 0 90 0 % 0.59 0.00 0.59 0.00 0.59 0.00 0.59 0.00 0.59 0.00 ST: Park Avenue ST: Park Avenue Eastbound Westbound Time Left Thru Right Truck Total Left Thru Right Truck Total Total Int. 04:30-04:45 0 185 19 2 204 14 157 0 1 171 409 04:45-05:00 0 131 9 1 141 15 194 0 3 209 382 05:00-05:15 1 224 10 1 235 10 155 0 4 165 447 05:15-05:30 0 175 21 0 196 15 169 0 5 184 420 SUM 1 716 59 4 776 54 674 0 14 728 % 0.00 0.92 0.08 0.01 0.07 0.93 0.00 0.02 DRIVEWAY DATA FIRST LINK: Between Atherton and Allen Driveway 3 (Lischer Rd) EB-R WB-L NB-L NB-R Time veh HV veh HV veh HV veh HV 11:45-12:00 1 2 1 4 12:00-12:15 0 1 1 3 1 12:15-12:30 1 1 0 6 12:30-12:45 0 2 0 5 TOTAL 2 0.10 0.90

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92 Driveway 4 (N. Borrowes Str) EB-L WB-R SB-L SB-R Time veh HV veh HV veh HV veh HV 04:30-04:45 22 2 9 1 04:45-05:00 29 0 4 0 05:00-05:15 19 2 18 2 05:15-05:30 24 1 5 1 TOTAL 0.90 0.10 SECOND LINK: Between Allen and Sortlidge Driveway 1 (McKee) EB-L WB-R SB-L SB-R Time veh HV veh HV veh HV veh HV 04:30-04:45 1 5 3 2 04:45-05:00 2 11 4 1 3 2 05:00-05:15 1 3 1 1 05:15-05:30 1 5 0 0 TOTAL 0.57 0.43 Driveway 2 (Holmes) EB-L WB-R SB-L SB-R Time veh HV veh HV veh HV veh HV 11:45-12:00 2 0 3 1 12:00-12:15 3 3 1 0 12:15-12:30 2 6 1 0 12:30-12:45 0 9 1 1 TOTAL 0.75 0.25 LOOP DETECTOR DATA BURROWES & ALLEN LISCHER & ATHERTON HOLMES & SHORTLIDGE EB WB EB WB EB WB 04:30-04:45 148 189 158 187 204 179 04:45-05:00 108 214 130 214 141 214 05:00-05:15 140 203 136 202 235 179 05:15-05:30 141 198 155 196 196 192 TOTAL 537 804 579 799 776 765

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93 WEST BEAVER AVENUE BETWEEN SPARKS STREET AND SOUTH ATHERTON STREET, 04/22/2004, 07:30AM-08:30AM INTERSECTION DATA ST: Sparks Street ST: Sparks Street Northbound Southbound Time Left Thru Right Truck Total Left Thru Right Truck Total 04:30-04:45 0 7 28 1 34 3 8 0 1 11 04:45-05:00 0 15 38 9 54 4 7 0 0 11 05:00-05:15 0 9 17 1 26 1 9 0 1 10 05:15-05:30 0 4 20 0 24 8 5 0 3 13 SUM 0 35 103 11 137 17 28 0 5 45 % 0.00 0.25 0.75 0.08 0.37 0.63 0.00 0.10 ST: Beaver Avenue ST: ___ Eastbound Westbound Time Left Thru Right Truck Total Left Thru Right Truck Total Total Int. 7:30-7:45 0 194 4 8 198 0 0 0 0 0 244 7:45-8:00 1 210 11 4 222 0 0 0 0 0 286 8:00-8:15 2 170 4 7 176 0 0 0 0 0 211 8:15-8:30 1 190 4 11 196 0 0 0 0 0 233 SUM 4 764 23 30 791 0 0 0 0 0 % 0.01 0.97 0.03 0.04 ST: Atherton Street ST: Atherton Street Northbound Southbound Time Left Thru Left Thru Left Thru Left Thru Left Thru 04:30-04:45 0 72 0 72 0 72 0 72 0 72 04:45-05:00 0 137 0 137 0 137 0 137 0 137 05:00-05:15 0 128 0 128 0 128 0 128 0 128 05:15-05:30 0 149 0 149 0 149 0 149 0 149 SUM 0 486 0 486 0 486 0 486 0 486 % 0.00 0.76 0.00 0.76 0.00 0.76 0.00 0.76 0.00 0.76 ST: Beaver Avenue ST: ___ Eastbound Westbound Time Left Thru Right Truck Total Left Thru Right Truck Total Total Int. 7:30-7:45 72 120 11 11 203 0 0 0 0 0 485 7:45-8:00 94 137 24 5 256 0 0 0 0 0 713 8:00-8:15 67 125 13 3 205 0 0 0 0 0 564 8:15-8:30 67 127 24 9 218 0 0 0 0 0 596 SUM 301 510 72 29 882 0 0 0 0 0 % 0.34 0.58 0.08 0.03 DRIVEWAY DATA

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94 Driveway 1 Driveway 2 EB-L SB-L SB-TH SB-L Time veh HV veh HV veh veh HV veh 7:30-7:45 0 2 0 0 2 7:45-8:00 0 0 1 0 0 8:00-8:15 0 1 1 0 1 8:15-8:30 0 0 3 0 0 TOTAL 0 3 5 10 Driveway 3 Driveway 4 EB-L SB-L SB-TH SB-L EB-L Time veh HV veh HV veh HV veh HV veh HV 7:30-7:45 0 0 7 7 10 7:45-8:00 2 0 1 9 4 8:00-8:15 1 0 1 4 4 8:15-8:30 0 0 2 1 4 TOTAL 3 0 11 21 22 Driveway 5 Driveway 6 SB-TH SB-L EB-L NB-TH NB-R EB-R Time veh HV veh HV veh HV veh HV veh HV veh HV 7:30-7:45 0 0 0 3 1 3 1 7:45-8:00 0 0 0 5 4 0 8:00-8:15 0 2 0 2 2 2 8:15-8:30 0 0 0 1 4 2 TOTAL 0 2 0 12 13 5 Driveway 7 Driveway 8 NB-TH NB-R EB-R NB-TH NB-R EB-R Time veh HV veh HV veh HV veh HV veh HV veh HV 7:30-7:45 3 3 1 3 3 1 7:45-8:00 6 3 3 6 3 3 8:00-8:15 2 5 3 2 5 3 8:15-8:30 2 2 1 2 2 1 TOTAL 13 13 8 0 4 0 LOOP DETECTOR DATA DOWNSTREAM OF ATHERTON ST. & BEAVER AVE. 7:30-7:45 215 7:45-8:00 248 8:00-8:15 226 8:15-8:30 230 TOTAL 919

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95 WEST BEAVER AVENUE BETWEEN SPARKS STREET AND SOUTH ATHERTON STREET, 04/22/2004, 12:30AM-01:30PM INTERSECTION DATA ST: Sparks Street ST: Sparks Street Northbound Southbound Time Left Thru Right Truck Total Left Thru Right Truck Total 12:30 12:45 0 4 12 0 16 6 8 0 1 14 12:45 1:00 0 10 8 0 17 12 5 0 0 17 1:00 1:15 0 14 5 0 19 4 8 0 0 12 1:15 1:30 0 7 9 1 16 6 5 0 1 11 SUM 0 35 33 1 68 28 27 0 2 54 % 0.00 0.52 0.48 0.02 0.51 0.49 0.00 0.04 ST: Beaver Avenue ST: ___ Eastbound Westbound Time Left Thru Right Truck Total Left Thru Right Truck Total Total Int. 12:30 12:45 0 111 1 5 111 0 0 0 0 0 141 12:45 1:00 0 132 8 3 140 0 0 0 0 0 174 1:00 1:15 4 140 5 4 148 0 0 0 0 0 179 1:15 1:30 2 183 5 11 191 0 0 0 0 0 218 SUM 6 566 18 23 590 0 0 0 0 0 % 0.01 0.96 0.03 0.04 ST: Atherton Street ST: Atherton Street Northbound Northbound Time Left Left Thru Left Thru Left Thru Left Thru Left 12:30 12:45 0 0 111 0 111 0 111 0 111 0 12:45 1:00 0 0 144 0 144 0 144 0 144 0 1:00 1:15 0 0 145 0 145 0 145 0 145 0 1:15 1:30 0 0 150 0 150 0 150 0 150 0 SUM 0 0 550 0 550 0 550 0 550 0 % 0.00 0.00 0.79 0.00 0.79 0.00 0.79 0.00 0.79 0.00 ST: Beaver Avenue ST: ___ Eastbound Eastbound Time Left Left Thru Left Thru Left Thru Left Thru Left Total Int. 12:30 12:45 0 46 104 46 104 46 104 46 104 46 531 12:45 1:00 0 50 116 50 116 50 116 50 116 50 611 1:00 1:15 4 39 120 39 120 39 120 39 120 39 626 1:15 1:30 2 43 107 43 107 43 107 43 107 43 576 SUM 6 178 446 178 446 178 446 178 446 178 % 0.01 0.24 0.61 0.24 0.61 0.24 0.61 0.24 0.61 0.24

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96 DRIVEWAY DATA Driveway 1 Driveway 2 EB-L SB-L SB-TH SB-L EB-L Time veh HV veh HV veh veh HV veh HV veh 12:30 12:45 0 0 3 0 0 3 12:45 1:00 0 0 7 0 0 7 1:00 1:15 2 5 7 2 5 7 1:15 1:30 0 2 6 0 2 6 TOTAL 2 7 23 24 24 Driveway 3 Driveway 4 EB-L SB-L EB-L SB-L EB-L Time veh HV veh HV veh HV veh HV veh HV 12:30 12:45 0 0 0 0 0 12:45 1:00 0 0 0 0 0 1:00 1:15 0 0 0 0 0 1:15 1:30 0 0 0 0 0 TOTAL 0 4 0 4 0 Driveway 5 Driveway 6 SB-TH SB-L EB-L NB-TH NB-R EB-R Time veh HV veh HV veh HV veh HV veh HV veh HV 12:30 12:45 0 3 1 3 3 0 12:45 1:00 0 2 3 4 2 0 1:00 1:15 1 3 0 2 7 0 1:15 1:30 0 3 0 3 3 1 TOTAL 1 11 4 11 15 1 Driveway 7 Driveway 8 NB-TH NB-R EB-R NB-TH NB-R EB-R Time veh HV veh HV veh HV veh HV veh HV veh HV 12:30 12:45 2 6 3 2 6 3 12:45 1:00 5 5 4 5 5 4 1:00 1:15 6 6 2 6 6 2 1:15 1:30 3 3 2 3 3 2 TOTAL 16 20 11 1 10 2 LOOP DETECTOR DATA DOWNSTREAM OF ATHERTON ST. & BEAVER AVE. 12:30 12:45 221 12:45 1:00 251 1:00 1:15 272 1:15 1:30 223 TOTAL 967

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97 APPENDIX C TRAVEL TIME STUDY PARK AVENUE (EB) TRAVEL TIME STUDY AM PEAK HOUR 7:30 8:30 CONTROL POINTS SEGMENT LENGTH RUN 01 RUN 02 RUN 03 RUN 04 RUN 05 RUN 06 AVER AGE ST. DEV. ATHERTON ST. 0 0 0 0 0 0 0 ALLEN ST. 1330 53 54 44 30 37 32 41.67 10.4 SHORTLIDGE RD 1520 47 35 43 35 31 36 37.83 5.9 BIGLER RD 1030 47 28 24 50 21 21 31.83 13.2 MID DAY PEAK HOUR 11:45 AM 12:45 PM CONTROL POINTS RUN 01 RUN 02 RUN 03 RUN 04 RUN 05 RUN 06 RUN 07 AVER AGE STD EV ATHERTON ST. 0 0 0 0 0 0 0 ALLEN ST. 70 28 39 34 32 38 36 39.57 13.9 SHORTLIDGE RD 32 29 44 39 41 52 36 39.00 7.7 BIGLER RD 19 47 36 27 33 27 64 36.14 15.1 PM PEAK HOUR 4:30 5:30 PM CONTROL POINTS RUN 01 RUN 02 RUN 03 RUN 04 RUN 05 AVER AGE STDE V ATHERTON ST. 0 0 0 0 0 ALLEN ST. 75 31 70 39 53 53.60 19.0 SHORTLIDGE RD 37 37 34 34 33 35.00 1.9 BIGLER RD 21 23 19 23 22 21.60 1.7 PARK AVENUE (WB) TRAVEL TIME STUDY AM PEAK HOUR 7:30 8:30 CONTROL POINTS SEGMENT LENGTH RUN 01 RUN 02 RUN 03 RUN 04 RUN 05 RUN 06 AVER AGE ST. DEV. BIGLER RD 0 0 0 0 0 0 0 SHORTLIDGE RD 1035 25 24 42 26 20 21 26.33 8.0 ALLEN ST. 1520 33 32 42 30 39 44 36.67 5.8 ATHERTON ST. 1415 228 111 159 93 87 43 120.17 64.8

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98 MID DAY PEAK HOUR 11:45 AM 12:45 PM CONTROL POINTS RUN 01 RUN 02 RUN 03 RUN 04 RUN 05 RUN 06 RUN 07 AVER AGE STD EV BIGLER RD 0 0 0 0 0 0 0 SHORTLIDGE RD 34 22 23 21 22 20 26 24.00 4.8 ALLEN ST. 36 33 49 35 35 29 40 36.71 6.3 ATHERTON ST. 93 64 117 66 61 57 71 75.57 21.7 PM PEAK HOUR 4:30 5:30 PM CONTROL POINTS RUN 01 RUN 02 RUN 03 RUN 04 AVER AGE STDE V BIGLER RD 0 0 0 0 SHORTLIDGE RD 21 45 63 103 57.60 30.0 ALLEN ST. 129 178 238 195 154.80 78.0 ATHERTON ST. 172 167 193 192 170.40 26.4 BEAVER AVENUE TRAVEL TIME STUDY AM PEAK HOUR 7:30 8:30 CONTROL POINTS SEGM. LENGTH RUN 01 RUN 02 RUN 03 RUN 04 RUN 05 RUN 06 RUN 07 RUN 08 AVER AGE ST. DEV. SPARKS ST. 0 0 0 0 0 0 0 0 0 ATHERTO N ST. 1395 90 90 88 89 53 32 90 41 71.63 25.2 BURROW ES ST. 463 20 18 15 16 23 29 23 18 20.25 4.6 FRASER ST. 375 15 23 12 11 17 17 15 28 17.25 5.7 ALLEN ST. 494 18 17 13 14 37 30 17 10 19.50 9.2 PUGH ST. 436 15 13 27 20 18 44 15 13 20.63 10.5 GARNER ST. 1263 36 58 33 32 33 40 32 33 37.13 8.9 MID DAY PEAK HOUR 12:30 1:30 CONTROL POINTS RUN 01 RUN 02 RUN 03 RUN 04 RUN 05 RUN 06 RUN 07 AVER AGE STD EV SPARKS ST. 0 0 0 0 0 0 0 ATHERTON ST. 64 70 48 16 146 68 116 75.43 43.1 BURROWES ST. 74 31 14 46 37 16 21 34.14 21.0 FRASER ST. 11 49 13 16 51 16 35 27.29 17.4 ALLEN ST. 22 54 19 29 49 58 22 36.14 16.9 PUGH ST. 47 15 47 44 13 27 40 33.29 14.8 GARNER ST. 39 36 32 34 28 39 39 35.29 4.2

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99 PM PEAK HOUR 5:00 6:00 CONTROL POINTS RUN 01 RUN 02 RUN 03 RUN 04 RUN 05 RUN 06 RUN 07 AVER AGE STD EV SPARKS ST. 0 0 0 0 0 0 0 ATHERTON ST. 96 92 90 94 88 91 141 98.86 18.8 BURROWES ST. 18 20 17 16 16 15 30 18.86 5.2 FRASER ST. 42 48 55 32 40 54 15 40.86 14.0 ALLEN ST. 59 58 51 20 17 59 45 44.14 18.3 PUGH ST. 17 17 17 50 47 21 49 31.14 16.5 GARNER ST. 30 40 34 35 40 27 42 35.43 5.6

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100 APPENDIX D EXAMPLE PROBLEM Example Problem The Arterial Link A two-lane, one-way arterial link w ith two signalized intersections at 2,000 ft spacing. The Question What is the arterial travel time? The Facts Field measured FFS = 40 mph 3 Driveways Effective green-to-cycle length ratio g/C = 0.60 (a ll through lane groups) Saturation flow rate = 1800 vph Cycle Length = 100 s Degree of saturation Xc = 0.75 Arterial and driveway turning volumes as shown in sketch. Arterial discharge at signal = 1,530 vph Demand at downstream driveway 300 vph No buses Pretimed control Analysis period = 1 hour Outline of Solution Obtain the discharge to demand ratio according to the measurements specified in attached sketch Compute mid-block delay. Then compute control delay according to the Highway Capacity Manual methodology (Chapter 16). Since no signal progression and no traffic filtering or metering takes place upstream of the first signal, assume that its PF= 1.0 and I= 1.0. The following steps describe computations of the mid-block delay, the cont rol delay and the arterial link travel time. L = 2,000 ft VDR-R = 320 vph FFS = 40 mph g/C = 0.60 C = 100 s VAR-R = 270 vph VAR-L = 250 vph VDR-L = 295 vph VDR-R = 285 vph DDR = 300 vph VAR = 1,530 vph VAR-TH = 1,245 vph VAR-R = 290 vph N

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101 1. Calculate the discharge to demand ratio. Demand measured as the discharge from the previous segment plus the driveway demand: Demand = VAR-TH + DDR = 1,245 + 300 = 1,545 vph Discharge at the downstream signal: VAR = 1,530 vph. Discharge to Demand ratio: 1,530/1,545 = 0.99 2. Distinguish operational condition. Since the discharge-to-demand ratio is greater than 0.95 the arterial link is considered to be uncongested. Equation 6–2 will be used for the calculation of mid-block delay. 3. Calculate mid-block delay (use Equation 6–2 or 6–3). veh s M BD e MBD N V N V DT FFS N e MBDart dr dr XC/ 08 13 270 00488 0 300 00551 0 0 0161 0 40 261 0 ) 2000 / 1000 3 ( 73 1 266 0 08 8 00488 0 00551 0 0161 0 261 0 73 1 266 0 08 875 0 951 3 4. Calculate d1 (use Equation 16-11 from HCM 2000). veh s d X C g C g C dc/ 55 14 ) 75 0 60 0 1 ( ) 60 0 1 ( 100 5 0 ) 0 1 min( 1 1 5 01 2 2 1 5. Calculate d2 (use Equation 16-12 from HCM 2000). veh s d cT kIX X X T dc c c/ 63 2 1 ) 60 0 1700 2 ( 75 0 1 5 0 8 ) 1 75 0 ( ) 1 75 0 ( 1 900 8 ) 1 ( ) 1 ( 9002 2 2 2 6. Obtain control delay (Equation 16-9 from HCM 2000). veh s d d PF d CD / 18 17 0 63 2 1 55 143 2 1 7. Calculate travel time under free-flowing conditions. s mph ft FFS LinkLength 73 40 47 1 / 2000 / 8. Obtain link travel time (Equation 6–6) veh FFS LinkLength CD MBD TT sec/ 59 103 73 18 17 08 13

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102 LIST OF REFERENCES American Association of State Hi ghway and Transpor tation Officials (2001). A Policy on Geometric Design of Highways and Streets, Washington, D.C. Alexander M. H. (1970). Development of an Economic Warrant for the Construction of Right-Turn Deceleration Lanes. Final Report. Joint Highw ay Research Project C36-17HH. Purdue University, Lafayette, Indiana Bonneson J. A. (1998). Delay to Major Street Through Vehicles Due to Right-Turn Activity. Transportation Research Part A, Vol. 32, No. 2, Elsevier Ltd., pp. 139-148 Bonneson J. A., and J. W. Fitts (1999). Delay to Major Street Through Vehicles at TwoWay Stop-Controlled Intersections. Transportation Research Part A, Vol. 33, No. 34, Elsevier Ltd., pp. 237-253 Bonneson J. A., and P. T. McCoy (1997). Capacity and Operational Effects of Midblock Left-Turn Lanes. National Cooperative Highway Research Program Report 395, Transportation Research Board, Nationa l Research Council, Washington D.C. Federal Highway Ad ministration (1995). TRAF/NETSIM. TRAF User Reference Guide. Version 5.0. U.S. Dept. of Transpor tation, Washington, D.C. Federal Highway Ad ministration (2003). Traffic Software Integrated System Version 5.1. ITT Industries Inc., Colorado Springs, Colorado Gluck J. S., H. S. Levinson, and V. Stover (1999). NCHRP Report 420: Impacts of Access Management Techniques. Highway Research Board, National Research Council, Washington, D.C. Gluck J. S., G. Haas, J. Mahmood, and H. S. Levinson (2000). Driveway Spacing and Traffic Operations. Urban Street Symposium, Trans portation Research E-Circular E-C019, Dallas, Texas Kyte M., Z. Tian, Z. Mir, Z. Hameedmansoor W. Kittelson, M. Vandehey, B. Robinson, W. Brilon, L. Bondzio, N. Wu, and R. Troutbeck (1996). Capacity and Level of Service at Unsignalized Intersections. NCHRP Project 3-46 – Final Report, Vol. 1, Transportation Research Board, Nationa l Research Council, Washington, D.C. Lin W. H., A. Kulkarni, a nd P. Mirchandani (2003). Arterial Travel Time Estimation for Advanced Traveler Information Systems. Transportation Research Board 82nd Annual Meeting, Washington, D.C.

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103 McShane W. R. (1995). Access Management and the Rela tion to Highway Capacity and Level of Service. Technical Memorandum on Activity 4; Final Report, RS&H Project 992 1062 001, Florida Intrastate Highway System, Tallahassee, Florida Olszewski P., (2000). Comparison of the HCM and Si ngapore Models of Arterial Capacity. 4th International Symposium on Highway Capacity, pp. 209-220, Maui, Hawaii Stover V. G., W. G. Atkins and J. C. Goodknight (1970). NCHRP Report 93: Guidelines for Medial and Marginal A ccess Control on Major Roadways. Highway Research Board, National Research Council, Washington, D.C. Stover V. G., and F. J. Koepke, (1988). Transportation Land Development. Institute of Transportation Engineers. Prenti ce Hall, Englewood Cliffs, N.J. Transportation Research Board (2000). Highway Capacity Manual, National Research Council, Washington D.C. Transportation Research Board (2003). Access Management Manual, National Research Council, Washington D.C. Wolfe A., and W. Lane (1999). Effects of Curvature for Right Turning Vehicles on Through Traffic Delay. 4th International Symposium on Highway Capacity, pp. 388-396, Maui, Hawaii Wolfe A., and J. Piro (2003). Delay to Through Vehicles Due to Right-Turn Activity. Transportation Research Board 82nd Annual Meeting, Washington, D.C.

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104 BIOGRAPHICAL SKETCH Ms. Alexandra Kondyli is a research assistant at th e Transportation Research Center of University of Florida, at the Depa rtment of Civil And Coastal Engineering. Ms. Kondyli received her graduate diploma in rural and surveying engineering from the National Technical University of Athens, Greece, in June 2003.


Permanent Link: http://ufdc.ufl.edu/UFE0013260/00001

Material Information

Title: Development of an Arterial Link Travel Time Model with Consideration of Mid-Block Delays
Physical Description: Mixed Material
Copyright Date: 2008

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Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0013260:00001

Permanent Link: http://ufdc.ufl.edu/UFE0013260/00001

Material Information

Title: Development of an Arterial Link Travel Time Model with Consideration of Mid-Block Delays
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: UFE0013260:00001


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DEVELOPMENT OF AN ARTERIAL LINK TRAVEL TIME MODEL WITH
CONSIDERATION OF MID-BLOCK DELAYS













By

ALEXANDRA KONDYLI


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

UNIVERSITY OF FLORIDA


2005

































Copyright 2005

by

ALEXANDRA KONDYLI

































This document is dedicated to the graduate students of the University of Florida.















ACKNOWLEDGMENTS

The author would like to thank her graduate advisor, Dr. Lily Elefteriadou of the

University of Florida for her insights and guidance throughout this thesis and her

valuable support.

The author wishes to thank the remaining members of the thesis committee, Dr.

Scott Washburn and Dr. Ruth Steiner, for their assistance and their advices.

Finally, the author expresses her sincere thanks to TransAssociates Consultant Firm

at State College, PA, for their assistance during the data collection.
















TABLE OF CONTENTS



A C K N O W L E D G M E N T S ................................................................................................. iv

LIST OF TA BLE S ............................... ....... .. .. .. ........... .............. .. vii

LIST OF FIGURES ............. ............. ........ ....... .......................... viii

A B ST R A C T .......... ..... ...................................................................................... x

CHAPTER

1 IN TR OD U CTION ............................................... .. ......................... ..

B a c k g ro u n d ............................................................................................. 1
Problem Statem ent .................. ............................. .. ........... ............. ..
O bjectiv e s ................................................................... ................................. . .2

2 LITER A TU R E REV IEW ............................................................. ....................... 5

H ighw ay C capacity M annual ........................................ ................................. 5
R ight T urns from A rterial ............................................................................. ..... .8
L eft Turns from A rterial ........................................... ....................................... 13
Access Management and Driveway Spacing...........................................................14
The Significance of Mid-Block Effects ....................................... ...............16
Sum m ary of the Literature Review ................................. ................... ...... ........ 17

3 M E T H O D O L O G Y ........................................................................ .......................19

Data Collection .................. ............... ........................................... 19
Simulation Model Development and Calibration.....................................................19
D design of E xperim ents ....................................................................... ..................20
D database Expansion...................................................................... .. .......... 21
Data Analysis and Formulation of Regression Models............................................22

4 D A T A C O L L E C T IO N ................................................................... ....... ...............25

D ata R equirem ents......... .................................................................. .. ....... .... ..25
D description of Study Sites ................................................ .............................. 26
D ata C collection M ethods ........................................ .............................................27



v









5 SIMULATION MODEL DEVELOPMENT.............................................................31

S im u latio n P ack ag e .......................................................................... ........... .. ..3 1
M odel D evelopm ent .............................. .......................... .. ........ .... ..... ...... 32
M odel Calibration ......... .. ................ .. .... ........... ......... .. ... .... 35
Sum m ary and C onclu sions .............................................................. .....................39

6 ANALYTICAL MODEL DEVELOPMENT........................... ....................41

D database E expansion ............................ .. .... .................... 4 1
Selection of Simulation Output Performance Measures..............................44
D database O organization ......... ............................................................ .. ... .... ....... 45
Data Analysis....................................................... ...... ........ 46
Selection of Candidate V ariables.......................................... ........... ............... 46
R egression M odels........... ... ......... ...... ..................... .. .......... ..... ...... .. 54
Regression Model for Two-Lane One-Way Arterials.................... ............... 59
Regression Model for Two-Lane Two-Way Arterials .....................................63
Discussion-Description of Independent Variables............... ......... ............... 68
C o n c lu sio n s..................................................... ................ 7 3

7 CONCLUSIONS AND RECOMMENDATIONS ............................................... 76

APPENDIX

A PHASING-TIMING DIAGRAMS .............................................................. 81

B TURNING MOVEMENT AND LOOP DETECTOR DATA ..................................88

C TR A V E L TIM E STU D Y ................................................................ .....................97

D EX A M PLE PR O B LEM ............................................................................ ....... 100

L IST O F R E FE R E N C E S ........................................................................ ................... 102

BIOGRAPHICAL SKETCH ............................................................. ............... 104
















LIST OF TABLES


Table p

4-1 Determination of number of vehicle runs based on field measured travel time.......28

5-1 Calibration parameters for Park Avenue midday and p.m. models .......................37

5-2 Calibration parameters for Sparks p.m model ............... ................ ................... 37

5-3 Calibration parameters for Pugh midday and p.m. models.................................37

5-4 Field measured vs. simulation travel time after calibration ...................................38

6-1 Two-lane two-way simulation model inputs for database expansion ....................43

6-2 Two-lane one-way simulation model inputs for database expansion. First group
of scenarios (from Beaver Avenue at Sparks Street) ............................................ 43

6-3 Two-lane one-way simulation model inputs for database expansion. Second
group of scenarios (from Beaver Avenue at Pugh Street)....................................44

6-4 Selected performance measures extracted from CORSIM ....................................45

6-5 Mid-block delay equations for two-lane one-way uncongested conditions.............60

6-6 Mid-block delay equations for two-lane one-way congested conditions ...............62

6-7 Mid-block delay equations for two-lane two-way uncongested conditions.............64

6-8 Mid-block delay equations for two-lane two-way congested conditions................66















LIST OF FIGURES


Figure page

1-1 Illustration of mid-block delay phenomena ..... ......... ..................................3

4-1 Two-lane two-way arterial (Park Avenue)........ ..............................................26

4-2 Two-lane one-way arterial (Beaver Avenue)................. ...... ... ........................ 27

4-3 First two-lane one-way arterial link (Beaver Avenue between Sparks St and
A th erto n S t) .................................................................... ................ 2 9

4-4 Second two-lane one-way arterial link (Beaver Avenue between Pugh St and
G a rn e r S t) ......................................................................... 2 9

4-5 Two successive two-lane two-way arterial links (Park Avenue between N.
Atherton St and N. Allen Rd and Park Avenue between N. Allen Rd and
Shortlidge R d) ........................................................................30

6-1 Sketch of variable lVdr/N ................. ...................................... 48

6-2 Sketch of variable V a/N ......... .................................................... ............... 50

6-3 M easurement of discharge to demand ratio. ................................. .................54

6-4 Dataplot of mid-block delay vs. discharge to demand ratio for two-lane one-way
arterials. .............................................................................56

6-5 Dataplot of mid-block delay vs. discharge to demand ratio for two-lane two-way
arterials. (A) Mid-block delay and discharge to demand ratio for low volume
level. (B) Mid-block delay and discharge to demand ratio for high volume level...57

6-7 Relationship between mid-block delay and independent variables for the two-
lane one-way uncongested model. (A) Average arterial turning volume, IVan/N.
(B) Average driveway turning volume, lVdr/N .............. ................... ................61

6-8 Relationship between mid-block delay and independent variables for the two-
lane one-way congested model. (A) Average arterial turning volume, lVat/N.
(B) Average driveway turning volume to demand ratio, (lVdr/D)/N. ...................63









6-9 Relationship between mid-block delay and independent variables for the two-
lane two-way uncongested model. (A) Interaction between total arterial left-
turning volume and total arterial opposing volume, (lVart-L* IVopp)/104. (B)
Number of driveways per 1000 ft, Ndr ........................... ................................. 65

6-10 Relationship between mid-block delay and independent variables for the two-
lane two-way congested model. (A) Arterial volume to demand ratio,
Vup/Demand. (B) Interaction between total arterial left-turning volume and total
arterial opposing volume, (IVart-L* IVopp)/10 4.............................. ................. 67















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 Science

DEVELOPMENT OF AN ARTERIAL LINK TRAVEL TIME MODEL WITH
CONSIDERATION OF MID-BLOCK DELAYS

By

Alexandra Kondyli

December 2005

Chair: Ageliki Elefteriadou
Major Department: Civil and Coastal Engineering

This thesis presents analytical models for estimating arterial travel time with

consideration of delays at mid-block locations. The mid-block delays are defined as the

delays that through drivers experience due to turning maneuvers of either the other major

stream vehicles ahead that exit from the arterial or the minor stream vehicles that enter

the arterial. These delays typically occur at the intersections with driveways. Volume and

travel time data are collected in two-lane two-way and two-lane one-way arterials. These

data are used for the development of simulation models and the expansion of the database

through simulation. The generated data are used for the development of analytical

equations of mid-block delay through regression. The final regression equations provide

estimation of arterial mid-block delay depending on the conditions that the arterial

operates (congested uncongested conditions).














CHAPTER 1
INTRODUCTION

Background

Arterial roadways are designed to provide both accessibility and mobility to the

users. Those two contradictory functions define the level of control of the arterials. There

can be variable combinations of these, with respect to the land use and roadside

development. According to the Highway Capacity Manual [HCM 2000] (Transportation

Research Board, 2000) and the Policy on Geometric Design of Highways and Streets

(AASHTO, 2001) arterials can be designated as high-speed, suburban, intermediate and

urban based on their design and as principal or minor, based on their functionality.

Arterials play a very important role in the roadway system.

A quantitative assessment of the factors that affect mid-block performance on

urban arterial streets is important for the determination of the total link delay, as it is

perceived by the drivers. The estimation of mid-block arterial delay is important for the

following reasons. First, it provides a complete delay estimation procedure that is

partially and not wholly dependent on the intersection delay. Second, it is closer to

reality, and it can assess the significance of the various mid-block phenomena into the

overall arterial link performance.

Problem Statement

Several traffic studies have focused on the operational characteristics of the arterial

streets and their capacity, indicating that the estimation/prediction of delays and travel

times is important information for the users. However, no study has developed a model









for estimating travel time on arterial links by including various parameters of arterial

mid-block performance.

Objectives

The main objective of this research is to develop an analytical model that will

predict arterial link travel time considering the delays of mid-block phenomena. The mid-

block delays are defined as the delays that through drivers experience due to turning

maneuvers of either the other major stream vehicles ahead that exit from the arterial or

the minor stream vehicles that enter the arterial. These delays typically occur at the

intersections with driveways. The model will also include delays due to parking activities

and bus stops. Figure 1 1 illustrates the mid-block phenomena that contribute to the

increase of arterial travel time and that are explored in this research. The upper part of the

figure shows a vehicle from the arterial (vehicle 1) reducing speed to make a right turn on

driveway #1. This maneuver will force the oncoming vehicle to decelerate as well in

order to maintain a safe distance. Similarly, vehicle 2 enters the arterial with lower speed

than the oncoming vehicle. This will possibly cause the oncoming vehicle to decelerate.

The lower part of Figure 1 1 illustrates an arterial vehicle (vehicle 3) about to perform a

parking maneuver and by doing so, it will decelerate and cause the following vehicle to

decelerate as well. Lastly, as vehicle 4 performs a left-turn maneuver from the driveway

onto the arterial it can force oncoming vehicles from both directions to reduce their

speeds to avoid a collision.












Vehicle tunintg nght
DRIVEWAY 2 nt the 1

OnconDRIVEWAYg eh3 DRIVEWAY 4



Ongoing vehcle
nd .. .g
nt. the dne, |y
DRIVEWAY 1

DRIVEWAY 3 DRIVEWAY 4


ffi~


Oncomn g vehicle
decelerates Vehicle perform es
parking maneuver


I Vehicle turning left
onto the arteal

I __{_ I
" n "7 "


Oncomng vehicles fom
both directions decelerate


Figure 1-1 Illustration of mid-block delay phenomena

The tasks of this research are as follows:

* Critically review all pertinent literature that involves arterial travel time estimation
as a function of the arterial turning maneuvers.

* Conduct field measurements of volumes and travel times in urban arterials to be
used for the development and calibration of the simulation models. The study
streets are two-lane, two-way and two-lane, one-way arterials.

* Generate new data according to a prespecified design of experiments.

* Use the expanded dataset for the development of analytical models using regression
analysis. The final models express the mid-block delay as a function of various
parameters such as the arterial through and turning volume, the driveway volume,
the arterial degree of saturation and others.

The next chapter of this thesis summarizes the literature review. The third chapter

presents the methodology to be followed in this research. The fourth chapter presents the

data collection effort and the arterials under study. The fifth chapter discusses the

simulation model development and the model calibration. The sixth chapter describes the

experimental design and the development of the analytical models through regression.


r


F






4


The last chapter discusses the findings of this thesis and presents recommendations for

further research.














CHAPTER 2
LITERATURE REVIEW

The literature review includes several components. First, the respective chapters of

the HCM 2000 have been reviewed. Additionally, studies that estimate delays due to

right or left turn maneuvers are presented. Also, studies that report the effect of driveway

spacing as a result of access management techniques to the overall arterial operations are

also reviewed. Finally, literature that suggests that arterial delay models should

incorporate the effects of mid-block phenomena is presented.

Highway Capacity Manual

The HCM 2000 provides methodologies for the evaluation of urban streets by

determining arterial link Level of Service and computing the intersection delay.

Information related to the urban streets methodology can be found in Chapter 15.

The first step of the methodology of urban streets is to define the arterial's

classification, which is based on both the design and functional characteristics. The

design characteristics of the arterials are related to the arterial posted speed limit, the

signal density, the driveway/ access point density and other design features. Based on

these characteristics, the arterial street classification is determined, which, in the next

step, affects the arterial running speed, along with other parameters, such as the free-flow

speed, and length of the segment. The arterial running speed is very important for the

LOS analysis, because when combined with the intersection control delay, it is used for

the calculation of the arterial through-vehicle travel speed for the segment or for the

entire link under consideration. According to the travel speed and the arterial









classification, the level of service of the arterial segment is calculated. Evaluating the

arterial analysis methodology of the HCM 2000 (Chapter 15) it is concluded that it does

not explicitly account for phenomena such as driveway density, cross-street traffic

blocking the through movement, and arterial turning maneuvers that impede the

oncoming vehicles. This chapter mentions that the arterial running time is affected by the

presence of parking; however the methodology does not quantify this effect.

The HCM 2000 also includes methods for estimating delays at both signalized and

unsignalized intersections. These methods are widely used and are accepted among

engineers.

For signalized intersections, the HCM 2000 (Chapter 16) provides a procedure for

estimating the delay and the level of service. The methodology calculates the control

delay, which includes movements at lower speeds and stops at intersection approaches as

vehicles move up in a queue or slow down at an intersection. The delay formula accounts

for uniform arrivals, for random arrivals and oversaturated queues and for initial queues.

The formula that estimates the delay assuming uniform arrivals is based on the Webster's

formula and it is widely accepted and used in practice.

The presence of buses and parking frequency is taken into consideration for the

saturation flow rate methodology (HCM 2000, Chapter 16). For the determination of the

saturation flow rate two adjustment factors are introduced; the bus-blockage adjustment

factor and the adjustment factor for existence of a parking lane and parking activity

adjacent to the lane groups. However, these factors are considered to affect the traffic

stream only within 250 ft from the signal and not at mid-block locations.









For two-way stop-controlled intersections and T-intersections with a single minor

street approach, the HCM 2000 (Chapter 17) provides methods for estimating delays and

levels of service for the minor approaches caused by the priority approaches. The delays

are calculated for minor approach vehicles that are crossing the major street, turning right

on the major street, turning left on the major street, and turning left from the major street,

depending on the minor street volume, major street volume, and follow-up time. Delays

are also calculated for the major street through or right turn vehicles that are impeded by

the left turning vehicles when there is a shared lane on the major-street approach and no

exclusive left-turn pockets are provided. The HCM 2000 also notes that these delays

usually 'have very small effect because the major street usually provides enough space

for the blocked (through) vehicle to sneak by or bypass the left-turning vehicle'.

Based on the HCM 2000, it can be concluded that a more detailed analysis of the

segment (on the level of individual major-minor street intersection) would require the

application of the unsignalized intersections methodology, which, according to the

aforementioned, provides the delays of the minor approaches and only of the major

approach left turn. The HCM 2000 does not provide a methodology for calculating the

delays that the major street vehicles may incur due to right-turn maneuvers from the

arterial, right-turn maneuvers and left turn maneuvers from the minor street, and such

delays are not taken into consideration for the analysis of the entire arterial segment.

More specifically, the HCM 2000 mentions that ". .. in special cases, there might be

midblock delays caused by vehicle stops at pedestrian crosswalks, or other delays caused

by bus stops or driveways." These mid-block delays can be directly incorporated into the

methodology provided that the user already has an estimate of their value.









Right Turns from Arterial

Two studies that were conducted in 1970 are focused on the impact of right-turn

vehicles to the delay of the through vehicles. In the first study, Stover et al. (1970) used

simulation to quantify the effect of right-turning vehicles. For the calibration of the

model, deceleration and right-turn speed data from aerial time-lapse photographs were

used. The simulation analysis considered the effect of major-street flow rate, proportion

of right-turns, and driveway entrance speed. The authors' simulation results show that

through vehicle delays increased with increasing major-street flow rate, and are higher

under low right-turning speed, but their findings may not be valid nowadays due to

changes in the driving behavior.

The second study was conducted by Alexander (1970). He observed traffic

operations at seven mostly urban intersections on two-lane two-way highways in Indiana

to determine the delay to through traffic due to right-turning vehicles. The following

equation was developed based on a regression analysis of the field-measured delay and

flow rates:

Dt = -219 + 2.OSQr + 0.37Q + 4.33u (2 1)

Where:
Dt = total through vehicle delay,
Qr = right-turn flow rate, vph,
Q = major-street flow rate, vph,
u = major-street running speed, m/s.

The R2 for this equation was reported to be 0.76. The author's findings are that the

total delay caused by the right-turn vehicles is related to the volume of the right-turn

maneuver, the volume and the speed of the through vehicles.

McShane (1995) used the TRAF/NETSIM (1995) simulation model to quantify the

effects of right-turn maneuvers on through vehicle travel speed. The author took into









consideration the driveway flow rate, driveway spacing, major-street flow rate, driveway

location, number of driveways, number of lanes on the major street, and free-flow speed.

The computed delays are comparable to those reported by Stover et al. (1970) and

Alexander (1970), although, an exact comparison is not possible due to different ranges

of major-street flow rate and speed.

Bonneson (1998) developed a deterministic model for predicting the delays to

major-street through drivers due to a right-turn maneuver from the outside through lane

of the major street. The author did not consider the number of through lanes on the major

street or the distribution of its flow rate to these lanes. The proposed model requires as

input the flow rate in the outside through lane and it is formed for both single and

multilane approaches where right-turns are assumed to occur from the outside through

traffic lane.

The author modeled the delay of the through vehicles that starts with the right-turn

maneuver of one vehicle and ends with another right-turn maneuver. In this model it is

assumed that lane changing by through drivers to avoid a slowing right-turn vehicle is

negligible during the event, due to the fact that the event has relatively short duration and

the delays are basically because of the acceleration/deceleration process (only a few

seconds).

The model describes first the delay incurred by the first through vehicle and then

the delay of the following vehicles by representing the trajectories of the turning vehicle

and the through vehicles, under the assumption of low flow conditions (1000 vph/ln),

constant running speeds and constant acceleration/deceleration rates. Bonneson's desired









turn speed was related to the curb return radius and the driveway width, based on

research by Richards (reported by Stover and Koepke, 1988). The formula used is

rt = 3.59 + 0.196 R (2- 2)

where urt is the right-turn speed and Rc is the turn radius.

The author also determined the minimum speed and the delay of the first delayed

through vehicle. Based on shock wave theory, the author developed a procedure for

calculating the delay of the following vehicles.

The verification of the model entailed comparison of the proposed model with the

findings of other researchers (Alexander, 1970; Stover et al., 1970; McShane, 1995)

which yielded overall agreement, as well as a comparison of it to the TRAF/NETSIM

model, but no validation with field data was conducted. The author's findings indicate

that the through vehicles' delay increases with increasing flow rate, increasing major-

street running speed, with an increase in right-turn vehicles proportion or a decrease on

right-turn speed. It is also shown that the delay per right-turn vehicle decreases as the

proportion of the right-turn vehicles increases, due to the fact that the proportion of

through vehicles that are following is smaller.

In NCHRP Report 420, Gluck et al. (1999) analyzed types of access management

techniques and their impacts. On the assessment of unsignalized access spacing, the

authors performed an operational analysis for identifying how right turns entering a

driveway affect other drivers following in the same travel lane. These findings are also

shown in a paper review of Gluck et al. (2000). In their study, information was gathered

on the number and percentage of through vehicles impacted by right turns. The impact

lengths of through vehicles impacted were determined, and, influence areas were









computed. Their results were used to quantify the effects of multiple driveways and to

develop inputs for establishing unsignalized access spacing guidelines. The field

measurements include traffic volumes and impact characteristics such as the number of

incidents caused by the activation of the brake lights and evasive maneuvers of through

vehicles following a right-turning vehicle. Moreover, the authors gathered information

and computed the following input parameters:

* The number and percentage of through vehicles in the right lane that were impacted
by right-turn-in at a single driveway.

* The percentage of through vehicles in the right lane that were impacted by right
turn- in over a series of driveways.

* The distances back from a single driveway entrance that cars began to be
impacted-the impact length-and the spatial distributions of impacted vehicles.

* The "influence areas" or influence distances before (upstream of) a driveway
entrance. This involved adding perception-reaction distance and car length to the
impact length.

* The proportions of through vehicles in the right lane whose influence lengths
extended to or beyond at least one upstream driveway over a section of road
(spillback rate).

* The variations of spillback rate by roadway operating speed

The single driveway analysis was extended to multiple driveways' analysis through

probability analysis. The findings denote a linear relationship between the percent of

right lane through vehicles impacted and the right-turn volume, irrespective of speeds.

The driveway impact lengths (and influence lengths) analysis revealed the

relationship between the percentage of through vehicles in the right lane that would be

impacted by right-turn traffic for various distances form a driveway for each range of

right-turn volume.









The analysis also revealed that the influence distance increases as speed increases.

A relationship between speed, distance from upstream traffic signal and impact length is

also established.

Another study presented by Wolfe and Piro (2003) describes a methodology for

determining the delay to through vehicles due to the right turning traffic. The study

involves both signalized and unsignalized intersections, but the authors eliminated from

their study the vehicles that were not under the operating speed due to being in a queue or

decelerating at the amber traffic light.

The methodology is based on total volume, right lane volume, right turning volume

and the difference in the through vehicle operating speed and the right turning speed due

to geometric constraints. The methodologies that were developed calculated three

different forms of delay, such as the total delay of all through vehicles, the delay to traffic

in the right lane and the delay to all through vehicles that follow a right turning vehicle.

The following delay equation was derived based on the total volume, the right lane flow,

the right-turning volume, and the algebraic difference between the right-turning vehicle

speed and the through operating speed.

Dtot = -0.352,tol + 0.729VRLne + 4.99VR, -5.97u R (2 3)

The right-turning speed was calculated by using an equation of a previous study

(Wolfe and Lane, 1999) that correlates the turning speed with the turning radius of the

curb. The authors collected data at 15 intersections, either signalized or unsignalized,

with various right curb radii, ranging from 1.6 m to 20 m. The authors determined the

speed of the turning vehicles based on the time they required for the maneuver and the

respective geometry of the intersection. Based on their data, the following formula was

derived.









S= 2.2678809 + 0.451631R + 0.078901R2 0.007308R3 + 0.0001811R4 (2 4)

Based on that speed, the authors determined the time difference between the right-

turning vehicles that enter the arterial and the through vehicles, which holds for the delay

that the through vehicles experience.

Left Turns from Arterial

Bonneson and Fitts (1999) discussed the delay on the major street due to vehicles

that perform a left-turn maneuver at two-way stop-controlled intersections. This delay is

incurred when major street left-turn demand exceeds the available storage area and

blocks the adjacent through lane (undivided cross section with no left-turn bay). In this

situation, the through drivers will merge with vehicles in the adjacent through lane if

there is an adequate gap for them to safely merge into, or they will remain in the inside

lane until the queue dissipates (if there is no merge opportunity). This paper is part of the

NCHRP Project Report 395 (Bonneson and McCoy, 1997) that evaluates the adequacy of

midblock left-turn treatments such as TWLTL, raised-curb median and undivided cross

section, based on the operational, safety and access effects of these treatments.

In this paper the authors combined four models for determining the average delay

to through vehicles in a blocked inside lane. The four models include: a lane flow rate

model, a merge capacity model, a merge delay model, and an overflow probability

model. The authors also discuss the work of Kyte et al., (1996) who proposed a model to

estimate the delays to through vehicles by assuming equal distribution of through traffic

to lanes and the probability of having a left-turn queue.

The lane flow rate model was developed to predict the through vehicle flow rate in

each approach lane just upstream of the left-turn location, when there is at least one left-









turn vehicle present (for two or more lane approaches). The distribution of through

drivers on the lanes follow the assumption that they will choose the traffic lane that

minimizes their travel time (and thus their delay), and this is accomplished by equating

the demand-to-saturation flow ratios among the alternative through lanes. Additionally,

the authors developed capacity models for the inside lane through vehicles, for either

non-merge or merge situations. The non-merge situations occur when a driver decides to

remain to the inside lane until the queue ahead of him dissipates and does not perform a

lane change maneuver. The merge situations take place when the driver merges into the

adjacent through lane, instead of waiting in the back of the queue. Lastly, the probability

of a left-turn bay overflow is calculated, which represents the probability of one or more

left-turn vehicles being queued in the inside through lane in an undivided cross section.

The combination of the models yields that the delay of the through vehicles

increases with left-turn percentage for low to moderate flow rates; however, for high flow

rates this is not the case. The authors' belief is that there may be a left-turn percentage

associated with the maximum delay for high flow rates such that left-turn percentages

higher or lower would yield lower delay. This methodology was verified using the

TWLTL-SIM simulation model and it was found that the two models generally agree,

although the proposed model predicts lower delays than the TWLTL-SIM in the 'low

delay' range.

Access Management and Driveway Spacing

According to the Access Management Manual, published by the TRB (2003)

access management is "the systematic control of the location, spacing, design, and

operation of driveways, median openings, interchanges, and street connections to

roadway". In other words, access management is a tool for providing vehicle access to









the abutting land development, in a way that the traffic safety and transportation

efficiency are met in balance.

There are several studies that developed guidelines for selecting the desirable

spacing between unsignalized access points (Stover and Koepke 2002, AASHTO 2001,

Gluck et al. 1999, TRB 2003). These guidelines were based on different criteria, such as

safety, stopping sight distance, intersection sight distance, functional area, right-turn

conflict overlap, influence distance and egress capacity (TRB, 2003).

Based on a study by S&K Transportation Consultants Inc. (2000), as appears in

Access Management Manual (TRB, 2003), the relative crash rates are expected to

increase if the driveways' spacing is reduced. For example, a decrease of access spacing

from 1056 ft to 264 ft would yield crash rates 2.1 times higher. Another study (Stover and

Koepke, 2002) suggests that long spacing between driveways is more desirable, since

auxiliary lanes can be designed to reduce the conflicts between the arterial through

vehicles and the turning vehicles and provide safety.

The AASHTO Green Book (2001) provides suggestions for stopping sight distance

and intersection sight distance, which can be applied as access spacing criteria. Stopping

sight distance is the minimum sight distance required to allow drivers to come to a stop.

This criterion is very useful for access spacing guidelines, as there are many potential

conflicts between the arterial through and turning drivers. AASHTO (2001) provides

tables that give the minimum stopping sight distance, depending on the arterial grade.

Additionally, intersection sight distance is the minimum sight distance required for a

vehicle stopped at an intersection to enter or cross the major approach. AASHTO (2001)









also provides minimum intersection sight distances as a function of the major street

speed, number of lanes and grade.

The functional area criterion (AASHTO, 2001) demonstrates that all access

connections such as intersections (signalized or unsignalized), driveways and arterials,

define a functional area upstream of their location. The criterion dictates that no other

connection should be placed within this functional area.

The right-turn conflict overlap occurs when the major street through driver has to

monitor more than one driveway at a given time. The speed difference between the

turning vehicle and the through vehicle define the minimum distance required to reduce

collision due to overlapping right-turn maneuvers (Stover and Koepke, 2002).

The Significance of Mid-Block Effects

Lin et al., (2003) developed a model which includes the effect of the vehicles

entering an arterial from a cross street. According to the authors, the total delay on an

arterial includes Link Delay and Intersection Delay. Link Delay can be caused by two

factors. The first factor is the intersection delay, in the sense that a vehicle will slow

down while approaching the queue at the intersection, hoping that the queue will start to

dissipate as soon as he will reach the intersection. In essence, this represents an early

deceleration, which does not affect delay. The second factor is the increase in flow,

which can be questionable, because it has been shown that travel time is not sensitive to

link flow, for medium or high flows, due to the metering of the upstream intersections.

For this reason, the authors assume that the delay of the link that is due to the internal

link flow can be considered zero. Under this assumption, the authors approximate the

Link Delay with the estimation of only the delay at intersections.









The model is based on discrete Markov chain properties. The key parameters of the

model are the ratio of the overall flow level to the service capacity for the intersection in

question, the net turning movement percentages into the arterial from the cross street at

the upstream intersection, and the traffic signal coordination level with its upstream

intersection. As it is seen, the authors acknowledge that the vehicles that turn into the

arterial can produce delays to the through vehicles. The model that is developed, exhibits

some desirable properties in predicting the arrival time at downstream arterial links, but it

has some limitations and it has not yet been validated with field data.

Olszewski (2000) compared the HCM 2000 methodology for estimating

intersection delay and another speed-flow model that requires intersection spacing and

minimum signal delay as input parameters. This model was developed primarily for

planning applications. The author compared the travel speeds that are predicted by both

models, for a range of parameters such as intersection spacing, traffic flow and signal

characteristics. The results yielded that the pattern of travel speeds is similar to both

models; eventhough the HCM 2000 model predicts lower speeds.

Summary of the Literature Review

Several research efforts have been reviewed to establish the current state-of-the-art

that relates the arterial travel time and delay estimation with mid-block effects. Most of

the literature is devoted to access control management and evaluation of alternative

median treatments. Other studies try to quantify the effects of either right turns or left

turns from the arterials to the travel time of the through vehicles, by developing analytical

models. Moreover, studies were found to acknowledge the significance of the mid-block

effects but without proposing any delay model that incorporates these effects. Thus, it can






18


be concluded that a comprehensive model that incorporates all the parameters that can

cause arterial link delay to the through vehicles is lacking from the current literature.














CHAPTER 3
METHODOLOGY

This chapter presents the methodology followed to develop the analytical models of

mid-block delay of arterial streets. The following tasks were undertaken:

1. Collect volume and travel time data for two-lane, two-way and two-lane, one-way
arterials.

2. Simulate the arterials in CORSIM and calibrate the models.

3. Conduct a factorial design experiment.

4. Expand the database and perform multiple simulation runs.

5. Extract the mid-block delay data and develop mid-block delay equations as a
function of the independent variables of interest using regression analysis.

A more detailed description of the process followed by this research is presented in

the remaining of this chapter.

Data Collection

Data were collected at two two-lane, one-way arterials and one two-lane, two-way

arterial. The data involve arterial through and turning volumes, driveway turning

volumes, heavy vehicles, parking frequency, and bus dwell time, as well as travel time

measurements. These data were used for the next step of the research (simulation model

development and calibration). A more detailed description of the data collection process

and the study sites is presented in Chapter 4 of this thesis.

Simulation Model Development and Calibration

One of the main purposes of this study is to use real-life traffic conditions for the

development of the regression models. However, this would require a large amount of









field measurements, which is outside of the scope of this research. Collecting a certain

amount of field data and using a simulation tool for expanding the dataset would reach

the research objectives.

The study arterials were simulated in CORSIM and calibrated with the travel time

measurements. The criterion used for deciding whether the models need to be calibrated

or not is that the simulation travel time should be within a 10% range of the actual field

measured travel time.

The calibration of the models includes adjustments of the simulation parameters

such that the resulting travel time would approach the field measured travel time and the

real-life conditions would be replicated effectively. The calibration parameters

considered are the discharge headway, the mean start-up delay, the driver's reaction time,

the driver familiarity, the probability of spillback, the duration of lane change maneuver,

the parking maneuver duration, and the free-flow speed. Details regarding the simulation

model development and calibration are given in Chapter 5.

Design of Experiments

Once the simulation models are created and calibrated, they were used as a basis

for generating more data and expanding the database in CORSIM. The database

expansion process requires the development of a factorial design in which several

simulation model inputs are identified as varying factors with different levels, to attain

variability in traffic and geometric conditions. The varying combinations between the

levels of the simulation input parameters form different scenarios.

The simulation input parameters were selected based on the arterial type, depending

on whether it is a two-lane, two-way or a two-lane, one-way arterial. The simulation

input parameters for the two-lane, two-way arterial are the arterial through volume (two









levels), the percentages of turning traffic from the arterial (six levels), the driveway

volume (two levels), the percentages of turning traffic from the driveway (three levels),

the number of driveways per directions (four levels), the arterial free-flow speed (two

levels), and the parking activity frequency (two levels). A more detailed description of

the varying factors and levels are given in Table 6-1 of the corresponding chapter

(Chapter 6). It is important to note that since there were two hours of data available for

the two-lane, two-way simulation model (Park Avenue midday and p.m. peak-hour) it

was decided to use the p.m. peak-hour model for the high-level arterial through volume

and the midday model for the low-level arterial through volume.

For the two-lane, one-way models there were two arterial links, thus the simulation

model inputs vary depending on the base model. As such, the first group of scenarios is

formed for the Beaver Avenue at Sparks Street, where the simulation input variables are

the arterial through volume (three levels), the percentage of turning traffic from the

arterial (six levels), the driveway total volume (two levels the values depend on the

arterial through volume level), the number of driveways by link (four levels), the arterial

free-flow speed (two levels) and the parking activity and duration (two levels). The

second group of scenarios corresponds to the base model of Beaver Avenue at Pugh

Street and the simulation model inputs are the same as those of the first group plus one

additional model input, which is the bus dwell time (two levels). Tables 6-2 and 6-3 of

the corresponding chapter illustrate the selected values for each level.

Database Expansion

The next step of the methodology is to perform multiple simulation runs for each

different scenario developed. All scenarios created through the factorial design would run

for seven times to account for the variability in the simulator.









For each new scenario, the appropriate data for the analysis were extracted. First,

the mid-block delay was calculated based on the following equation:


MBD = TT CD -LinkLength (3 1)
FFS

Where:

MBD = Mid-block delay of each link

TT = Arterial link travel time (provided by the simulation output)

CD = Intersection control delay at the arterial downstream signal (provided by the

simulation output)

Link Lengh = The link length of the study arterial link

FFS = Arterial free-flow speed (according to the simulation input)

Additionally, other information such as the arterial through and turning volume,

and the driveway turning volume were extracted from the simulation output.

Data Analysis and Formulation of Regression Models

The final step of the methodology includes the data analysis and the development

of the analytical models. To achieve this, the extracted data and the calculated mid-block

delay were grouped depending on the arterial configuration under study. Thus, two

datasets were created for the case of the two-lane two-way arterials and the two-lane one-

way arterials.

Each dataset was further divided according to the traffic operations of the arterial to

congested and uncongested. The notion behind this decision is that the mid-block delay

under uncongested conditions is affected significantly by the driveway turning volume

and the arterial turning volume which contributes to frequent vehicle frictions; however,

under congestion, the mid-block delay is primarily a result of the overall congestion.









For each of the datasets, one regression equation is formulated, which estimates the

calculated mid-block delay of Equation 3 1 with respect to the selected independent

variables. There were several variables initially considered to be incorporated into the

regression models (candidate variables), which are described briefly below. A more

detailed description of the candidate variables is included in the relevant chapter of the

data analysis (Chapter 6).

Vp: Arterial volume (vph/ln):

This variable represents the volume that feeds the arterial segment and is measured

at the beginning of the arterial link.

ZVdr/N: Average Driveway volume (vph/ln):

This variable represents the amount of traffic that enters the arterial through the

driveways, divided by the number of driveways per link.

ZVar/N. Average Arterial Turning Volume (vph/ln):

This variable describes the average traffic that exits the arterial through all

driveways, either by a left-turn or right-turn maneuver.

ZVopp/N: Average Arterial Volume that is Opposed to Arterial Left Turns (vph/ln)

(for two-lane two-way models):

This variable is expressed as the sum of the arterial volume that opposes to left

turns from the other direction divided by the number of driveways involved.

Xc: Arterial Degree of Saturation at Downstream Intersection:

This variable is the arterial demand to capacity ratio measured just upstream of the

signal.


FFS: Arterial Free Flow Speed (mph)









Ndr: Number of driveways per 1000 ft

DT. Bus Dwell Time (s)

P: Parking Activity (vph/ft):

This factor describes the parking frequency of the arterial segment for every

available parking space of 20 ft.

The final regression models include these variables as they are described above.

However, some of them are altered in order to describe more effectively the mid-block

delay.

Last, the mid-block delay equations are used for the travel time estimation of two-

lane two-way or one-way arterials that operate under congested or uncongested

conditions according to the following equation:


TT = MBD + CD + LinkLength (3 2)
FFS


Where all measures are as defined earlier.














CHAPTER 4
DATA COLLECTION

Data collection is an important step of this research and it is used to generate

variable arterial traffic conditions in the simulation environment. Thus, sufficient data for

the development of the analytical travel time models can be generated.

Data Requirements

Two types of data are required in this study. The first type includes the input data

used for the development of the simulation models:

6. Arterial through volumes.

7. Arterial right and left-turning volumes in the driveways.

8. Driveways turning movements' volumes (right turns, left turns and through
movements).

9. % of heavy vehicles on the arterial and on the driveways.

10. Parking and departing maneuvers volumes on the arterials.

11. % time that the segment is occupied by buses when stopped (dwell time).

12. Number of passengers in and out of the buses.

13. Study site geometry (number of driveways per link, driveway spacing, driveway
turning radii, number of lanes per link, total link length)

14. Number of bus stops per link and parking bay lengths.

15. Phasing and timing plans for the signalized intersections.

The second type of data, namely travel time, were collected concurrently with the

input data. The travel time information of the arterials is used for verifying that the









developed simulation models replicate efficiently the real-life traffic conditions (model

calibration.

Description of Study Sites

The data collection plan considers two arterials which are located in urban and

residential environment. The first one is a two-lane two-way arterial (Park Avenue) and

the second one is a two-lane one-way arterial (Beaver Avenue). The two arterials are

shown in the following figures. Both streets are located in State College, PA.

Park Avenue does not provide a TWLT lane or left turn pockets, thus each lane per

direction serves as a shared lane for through/right-turn and left-turn movements. For Park

Avenue two successive links were analyzed. Each link contains two, two-way driveways,

which form T-intersections with the arterial. Two separate links are also studied in

Beaver Avenue, both of which include a combination of T-intersections and TWSC

intersections, with a total of six and eight intersections for each arterial link respectively.

Descriptive sketches of the two arterial configurations are presented in Figures 4-3

through 4-5.

Y JWidw;L~-~tlwa


Figure 4-1 Two-lane two-way arterial (Park Avenue)
























Figure 4-2 Two-lane one-way arterial (Beaver Avenue)

Data Collection Methods

The field data required for this research were collected during peak and non-peak

hours to cover a wide range of flow conditions.

1. Loop detectors and cameras were used for the arterial through vehicle data
collection. The loop detectors were located at the approaches of the signalized
intersections and also at mid-block locations. The cameras were used for those
approaches that traffic volumes were not available from loop detectors.

2. Manual recording and cameras were used for the collection of driveway turning and
through volumes, the presence of heavy vehicles and the parking activity.

3. Manual recording of the number of passengers that use the buses at the study area
and of the respective time the bus decelerates, remains stopped and accelerates at
the bus stations.

4. The travel time study was conducted with the floating car technique. The travel
time measurements occurred concurrently with the volume measurements.

The required number of vehicle runs was calculated based on the standard deviation

(S) of the field measured travel time and the margin of error (e) according to the

following equation.

(1.96 S)2(4
n (4 1)
e










The calculation was performed for all three study arterials and the summary of the

results are given in the following table, where the margin of error was selected at 30 sec.

Table 4-1 Determination of number of vehicle runs based on field measured travel time.
PARK
B SPARKS PUGH
EB WB
MID P.M. MID P.M. A.M. MID P.M. A.M. MID P.M.
Travel Time (s) 78.6 88.6 112.3 366.0 71.6 75.4 98.9 37.1 35.3 35.4
St.Dev. (s) 14.6 19.1 27.3 55.8 25.2 43.1 18.8 8.9 4.2 5.6
Number of Vehicle
Numb7 5 7 4 8 7 7 8 7 7
Runs Performed
St.Dev (s) 16.52 39.18 30.51 6.66
Number of Vehicle
1 7 4 1
Runs Required

Since the maximum number of required vehicle runs is less or equal to the number

of performed vehicle runs, it is concluded that the field measured travel time data are

sufficient.













Beaver Avenue First Link


Sparks Street


driveway 1


driveway 2


driveway 3


driveway 4 driveway 5


driveway 6 driveway 7 driveway 8
Figure 4-3 First two-lane one-way arterial link (Beaver Avenue between Sparks St and Atherton St)

Beaver Avenue Second Link

Pugh Street
Pugh Street driveway 1 driveway 2 driveway 3 driveway 4







\/ ~ / ~ '~~\ /



driveway 5 driveway 6
Figure 4-4 Second two-lane one-way arterial link (Beaver Avenue between Pugh St and Garner St)


Atherton Street


Garner Street
















Park Avenue Successive Links


N. Atherton Street


driveway 2


N. Allen Rd


driveway 3 driveway 4


parking bay


driveway 1


Figure 4-5 Two successive two-lane two-way arterial links (Park Avenue between N. Atherton St and N. Allen Rd and Park Avenue
between N. Allen Rd and Shortlidge Rd)


Shortlidge Rd













CHAPTER 5
SIMULATION MODEL DEVELOPMENT

An important step of this research is to use simulation to replicate the real time

traffic conditions during the data collection study periods, and subsequently use them as

the basis for the database expansion. The remaining of this chapter describes the

simulation software used, the modeling of the study sites, and their calibration with real

field data.

Simulation Package

For the purposes of this research effort CORSIM (FHWA, 2003) was selected as

the appropriate simulation tool for the model development as it can provide control delay

in the output. The gap acceptance algorithm of CORSIM and its ability to replicate

turning maneuvers are reviewed in the following paragraph.

The gap acceptance model in CORSIM is based on default values of the

distribution of the acceptable gaps, depending on the driver type category. The program

uses 10 behavioral categories and the gap for near-side cross-street traffic ranges from 56

to 20 tenths of a second, based on a decile distribution. These default values can be

altered by the user. The same logic is used for left-turn or right-turn gap acceptance,

where the gaps are selected based on the driver characteristics code. If a far side cross

street exists, additional gap time is required as input, which depends on the number of

lanes. Similarly, the additional time is given by a decile distribution.

CORSIM does not have the capability of explicitly defining the speed of a turning

maneuver; however, the user can implicitly affect the turning speed of the vehicles on the









arterial by defining the free-flow speed of the driveway. Alternatively, it is possible to

define the maximum allowable turning speeds in CORSIM, but this option applies to the

whole network and not to each individual intersection.

The intersection control delay estimation in CORSIM includes the initial

deceleration of the vehicles, the stopped delay, and the delay due to acceleration back to

the full operating speed. The simulator considers the time difference between the actual

travel time of the vehicles versus the travel time had there been no signal (approximated

by the ratio of the link length and the operating speed). When the arterial volume is low

the operating speed is approximated by the free-flow speed. However, when the flow is

significant the operating speed of the vehicle cannot be approximated by the free flow

speed and a smaller speed is considered.

Although there are other simulation tools available, CORSIM is a broadly used

traffic engineering tool and it was selected for this research mostly because it can provide

control delay information. Nevertheless, as every software, CORSIM has its own

limitations, which will be discussed in a later section.

Model Development

The simulation of the study sites starts with the input of all the available and

appropriate data that were collected in the field. According to the data collection effort

there were a total of eight hours of available volume and travel time data; two hours for

both two-lane two-way arterial links (midday and pm peak hour) and three hours for both

two-lane one-way arterial links (am peak hour, midday and pm peak hour). Since the two

two-way arterial links are adjacent they were modeled into CORSIM together and

therefore, each hour was modeled as a separate CORSIM file with the particular volumes.









The data used for the simulation model development include geometric data, signal

phasing and timing data and volume data. The geometric data for each arterial segment

consist of the arterial link length, the location of the driveways, the number of lanes on

the arterial and on the driveways, the turning speed into the driveway, the parking space

location and the bus turnouts.

However, not all geometric data were able to be modeled into CORSIM explicitly

due to program limitations. The turning speed of the arterial vehicles on the driveways

was modeled by considering the curb radius of the driveways and the driveway throat

width. Based on a study performed by Richards (1980) and reported by Stover and

Koepke (1988) the speed of a vehicle that enters a driveway is significantly low for all

combinations of curb radii and throat widths. The author developed a nomograph that

provides the driveway turning speed based on these two measures. Therefore, based on

the measure of the curb return radii and the throat width, the turning speed on the

driveways was calculated for all cases. However, this type of speed cannot be input

directly in CORSIM, as the program allows only the input of a link's free-flow speed. To

address this issue, the driveway links were modeled with free-flow speed equal to the

corresponding "driveway entry speed" for a length of 100 ft near the intersection.

It should be noted that there is an option in CORSIM where the user can specify the

maximum allowable turning speed, either left- or right-turn, but this option is network-

wide. Therefore, it is not possible to have different maximum turning speeds on the same

arterial street.

Additionally, since there is bus activity on one of the arterials, data such as bus

frequency and mean dwell time were collected and modeled into the simulator. Although









in the study site the buses do not stop at a turnout but at the shoulder lane, this

representation could not be done realistically in CORSIM, as the simulator provides only

bus turnouts for modeling the bus stops. However, it can be assumed that the effect on

traffic operations between a bus stopping at a turnout in CORSIM and a bus stopping at

the shoulder lane in the study site is similar.

The signal phasing and timing data of the intersections involved were also modeled

in CORSIM. All signalized intersections of the study sites operate under semi-actuated

control. All pertinent data of the semi-actuated control such as minimum and maximum

green intervals, vehicle passage times, detector locations and size, and offsets were

available and modeled appropriately. Moreover, different phasing and timing schemes are

available throughout the day for the same intersections, therefore, the modeling of the

signal control changed accordingly in all models.

At this point it is important to mention that although there were pedestrian phases

on the control, ped-related data were not available. Also, the study sites do not have many

pedestrians, but there are a few as the arterials are located in the periphery of the campus.

As such, it was assumed that the pedestrian intensity was 20 pedestrians per hour.

Additionally, in one case the signal plan considered an exclusive pedestrian phase, but

CORSIM cannot model this directly. For this reason, a "dummy" phase replaced the

pedestrian phase where the "dummy" vehicle volume would equal the pedestrian

intensity and their route would be to a direction that does not interfere with the arterial

traffic.

Volume data along with proportions of turning movements at the intersections and

the driveways and heavy vehicle percentages were also input in the models. Lastly, field









measurements of the free-flow speed did not occur in the study corridors thus, it was

approximated by the arterial speed limit as in these locations police enforcement of the

speed limit is very regular.

At this point it should be noted that modeling arterial links into CORSIM requires a

large amount of information, only a portion of which were actually collected in the field

for this study. For other traffic characteristics such as driver behavior, saturation

headway, and start-up lost time the default values of the software were initially used, and

these were altered as appropriate during the model calibration process.

Model Calibration

The model calibration process includes first, the comparison of the simulated

models with field measurements of travel time, and second, the adjustment of the

simulated models that do not match well with the real-life conditions.

Once the simulation models were created, the number of the simulation runs

needed to be specified to account for the variability in the simulator. As such, several

simulation runs were performed and the travel time standard deviation for each arterial

model was calculated. By using the following equation and considering a margin of error

(e), 15 sec, the number of required simulation runs was calculated as seven.

n (1.96*S) (5 1)
e

The resulting number of simulation runs was very small; thus to simplify the

process, the maximum number of required vehicle runs was considered as the number of

simulation runs to be performed in every model. Therefore, each of the eight models

would run for seven times.









The next step of the process is to verify that the simulation models would replicate

traffic operations on the arterials and this is accomplished by comparing the average

travel times derived from the simulation with the field measured travel times. If the travel

time calculated from the simulation would range between 10% of the travel time

collected in the field then the respective models would not need any further adjustment.

The models that yielded acceptable travel time prediction are the Sparks a.m. and mid

model and the Pugh a.m. model.

However, if the simulation models yielded travel time outside of the 10%

acceptable range they would then be calibrated by making reasonable adjustments in

those characteristics that were not collected in the field.

Typically, the adjustments that are made in the simulation environment include

changes to the saturation (discharge) headway, the mean start-up delay, the driver

reaction time etc.

The models that their predicted travel time was beyond the acceptable range where

the Park mid and p.m., the Sparks p.m. and the Pugh mid and p.m. models. The

calibration parameters for those models that were adjusted include the mean start-up

delay, and discharge headway, the time reaction to deceleration, the duration of lane

change maneuver, the parking maneuver duration, the driver familiarity, the free-flow

speed and the spillback probability of discharging with respect to the discharge position.

The default and calibration values of these parameters are given in tables 5-1 to 5-3.










Table 5-1 Calibration parameters for Park Avenue midday and p.m. models
CORSIM Vl
Calibration Parameter C M Calibration Value
Default Value
1 (EB), 2.5 (WB)
Mean start-up delay (s) 2 1
1.5 driveways
1.4 (EB), 2 (WB)
Mean discharge headway (s) 1.8 1.
1.5 driveways
Probability (%) of a vehicle joining 1 2 3 4 1 2 3 4
spillback with respect to the number of
vehicles in the spillback 80 40 0 0 0 5 0 0
Driver familiarity (% drivers that 50
know 1 turn movement in advance)
Time reaction to deceleration (s) 1 0.8
Duration of lane change maneuver (s) 3 2
Parking maneuver duration (s) 4 3.5

Table 5-2 Calibration parameters for Sparks p.m. model
CORSIM
Calibration Parameter Defat V e Calibration Value
Default Value
3-4 arterial
Mean start-up delay (s) 2
3 driveways
Mean discharge headway (s) 1.8 3
Driver familiarity (% drivers that 80
know 1 turn movement in advance)
Time reaction to deceleration (s) 1 3
Duration of lane change maneuver (s) 3 2
% drivers who cooperate with lane 50 20
changes
Parking maneuver duration (s) 4 3.5

Table 5-3 Calibration parameters for Pugh midday and p.m. models
CORSIM
Calibration Parameter M Calibration Value
Default Value
Mean start-up delay (s) 2 1.5
Mean discharge headway (s) 1.8 1.5
Time reaction to deceleration (s) 1 0.7
Free flow speed (mph) _30
Parking maneuver duration (s) 4 3.5

The probability of a vehicle joining a "spillback" queue with respect to the number

of vehicles in the spillback (Table 5-1) is an important variable which was used for the

models that had congestion and yielded high travel times. It was observed that due to the

congestion, the arterial through vehicles would not leave gaps, even if the queues were

extended beyond the unsignalized intersection. This would lead to an unrealistic









representation of field conditions and to increased travel times as the vehicles blocking

the intersections would impede the left turns from the opposing direction. In reality,

queued vehicles usually leave the unsignalized intersection clear for the driveway

vehicles to enter or cross or for the left-turn vehicles on the opposing direction. However,

it is possible to define in CORSIM the probability of a vehicle joining a spillback and in

this case this probability was reduced (see Table 5-1) to account for vehicles waiting until

the spillback ahead dissipates. The particular combination of probabilities of joining

spillback (0% if there is 1 vehicle in the spillback and 5% if there are 2 vehicles in the

spillback) resulted in lower travel times for the simulation models, as the vehicles from

the opposing directions would leave gaps at the intersections for the left-turning vehicles

to cross.

By implementing these calibration parameters the travel times from simulation

were in agreement with the field measured travel times. The following table presents the

simulated and field measured travel times for all models.

Table 5-4 Field measured vs. simulation travel time after calibration
Modl Field Measurements Simulation
Model .
Travel Time Acceptable Range Travel Time
Park EB 78.57 [70.71,86.43] 78.16
mid WB 112.29 [101.06,123.52] 104.13
Park EB 88.60 [79.74,97.46] 96.73
pm WB 355.40 [319.86,390.94] 345.76
Sparks a.m. 71.63 [64.47,78.79] 74.76
Sparks mid 75.43 [67.89,82.97] 74.49
Sparks p.m. 98.86 [88.97,108.75] 97.43
Pugh a.m. 37.13 [33.42,40.84] 39.83
Pugh mid 35.29 [31.76,38.82] 36.01
Pugh p.m. 35.43 [31.89,38.97] 33.82









Summary and Conclusions

The simulation model development process includes two important steps. The first

step was to recreate the geometry of the arterials and the traffic conditions into the

simulation environment during the data collection period. The second step was to

calibrate the models in order to ascertain that they can replicate satisfactorily traffic

operations on the arterials.

During the simulation model development several weaknesses of CORSIM were

revealed. Although CORSIM is a widely used tool there are some issues that the program

does not address directly. For this reason this section also provides some

recommendations for improving/extending the capabilities of the software.

CORSIM does not provide the option for defining the arterial turning speed for

individual intersections. Although the program allows for network-wide input of the

maximum right- or left-turning speed, it may be more useful that this option is applied to

individual intersections based on the research purposes. In this research the issue of

modeling right- or left-turn maneuvers' speed was addressed by "forcing" the vehicles to

enter the driveway with low free-flow speed, which corresponded to the site

specifications of throat width and curb radius.

Additionally, CORSIM does not allow for modeling exclusive pedestrian phases. In

the simulation modeling process this issue was addressed by creating a "dummy" phase

for an approach that does not interfere with the arterial network and traffic flow that

equals to the pedestrian intensity. Nevertheless, it may not always be possible to create a

"dummy" phase for a traffic movement that does not affect the network; thus, this

capability of modeling exclusive pedestrian phases should be provided by the software.






40


Another important limitation that was observed is the gap acceptance algorithm that

the software applies. As already mentioned, CORSIM builds the gap acceptance

algorithm upon the driver characteristics code, but in reality this is not the only basis for

the gap acceptance. It is recommended that the software accounts for the fact that drivers

become impatient while waiting a long time for a gap.














CHAPTER 6
ANALYTICAL MODEL DEVELOPMENT

The effects of mid-block phenomena are explored with the help of the CORSIM

simulation package. The analysis includes two basic steps; the expansion of the available

field data set through simulation and the development of the analytical models through

regression analysis. For the expansion of the database, alternative scenarios are built in

the simulation environment, according to the prespecified design of experiments. These

scenarios are formed for both cases of two-lane, two-way and two-lane, one-way urban

arterial streets, thus two subsets of data are generated through CORSIM. These data are

eventually used for the mid-block delay model development, with the help of MINITAB

statistical analysis package.

Database Expansion

Due to the limited amount of field data available, it was decided to use simulation

in order to generate enough data to be used for the analytical model development. After

building the models into CORSIM and calibrating them with the field measured travel

time data, they were used as a basis for generating more data and expanding the database.

For each calibrated model, several simulation model inputs were selected, to be used as

varying factors of the factorial design of the experiment.

In an effort to attain large variability in traffic conditions and arterial geometry,

each factor was designed with different levels. For example, arterial traffic flow ranges

between 800 vph/ln and 1000 vph/ln for the two-lane, two-way scenarios and between









400 vph/ln and 800 vph/ln for the two-lane, one-way scenarios. Each combination of the

varying levels of the factors represents a different scenario in the database.

The simulation model inputs depend on the calibrated model. Thus, all cases of

two-lane, two-way arterials derive from the model of Park Avenue (two adjacent links).

However, the two-lane one-way cases come from both calibrated models of Beaver

Avenue at Sparks Street and at Pugh Street; thus, the selected simulation model inputs are

different. Additionally, since there are different levels of volume for each scenario, it was

decided to match these with the calibrated models that had similar volume throughput

(am, mid and pm models). Tables 6 1 and 6 2 summarize the different inputs and their

levels that were used for the database expansion. Table 6 1 corresponds to the cases of

two-lane, two-way arterials and Tables 6 2 and 6 3 correspond to the cases of two-

lane, one-way arterials.

The selection of the simulation model inputs that appear in tables 6.1 through 6.3 is

based on (1) the anticipated form of the mid-block delay analytical models, and (2) the

CORSIM modeling capabilities. That is, the generated data would be used for applying

regression analysis and modeling the arterial mid-block delay as a function of several

parameters of influence. Thus, the simulation model inputs used for generating these data

should be such that affect the arterial travel time and mid-block delay.

Moreover, since the simulation model inputs are in fact CORSIM inputs, the

limitations and capabilities of the software should also be considered. As an example,

although the actual amount of traffic that exits the arterial through right-turn or left-turn

maneuvers is a more straight-forward input than the turning percentage at the










intersection, this is not feasible to model in CORSIM, since only the turning percentages

can be modeled in the simulator.

Table 6-1 Two-lane two-way simulation model inputs for database expansion
Two-Lane Two-Way Arterials
Simulation Model Inputs # of
Level Value
Levels
1 Arterial Through Volume 2 800 1000
VA (vph/ln)
% of Arterial Traffic Performing
% of Arterial Traffic Performing 20/5 30/5 20/15 20/25 30/15 30/25
2 Right/Left Maneuver VA-R /VA-L (%) 6
Driveway Total Volume VDR (vph) 200 300
% of Driveway Traffic Performing
3 Right/Left Maneuver at T- 3 30/70 50/50 70/30
Intersections VDR-R VDR-L (%)
4 # of Driveways by Link per Direction 4 0* 1 2 4
5 Arterial Free Flow Speed (mph) 2 30 45
6 Parking Activity Frequency (mph) 2 20 0
Number of Runs per Case 7
Number of Data Points per Link 872
Subtotal 12208
For zero number of driveways the simulation model inputs 2, and 3 do not apply.

Table 6-2 Two-lane one-way simulation model inputs for database expansion. First group
of scenarios (from Beaver Avenue at Sparks Street)
Two-Lane One-Way Arterials
Simulation Model Inputs # of
Level Value
Levels
1 Arterial Through Volume 3 600 650 800
VA (vph/ln)
Sof Arterial Traffic Performing 20/5 30/5 20/15 20/25 30/15 30/25
2 Right/Left Maneuver VA-R/ VA-L (%) 6
Driveway Total Volume VDR (vph) 180/ 200/ 220 260/ 300/ 370 *
3 # of Driveways by Link 4 0** 2 3 4
4 Arterial Free Flow Speed (mph) 2 30 45
5 Parking Activity Frequency (mph) 2 20 0
Number of Runs per Case 7
Number of Data Points 220
Subtotal 1540
The driveway total volume depends on the arterial volume and turning percentage; for
600 vph/ln arterial volume and for the first three levels of arterial turning percentages, the
driveway volume is 180 vph.
** For zero number of driveways the simulation model input 2 does not apply.










Table 6-3 Two-lane one-way simulation model inputs for database expansion. Second
group of scenarios (from Beaver Avenue at Pugh Street)
Two-Lane One-Way Arterials
Simulation Model Inputs # of
Level Value
Levels
1 Arterial Through Volume 3 400 800 1000
VA (vph/ln)
% of Arterial Traffic Performing 20 30 20 20 30 30
2 Right/Left Maneuver VA-R VA-L (%) 6 5 5 15 25 15 25
Driveway Total Volume VDR (vph) 150/ 300/ 300 200/ 400/ 470 *
3 # of Driveways by Link 4 0** 2 3 4
4 Arterial Free Flow Speed (mph) 2 30 45
5 Bus Activity Dwell Time (s) 2 30 0
6 Parking Activity Frequency (mph) 2 20 0
Number of Runs per Case 7
Number of Data Points 448
Subtotal 3136
The driveway total volume depends on the arterial volume and turning percentage; for
400 vph/ln arterial volume and for the first three levels of arterial turning percentages, the
driveway volume is 150 vph.
** For zero number of driveways the simulation model input 2 does not apply.

Each new scenario created in CORSIM was run seven times to account for the

variability in the traffic simulator. Note that the number of runs and the seed numbers of

each run is the same as the ones used during the model calibration procedure.

Selection of Simulation Output Performance Measures

After performing the required simulation runs in CORSIM, it was desired to select

appropriate simulation outputs of performance measures, which would be used for the

analytical model development in a later step. The selection of the simulation outputs was

made with the notion that several of these outputs would be used for the calculation of the

mid-block delay, while others would represent the regression model's independent

variables.

As such, the outputs that were extracted directly from CORSIM, for each

simulation run are listed in the following table.









Table 6-4 Selected performance measures extracted from CORSIM
CORSIM Performance Measures
Control delay at downstream signal (sec/veh)
Link travel time (sec/veh)
Arterial volume by link (vph)
Driveway volume (vph)

Based on the methodological framework developed, the control delay and arterial

link travel time information are used for the determination of the mid-block delay

(Equation 6 1).


MBD = TT CD LinkLength (6 1)
FFS

Where all measures are as defined earlier.

The arterial volume by link and the driveway volume are used as candidate

variables for the regression model development. The extracted driveway volume

information includes the total volume that enters and exits the arterial link (i.e., the

CORSIM output file provides the total outgoing driveway volume and the total incoming

arterial volume).

Database Organization

The expanded database is organized in an appropriate format to be used for the

analytical model development. The database is divided primarily into two sets; the two-

lane two-way dataset and the two-lane one-way dataset. This separation is done due to

the fact that not all parameters of mid-block delay are common to both arterial

configurations. Some parameters that influence mid-block delay are not the same for both

models. The final models estimate the mid-block delay that is experienced by arterial

through drivers within a single arterial link (between two traffic signals), as a function of

the selected independent variables.









For the two-lane two-way arterials, the mid-block delay of Equation (6 1) is

calculated for each approach of the arterial link (eastbound/westbound). The generated

data (arterial volumes, driveway volumes etc) are transformed into the independent

variables to be used for the regression model, and are organized with respect to the

approach that they influence the most. A more detailed description of the candidate

variables of the model is given in the data analysis section.

The total number of datapoints that were generated from the simulation runs and

used for the regression models is 4,676 for the two-lane one-way arterials and 12,208 for

the two-lane two-way arterials.

Data Analysis

The data analysis involves primarily the selection of the appropriate variables that

should describe adequately the regression model to be developed, and finally its

formation. Additionally, it is essential that the data are organized in a way that all

possible conditions are covered; such as congested vs. not congested conditions, and two-

lane one-way vs. two-lane two-way arterials.

Selection of Candidate Variables

In this step of the data analysis the variables that best explain arterial mid-block

delay are selected. A detailed description of the candidate variables to be used in the rest

of the analysis is presented in the following section.

* Vup: Arterial volume (vph/ln)

This variable represents the volume that feeds the arterial segment and is measured

at the beginning of the arterial link. It is speculated that the arterial upstream volume

affects positively the mid-block delay; as the amount of traffic entering the arterial









increases, the arterial and driveway vehicle interactions are more intense and thus the

mid-block delay increases.

Nevertheless, there is a limit in the influence of the arterial volume on mid-block

delay. For instance, if the arterial is congested, the amount of traffic that enters the

arterial segment is impeded by the downstream queued vehicles, which means that the

actual throughput could be less than that under uncongested conditions.

Note that when trying to model the arterial mid-block delay, the actual arterial

throughput is more useful information than the demand, since the latter is not always met

(congested conditions). Also, the prevailing throughput can better explain vehicle

interactions within a segment than the projected demand obtained from upstream

segments.

* XVdr/N: Average Driveway volume (vph/ln)

This represents the amount of traffic that enters the arterial through the driveways.

In the case of two-lane one-way arterial this is the sum of all driveways volume divided

by the number of driveways per link. In the case of two-lane two-way arterials this

variable is defined as the sum of traffic that enters the particular direction, divided by the

total number of driveways involved. An example of this is illustrated in the following

figure.










Driveway 1 N

- -
Vdr-L -

Vdr-R





Driveway 2


Figure 6-1 Sketch of variable lVdrN

In Figure 6 1, one could consider that the vehicles traveling eastbound are mostly

affected by the left-turning vehicles of driveway 1 and the right-turning vehicles of

driveway 2. The through vehicles would likely decelerate to maintain a safe distance

from the entering vehicles. With this assumption, the sum of Vdr-L and Vdr-R divided by

the two driveways would represent the selected variable that affects the mid-block delay

for the eastbound approach. More generally, the sum of vehicles that enter a specific

direction of the arterial from the driveways, divided by the number of driveways

involved, would likely influence the mid-block delay that the drivers of that direction

experience. The following equation illustrates the average driveway volume used for the

two-lane two-way model.


'Vdr(EB) (Vdr-L(EB)i + Vdr-R(EB)i
N N
'Vdr(WB) Z(Vdr-L(WB)i+ Vdr-R(WB)i)
N N

Where:

Vdr(EB) /N= average driveway volume that affects EB direction.

ZVdr(wB)/N = average driveway volume that affects WB direction.









Vdr-L(EB)I = driveway volume that enters the arterial EB direction through left-turn

maneuver at the ith intersection.

Vdr-R(EB) = driveway volume that enters the arterial EB direction through right-turn

maneuver at the ith intersection.

Vdr-L(WB) = driveway volume that enters the arterial WB direction through left-turn

maneuver at the ith intersection.

Vdr-R(WB) = driveway volume that enters the arterial WB direction through right-turn

maneuver at the ith intersection.

N = Total number of driveways involved within the segment.

The effect of this variable is mostly apparent in non-congested conditions, as it can

increase the mid-block delay that the arterial vehicles experience. In congested

conditions, however, this variable may not affect mid-block delay significantly, since the

arterial vehicles' speed would be most likely very low and they would not decelerate for

the oncoming traffic.

S Vart/N: Average Arterial Turning Volume (vph/ln)

This variable describes the average traffic that exits the arterial through all

driveways, either by left-turn or right-turn maneuver. The logic behind this variable is the

same as with the average driveway volume as the through vehicles would likely be

delayed by the arterial turning vehicles ahead. The average arterial turning volume is the

sum of the right turns and left turns from the arterial that are moving to a particular

direction divided by the number of driveways that are involved.

A schematic illustration of the variable description is shown in Figure 6-2.











Driveway 1 N


_Vart-R Vart-L _
Vart-L Vart-R

Vart-L


Driveway 2


Figure 6-2 Sketch of variable IVar/N

In this case, the average arterial turning volume that affects mid-block delay in the

eastbound direction should be the sum of the left-turning volume at driveway 1 and right-

turning volume at driveway 2, divided by the two driveways. Similarly, the right-turning

vehicles at driveway 1 and the left-turning vehicles at driveway 2 affect the mid-block

delay that the vehicles of the westbound approach experience.

In general, this variable can be described by the following equations:

YVart(EB) X(Vart-L(EB)z + Vart-R(EB))
N N
Vart(WB) (V art-L(WB) + Vart-R(WB)i)
N N

Where:

Vart(EB) /N = average arterial turning volume that affects EB direction.

ZVar(WB) /N= average arterial turning volume that affects WB direction.

Vart-L(EB), = arterial left-turning volume that exits the arterial EB direction at the ith

intersection.

Vart-R(EB) = arterial right-turning volume that exits the arterial EB direction at the ith

intersection.









Vart-L(B), = arterial left-turning volume that exits the arterial WB direction at the ith

intersection.

Vdr-R(WB), = arterial right-turning volume that exits the arterial WB direction at the ith

intersection.

N = Total number of driveways involved within the segment.

* ~Vopp/N: Average Arterial Volume that is Opposed to Arterial Left Turns (vph/ln)

The main goal of this variable is to capture the influence of the arterial left-turning

vehicles on the arterial through vehicles of the opposing direction. This variable is

expressed as the sum of the arterial volume that is just upstream of each driveway divided

by the number of driveways involved, and this affects the mid-block delay of the

opposing direction vehicles.

Generally, this variable can be expressed as:

pp(EB) artup(EB), for WB Mid-Block Delay calculation
N 2N,


pp(B) art-p(B)) for EB Mid-Block Delay calculation
N ENJ

Where:

XVopp(EB) /N = average EB arterial volume opposed to left turns from WB direction.

IVopp(WB) /N = average WB arterial volume opposed to left turns from EB

direction.

X(Vart-up(EB))= sum of the EB arterial volume that is upstream of each ith

intersection.









X(Vart-up(Bj) = sum of the WB arterial volume that is upstream of each jth

intersection.

Ni = number of driveways that serve EB left turns.

Nj = number of driveways that serve WB left turns.

* X,: Arterial Degree of Saturation

Another variable that is considered for the final analytical model is the degree of

saturation of the arterial segment. This variable is estimated based on the HCM

methodology (Chapter 16), according to the following equation:


X =
s(g/C)

Where:

v = the arterial demand (vph)

s = the saturation flow rate (vph)

g = the effective green time of the signal (s)

C = the cycle length (s)

The degree of saturation is a candidate variable for the model because it represents

the unmet demand at the traffic signal and thus the arterial congestion. When the degree

of saturation increases, it is expected that the mid-block delay would increase due to

frequent vehicle interactions.

* FFS: Arterial Free Flow Speed (mph)

The arterial free flow speed is another candidate variable for the analytical model.

It is speculated that when the FFS is high, sudden vehicle decelerations due to turning

maneuvers would yield additional delays to through vehicles than for lower FFS. This

candidate variable may be important for uncongested conditions where vehicles can









achieve desired travel speeds, while in congested conditions all vehicles travel with

relatively the same (low) speed and thus, the FFS may not be an explanatory variable.

* P: Parking Activity (vph/ft)

This factor describes the parking frequency of the arterial segment for every

available parking space of 20 ft. The parking activity may affect the travel time as the

vehicles that perform such a maneuver (either by parking or by leaving the parking space)

would cause the arterial oncoming vehicles to decelerate.

* Ndr: Number of driveways per 1000 ft

An important variable for the regression model is the number of driveways per

1000 ft, which can be considered as the driveway density. It is expected that the more the

driveways, the more the opportunities for turning maneuvers, either from the arterial

vehicles or from the driveways' vehicles, leading to more chances for vehicle frictions.

For the model development, it is assumed that the mid-block delay of each direction is

affected by the number of driveways that are adjacent to that direction.

* DT: Bus Dwell Time (s)

The final variable to be considered for the model is the bus dwell time. This

variable represents the time that the bus is stopped at the bus stop and it is used only for

the two-lane one-way model, as this is the only simulation model available with this kind

of input. The effect of buses on vehicle delays is considered from the aspect that vehicles

generally tend to decelerate when passing through a bus-stop in the presence of a bus.

Apart from the variables described here, several interaction terms were also

considered as candidate variables for the regression models. Some of the candidate

interaction terms are the interactions between the total arterial left-turning volume (Va.t-

L) and the total arterial opposing volume (IVopp), (for two-way arterials only),and the









interaction between the driveway density (Ndr) with the total arterial turning volume

(XVart-L+XVart-R) or with the total driveway turning volume (XVdr-L++Vdr-R). These

interactions were tested in terms of their applicability and their significance.

Regression Models

For the development of the analytical models, both datasets of two-lane one-way

and two-lane two-way arterials are further divided into congested and non-congested,

depending on the conditions under which they operate. The distinction between the two

traffic conditions is not an easy task, since the selected criterion should be as clear as

possible. In both cases, the criterion for separating the data is the discharge to demand

ratio at the downstream arterial signal since this performance measure can effectively

distinguish between the two states.

A schematic of the measurements required for the calculation of the discharge to

demand ratio is illustrated in Figure 6-3. The measurement of the discharge is the average

flow that traverses the downstream arterial segment, VAR (i.e., the segment between the

last driveway and the stop line). The demand measurement equals the discharge from the

previous segment, VAR-TH, minus the arterial turning volume, VAR-TURN, plus the demand

entering from the driveway, DDR.

ARTERIAL LINK





VAR T__

D Downstream
Arterial Segment


Figure 6-3 Measurement of discharge to demand ratio.









It is expected that in undersaturated conditions, the discharge to demand ratio is

near one, since in those cases all the demand is accommodated and the vehicles are

discharged unimpeded at the signal. On the contrary, under congestion, the discharge is

significantly lower than the demand, and as such the ratio is expected to be significantly

lower than one. Thus, all datapoints with a low discharge to demand ratio were

considered to represent congested conditions. The threshold for determining the two

operating conditions was set by visually inspecting the graphs of the discharge to demand

ratio versus the mid-block delay that are presented in Figures 6-4 and 6-5 (a) and (b). The

graph that corresponds to the two-lane one-way cases appears in Figure 6-4 while the

two-lane two-way graphs for the low volume and high volume data are shown in Figure

6-5 parts (a) and (b), respectively. As is appears from the graphs that follow, the

boundary value was selected to be 0.95, to account for variability of the simulation

model. The discharge to demand ratio appears to be slightly higher than 1.00 for some

datapoints, which intuitively is incorrect; however this is a result of volume fluctuations

during the simulation period.








56




TWO-LANE ONE-WAY ARTERIALS


050 055 060 065 070 075 080 085 090 095 100 105 110
DischargelDemand Ratio



Figure 6-4 Dataplot of mid-block delay vs. discharge to demand ratio for two-lane one-
way arterials.


TWO-LANE TWO-WAY ARTERIALS
LOW VOLUME LEVEL


050 055 060 065 070 075 080 085 090 095 100 105 110
DischargelDemand Ratio











TWO-LANE TWO-WAY ARTERIALS
HIGH VOLUME LEVEL
160

140 Reduced discharge -
upstream congestion Low discharge and
120 demand rates






60



Uncongested *
datapoints100 *

050 055 060 065 070 075 080 085 090 095 100 105 110
Discharge to Demand Ratio
B

Figure 6-5 Dataplot of mid-block delay vs. discharge to demand ratio for two-lane two-
way arterials. (A) Mid-block delay and discharge to demand ratio for low
volume level. (B) Mid-block delay and discharge to demand ratio for high
volume level.

It is important to note that congestion appeared to take place for different reasons

and with different effects. Several datapoints were identified to describe congested

conditions although the ratio of discharge over demand would be high, near the threshold

of 0.95. This usually occurred when the arterial discharge is low but also the demand

measured at the downstream segment is reduced, due to congestion further upstream (i.e.,

the measurement of the demand is a function of the number of vehicles discharged

upstream on the same link; if this value is reduced due to congestion then the demand

would also be low). This type of congestion has different effects depending on the type of

facility. In both cases of two-lane one-way and two-lane two-way models the arterial

turning volume at the most downstream driveway is replenished by the volume that









enters the arterial through the driveway, leading to a discharge rate that approaches

demand and for this reason the ratio remains high.

However, for the two-lane two-way scenarios, another type of congestion is also

observed which yields diminishing discharge over demand ratio associated with very

high delays. This usually occurred (Figure 6-6) when the vehicle discharge at the

eastbound or westbound upstream segment was impeded by arterial left-turning traffic

that was blocking the through movement (due to high opposing traffic). In these cases,

the downstream segment of the arterial (where demand and discharge is measured) does

not accommodate a large number of vehicles and thus, the discharge to demand ratio is

reduced. Furthermore, driveway vehicles cannot enter the arterial eastbound direction as

they would also have to perform a left-turn maneuver, but they lose the priority due to the

arterial left-turning vehicles or vehicles from the westbound direction. This type of

congestion is also referred to as demand starvation.



Reduced discharge rate




Reduced discharge rate


Figure 6-6 Congestion occurrence in two-lane two-way arterials

It was also observed that in several cases the discharge to demand ratio was lower

than 0.95 but the mid-block delay ranged within low levels. These cases typically

occurred when the demand at the driveways had very high values and not all of that

demand could discharge. However, that does not necessarily mean that the arterial is









operating under congested conditions. As such, these datapoints were considered as

uncongested conditions.

Lastly, it should be noted that a few simulation runs yielded very low discharge to

demand ratios, discharge to capacity ratios, long queues on the arterial and extremely

large mid-block delay. These datapoints represent gridlock and they were eliminated

from the database since there is no distinguishable mid-block delay in stop-and-go traffic.

Regression Model for Two-Lane One-Way Arterials

The distinction of the data in the two datasets yields two different regression

models that are presented in the following table, along with the respective ANOVA

tables. The derivation of the final independent variables of the models is based on the

candidate variables described in an earlier section. The candidate variables were

transformed appropriately, in order to achieve a better model fit, depending on their

relationship with the mid-block delay. Characteristic trends of the independent variables

versus mid-block delay are plotted in the figures that follow.










Table 6-5 Mid-block delay equations for two-lane one-way uncongested conditions
Mid-Block Delay Equation for Uncongested Conditions

MBD= -8.08 + 0.266 x eP +1.73 x Nd + 0.261 x FFS + 0.0161 x DT
(6-2)
+0.00551 r +0.00488 ~
N N
Predictor Coef SE Coef T P
Constant -8.0783 0.3026 -26.70 0.000
eP*xe 0.265828 0.004175 63.68 0.000
Ndr 1.72951 0.06496 26.63 0.000
FFS 0.261140 0.006249 41.79 0.000
DT 0.016097 0.003633 4.43 0.000
EVd/N 0.005505 0.001351 4.08 0.000
EVa/N 0.004879 0.001010 4.83 0.000
S = 3.104 R-Sq = 63.8% R-Sq(adj) = 63.8%
ANOVA Table
Source DF SS MS F P
Regression 6 75569 12595 1307.04 0.000
Residual Error 4441 42794 10
Total 4447 118364
Independent Variables:
ep x --- exponentiate X. = arterial degree of saturation, 3 = 3.951
Nd --- number of driveways per 1000 ft
FFS --- arterial free-flow speed (mph)
DT --- bus dwell time (s)
EVd/N --- average turning volume that enters the arterial link (vph)
EVa/N --- average turning volume that exits the arterial link (vph)



It was decided to use the exponential Xc instead of the candidate variable Xc

(arterial degree of saturation) as this variable appeared to describe better to the dataset.

The parameter estimate / was obtained with regression through the origin of the

independent variable Xc and the dependent variable In(MBD). The result of the

regression yielded a parameter estimate / = 3.951.

The effect of parking activity was found to be non-significant and for this reason it

was removed from the final equation of mid-block delay (Equation 6-2).


































100 200 300 400 500
Average Arterial Turning Volume


Figure 6-7 Relationship between mid-block delay and independent variables for the two-
lane one-way uncongested model. (A) Average arterial turning volume,
XVart/N. (B) Average driveway turning volume, XVdrN


UJ


50


*




20
2o-I ,, --- -----

10


5 1W 150 2U 250 300
Average Diveway Turing Volume


350 4W 450 500U










Table 6-6 Mid-block delay equations for two-lane one-way congested conditions
Mid-Block Delay Equation for Congested Conditions

MBD=285-66.2x "p -0.184xeP" +10.4xNd, +0.359xFFS
Demand (6-3)
-172 ,/D 0.262 V,
N N
Predictor Coef SE Coef T P
Constant 285.41 74.50 3.83 0.000
V,, /Demand -66.24 71.40 -0.93 0.355
e' -0.1842 0.1646 -1.12 0.264
Ndr 10.383 2.097 4.95 0.000
FFS 0.3592 0.1185 3.03 0.003
(EVd/D)/N -172.317 8.314 -20.73 0.000
EVaI/N -0.26250 0.02111 -12.43 0.000
S = 13.15 R-Sq = 82.3% R-Sq(adj) = 81.8%
ANOVA Table
Source DF SS MS F P
Regression 6 178171 29695 171.70 0.000
Residual Error 222 38394 173
Total 228 216565
Independent Variables:
V,, /Demand --- arterial volume to demand ratio
e --- exponentiate X, = arterial degree of saturation, 3 = 4.352
Nd --- number of driveways per 1000 ft
FFS --- arterial free-flow speed (mph)
(EVd/D)/N --- average turning volume that enters the arterial link to demand ratio (vph)
EVa/N --- average turning volume that exits the arterial link (vph)



Similarly to the previous model the exponential Xc was used instead of the

candidate variable Xc (arterial degree of saturation). The parameter estimate / was

obtained with regression through the origin of the independent variable Xc and the

dependent variable In(MBD). In this case, the result of the regression yielded a parameter

estimate / = 4.352.

In the case of congested conditions the parking activity was found to have no effect

on mid-block delay; thus it was removed from the respective equation (Equation 6-3).








63



160

140






o 40
12











0 o 100 150 20 25 OD 350
Average Arterial Turning Volume
A


160

140


100 D 1 O230 0 0 5 O 070 0 0 0 1


80distnctono cogestdv ---- ngeteddt a te lo
60
0 40



20

0


000 010 020 030 040 050 060 070 080 090 100
Average Diveway Volume to Denmand Rtio
B


Figure 6-8 Relationship between mid-block delay and independent variables for the two-
lane one-way congested model. (A) Average arterial turning volume, XVat/N.
(B) Average driveway turning volume to demand ratio, (XVdr/D)/N.


Regression Model for Two-Lane Two-Way Arterials


The distinction of congested vs. uncongested data at the low volume dataplot


(Figure 6-5 (a)) appears to be straightforward, if one considers the discharge to demand


ratio threshold of 0.95. However, the dataplot of Figure 6-5 (b) appears more complex


but this is understandable given the complexity of traffic operations on high volume two-










way arterials. In Figure 6-5 (b), the datapoints with a low ratio but also low mid-block

delay were considered for the uncongested model.

The regression analysis performed on both databases of congested and uncongested

conditions yields the results presented in the following table. Representative dataplots of

mid-block delay versus the independent variables of each model are also available. These

dataplots are useful for distinguishing trends among the variables, depending on the

prevailing traffic conditions.

In both congested and uncongested two-lane, two-way models the presence of

buses is not considered as an independent variable as the simulation input models do not

include such a variable.

Table 6-7 Mid-block delay equations for two-lane two-way uncongested conditions
Mid-Block Delay Equation for Uncongested Conditions

MBD=-13.9+0.0126x V +0.126xFFS+0.03100- +0.0128 P"
N N (6-4)
Zv V xZV
+0.00502 --+0.608xNd, +0.105 -
N 104
Predictor Coef SE Coef T P
Constant -13.9070 0.6274 -22.17 0.000
Vup 0.0125814 0.0005931 21.21 0.000
FFS 0.125672 0.009947 12.63 0.000
EVa/N 0.030951 0.002189 14.14 0.000
EVopp/N 0.0128054 0.0002931 43.69 0.000
EVd/N 0.005021 0.002875 1.75 0.081
Ndr 0.60799 0.05581 10.89 0.000
(EVrt-L* EVo,)/104 0.104880 0.001704 61.56 0.000
S = 6.174 R-Sq = 64.7% R-Sq(adj) = 64.7%
ANOVA Table
Source DF SS MS F p
Regression 7 479460 68494 1797.09 0.000
Residual Error 6860 261462 38
Total 6867 740922
Independent Variables:
Vp --- arterial volume at the beginning of the link
FFS --- arterial free-flow speed
EVa4/N --- average turning volume that exits the arterial link (vph)
Vopp/N --- average arterial traffic that opposes to left turns (vph)
EVd/N --- average turning volume that enters the arterial link (vph)
Nd --- number of driveways per 1000 ft
(EVrt-L* EVopp)/104 --- Interaction between arterial right turns and opposing volume
(vph vph)














The parking frequency was found to be not significant in the determination of mid-


block delay for uncongested conditions in two-lane, two-way arterials.


70

60





0







0 50 100 150 200 250 300 350








60
('Vart-L* Vopp)/104











40 _
70 -i----------------------------------














I
o
20 -
2 0 ---- ------------------------ --


Figure 6-9 Relationship between mid-block delay and independent variables for the two-
lane two-way uncongested model. (A) Interaction between total arterial left-
turning volume and total arterial opposing volume, (EVa -L* 2Vopp)/104. (B)
Number of driveways per 1000 ft, Ndr.


O 1 2 3 4 5










Table 6-8 Mid-block delay equations for two-lane two-way congested conditions
Mid-Block Delay Equation for Congested Conditions
V ZV/ ID
MBD=17.8-6.99x "- -25.6 d +0.0983xe + +0.250xFFS
Demand N (6-5)
+0.00428x ZVr1, +0.0418x ZV,,, -0.0265x ZVd +0.0123x Vp
ZV xZV
+2.91x N +0.331xP-0.0832
104
Predictor Coef SE Coef T P
Constant 17.760 1.945 9.13 0.000
Vup/Demand -6.991 2.044 -3.42 0.001
Vdr/N*D -25.635 1.291 -19.85 0.000
ePxe 0.098332 0.005147 19.11 0.000
FFS 0.24979 0.02408 10.38 0.000
EVat-r 0.004277 0.002156 1.98 0.047
1Vat-i 0.041828 0.002893 14.46 0.000
EVd, -0.026469 0.001888 -14.02 0.000
Vopp 0.0123063 0.0004202 29.29 0.000
Nd. 2.9092 0.4439 6.55 0.000
P 0.3307 0.1775 1.86 0.062
(EV,-L* EVop)/104 -0.083248 0.005995 -13.89 0.000
S = 12.94 R-Sq = 52.9% R-Sq(adj) = 52.8%
ANOVA Table
Source DF SS MS F P
Regression 7 99395118 90359 540.03 0.000
Residual Error 5283 83291 167
Total 5290 1877241
Independent Variables:
Vup/Demand --- ratio of arterial volume at the beginning of the link and demand
(EVd/D)/N --- average turning volume that enters the arterial link to demand ratio
(vph)
e *x --- exponentiate X, = arterial demand to capacity, 3 = 3.977
FFS --- arterial free-flow speed
EVa-r --- total arterial right-turning volume (vph)
EV-_1 --- total arterial left-turning volume (vph)
EVd, --- total turning volume that enters the arterial link (vph)
Vopp --- total arterial traffic that opposes to left turns (vph)
Ndr --- number of driveways per 1000 ft
P --- parking frequency per 20 ft of available space (vph/ft)
(EVatL* EVopp)/104 --- Interaction term between arterial right turning vehicles and
arterial opposing volume (vph2)



The arterial degree of saturation was transformed to exponential Xo, and the

calculation of the parameter / was done as described previously. The derived parameter


estimate in this case is 3.977.







67


The parking frequency for every 20 ft of available parking space is found to be not


significant considering a confidence level of 95%; however, as the p-value slightly


exceeds the threshold of 0.05 for that confidence level, it was decided to include the


parameter in the final model.


0.2 0.4


0.6
Vup/Demand


0.8 1


160

140
**
120


100





40
20

0


(EVart-L*EVOpp)/104
B


Figure 6-10 Relationship between mid-block delay and independent variables for the
two-lane two-way congested model. (A) Arterial volume to demand ratio,
Vup/Demand. (B) Interaction between total arterial left-turning volume and
total arterial opposing volume, (Vat_-L* XVopp)/104.


i~

**~* I~C
**+





F~ ~i I 'Q~
T









Discussion-Description of Independent Variables

A more detailed description of the selected independent variables and a discussion

of their relationship to mid-block delay is presented in this section.

e '

For the two-lane one-way model, the arterial mid-block delay under uncongested

conditions is found to increase exponentially with the degree of saturation measured at

the downstream signal. When the v/c ratio is low, the flow levels even at the downstream

signal are low, which means that vehicles do not interact frequently within the arterial

segment. But as the v/c ratio increases and queues are formed at the downstream signal,

the arterial operation is moving towards more congested conditions. This would mean

that the vehicles are traveling in smaller headways and thus, their frequent interactions

are expected to increase the mid-block delay.

However, when a two-lane one-way arterial is operating under congested

conditions the degree of saturation affects negatively the mid-block delay. When already

in oversaturated conditions, an increase of the v/c ratio at the downstream signal means

that the number of vehicles discharged is increased. This is an indication that the arterial

operation moves towards uncongested states, and to reduced mid-block delay.

In the case of two-lane two-way arterials that operate under congested conditions,

as the degree of saturation increases then the mid-block delay also increases. This is

contrary to the congested model of the one-way arterial; however, the two arterial

configurations display differences in the trend of the degree of saturation. The arterial

degree of saturation is not included in the two-lane, two-way uncongested conditions

model because it was not found to be significant.









V

The arterial mid-block delay, under uncongested conditions, increases as the

feeding volume increases. This means that when the arterial volume is high and it

operates under undersaturated conditions, there are more opportunities for vehicle

interactions and more maneuvering actions that affect drivers' speed and thus their travel

times. On the other hand, under low flow conditions, the mid-block delay is reduced.

V
Demand

In congested conditions, the flow-to-demand ratio is significantly less than 1.

Furthermore, as operations move towards more congested conditions, the actual

throughput is reduced, and thus the ratio is also reduced, which leads to an increase of

arterial mid-block delay as a result of the overall congestion. On the other hand, an

increase of the ratio yields more arterial throughput and therefore less congestion and

delay for the through vehicles. This variable is used for the congested models of both

arterial configurations.

* Nd

This variable represents the number of driveways involved in the study link per

1000 ft of arterial link length, as it is described in an earlier section. The driveway density

is found to be positively related to the mid-block delay in all four models; as the number

of driveways increases there are frequent occurrences of vehicles that decelerate to either

exit or enter the arterial segment; thus the through vehicles may encounter more delay

within the link.

* FFS









The arterial free-flow speed is also included in all final models. Based on the

analysis, it was shown that when the arterial free-flow speed increases, then the mid-

block delay also increases. For example, if the free-flow speed is relatively high, then the

through vehicles will have to decelerate more in order to reduce their speed significantly

and avoid the turning vehicles that are traveling with low speeds.

Y Vdr
N

The mid-block delay depends on the average number of vehicles that enter the

arterial segment from the driveways through left or right turns. By increasing the number

of incoming vehicles, the delay that the through drivers experience is increased, due to

additional vehicle interference. This variable appears in the uncongested models for both

cases of two-lane one-way and two-way arterials with the same trend. In the two-lane,

two-way congested model the total number of vehicles that enter the arterial segment

from the driveways (XVdr) is considered instead.

SVdr /D
N

The ratio of the driveway turning volume to the respective demand, averaged

through the corresponding driveways is found to be an important variable for both

congested models. In congested conditions this ratio is reduced as there are not enough

available gaps in the traffic stream for the driveway vehicles to discharge. However, an

increase of the ratio means that the driveway vehicles have more opportunities to

discharge, thus the arterial operation moves towards less congested conditions with

overall less mid-block delay.

Vart
N










The average number of vehicles that exit the arterial through right or left turns

affects positively the mid-block delay in uncongested conditions. As the average number

of outgoing vehicles increases, there are more decelerations of the following vehicles and

thus their delay is also increased.

In congested conditions however, where the mid-block delay ranges to high levels,

the arterial throughput is reduced and consequently the number of vehicles that perform

right or left-turn maneuvers reduces. However, as congestion dissipates and the arterial

throughput gradually increases, the number of turning vehicles also increases.
I I-V
art L, Z art R

In the two-lane, two-way arterial model (congested conditions) the total arterial

traffic that performs left and right turns is considered instead of the average turning

activity.

LV
* opp
N

The average arterial volume of the opposite direction that conflicts with left-turning

vehicles affects the mid-block delay of the through drivers of that direction. This

parameter is very important for the two-way model particularly because the arterials

under study have only one lane per direction; thus, a vehicle that performs a left-turn

maneuver would have to search for available gaps and the following vehicles would have

to stop.

SVart-Lx opp
104









The total arterial left-turning traffic is interacted with the total volume from the

opposing direction, in the two-lane two-way models, for both uncongested and congested

conditions.

In Equation (6-4) (two-way uncongested conditions) the interaction impacts

positively the mid-block delay. Additionally, as the total arterial left turning volume

ZVart-L, or the total opposing volume Vopp, increase, the mid-block delay also increases.

In the two-way congested model (Equation (6-5)) the interaction term parameter has

negative value, but the overall effect of the two independent variables involved remains

positive. Note that the marginal effect of an arterial turning vehicle remains positive as

long as the total opposing traffic is less than 5,000 vph. This assumption is valid if one

considers that there is only one lane per direction and a maximum of four driveways

involved.

Similarly, the total opposing traffic has a positive effect in mid-block delay if the

sum of the arterial left-turning traffic is less than approximately 1,500 vph. This is

reasonable if the boundary condition of four driveways is taken into account.

* DT

The bus dwell time is included in the analytical model for the two-lane one-way

only. The analysis shows that the bus dwell time indeed affects the through vehicles'

mid-block delay, as the arterial through vehicles generally tend to decelerate when

passing through a bus-stop. This variable is not included in the two-way models because

buses were not present at the arterials from the field data collection.

* P

The parking activity influences positively the mid-block delay. Increasing parking

activity for every 20ft of available parking space yields an increase on the delay that the









arterial through drivers experience. This variable appeared to be important only for the

congested model of the two-lane, two-way arterials.

Conclusions

In this study four analytical models for estimating arterial mid-block delay are

presented. These models estimate mid-block delay for two-lane two-way and two-lane

one-way arterial streets that operate under both congested and uncongested conditions.

Arterial travel time can be estimated as a function of the mid-block delay, the intersection

control delay and the arterial running time when operating under free-flowing conditions.

The final arterial travel time model for all four cases is expressed as:

LinkLength
TT = MBD + CD + nk(6 6)
FFS

Where:

TT is the arterial travel time (sec/veh).

MBD is the mid-block delay and it is calculated from equations 6-2 through 6-5,

according to the arterial configuration and the operating conditions (sec/veh).

CD is the intersection control delay which can be obtained from the HCM

methodology (Chapter 16) (sec/veh).

Link Length/FFS is the arterial running time under free-flowing conditions

(ft/(ft/sec)).

A descriptive example problem of the calculation of an arterial link travel time is

presented in Appendix D. The example problem calculates the mid-block delay, the

control delay and the arterial running time under free-flowing conditions components of

the model for a two-lane one way arterial link.









Different regression models are used depending on whether the arterial is congested

or not. To distinguish between the two conditions the ratio of the arterial discharge over

the demand needs to be calculated. This calculation is performed at the arterial segment

just upstream of the traffic signal (between the last driveway and the traffic signal). The

discharge to demand ratio is expected to be approaching 1 as operations move towards

uncongested conditions. This criterion should be used with caution, as it is possible that

the discharge to demand ratio is approaching 1, but the measured demand is reduced due

to congestion on the upstream segment. In this case the models that correspond to

congested conditions should apply.

An important conclusion of this research is that both uncongested and congested

models of the two-lane one-way arterials have better goodness-of-fit measures than the

two-lane two-way models. The equations developed for the two-lane one-way models

have reasonable R2 values; however in the two-lane two-way models the R2 values are

lower. This is mostly due to the different degree of complexity between the operations of

the two arterial configurations. From the analysis it can be seen that the distinction

between the congested and uncongested states is very clear in the case of the one-way

arterials and the regression equations fit fairly well to the data. Additionally, the dataplots

shown in Figures 6-7 and 6-8 are very descriptive of the relationship between the mid-

block delay and the independent variables.

However, the vehicle interactions that take place in the two-way arterials are much

more complicated and for this reason the dataplots in Figures 6-9 and 6-10 are scattered.

Moreover, the two-lane two-way models were developed from data taken from two






75


adjacent arterial links; therefore, they also capture the effect of spillbacks, which is a

realistic representation frequently observed in the field.















CHAPTER 7
CONCLUSIONS AND RECOMMENDATIONS

The research conducted for this thesis resulted in some important conclusions and

recommendations concerning the modeling limitations of CORSIM and the analytical

models of mid-block delay. The research findings associated with the modeling

limitations of CORSIM are as follows:

CORSIM allows for network-wide input of the maximum right- or left-turning

speed but it does not allow for individual modification of the turning speed at each

driveway. The input of the turning speed at each driveway can be an important factor

when analyzing the impact of right or left-turning vehicles on the arterial through traffic.

In this research this issue was addressed by "forcing" the vehicles to enter the driveway

with low free-flow speed, which corresponded to the site specifications of throat width

and curb radius.

The gap acceptance algorithm of CORSIM is based on the type of sign (stop or

yield), the driver characteristic code (measure of aggressiveness) and the number of lanes

that the driver has to cross. However, in real-life conditions, these factors are not the only

basis for gap acceptance. It is more realistic that the drivers become impatient when

waiting for a long time for a gap and this impatience usually reduces their safety margins.

Thus, when waiting for a long time for a gap, it is possible that the drivers'

aggressiveness increases.









The major results of the development of the analytical models that estimate arterial

mid-block delay are summarized below:

In uncongested conditions, the mid-block delay is affected by the turning vehicles

from the arterial or the driveway. The turning maneuvers increase the vehicle interactions

and thus, they force the arterial through vehicles to decelerate, which leads to an increase

of their mid-block delay. On the other hand, under congested conditions turning vehicles

from the arterial do not contribute to the mid-block delay, as the arterial speed is already

decreased. Also, vehicles turning into the arterial do not affect mid-block delay, as they

have fewer opportunities to enter the arterial. In these situations, there is high delay on

the arterial. In summary, the mid-block delay under congested conditions is primarily

influenced by the mainline volume and its degree of saturation.

Since different variables influence mid-block delay under congested and

uncongested conditions the two datasets of two-lane one-way and two-lane two-way

arterials were divided in congested and uncongested. By observing the data it was

perceived that for the same arterial throughput the arterial was operating under either

free-flowing conditions or congestion. The selected criterion for separating the data is the

discharge to demand ratio at the downstream arterial signal. When the discharge to

demand ratio is higher than 0.95 then the arterial operates under uncongested conditions.

When the ratio is below the 0.95 threshold the arterial is congested. However, if the

reduced discharge to demand ratio is the result of high demand at the downstream

driveway while the overall arterial performance is free-flowing, then these conditions can

also be considered as uncongested conditions. It is shown that this criterion conforms









well to the data for the two-lane one way arterials. For the two-lane two-way arterials the

distinction is not as clear due to the increased complexity of the system.

The final regression models for the two arterial configurations of study are given by

the equations below:

For two-lane one-way arterials under uncongested conditions the mid-block delay

(MBD) is:

MBD = -8.08 +0.266 x ee +1.73 x N +0.261 xFFS +0.0161 xDT +0.00551 dVr +0.00488 -"r
N N

R2 = 63.8%

For two-lane one-way arterials under congested conditions the mid-block delay

(MBD) is:

V Vd/D _V
MBD =285- 66.2x "x 0.184xePX +10.4xNd +0.359xFFS -172Vd -0.262-
Demand N N

R2 = 82.3%

For two-lane two-way arterials under uncongested conditions the mid-block delay

(MBD) is:

IV IV IV
MBD= -13.9 + 0.0126x V + 0.126x FFS + 0.0310 + 0.0128 + 0.00502 d
N N N

+0.608xNdr +0.105 r-,L
104

R2 = 64.7%

For two-lane two-way arterials under congested conditions the mid-block delay

(MBD) is:


MBD=17.8-6.99x -25.6 vD + 0.0983xeIPX +0.250 xFFS+ 0.00428x V
Demand N
V0.1 x V+3
+ 0.0418x YV,,, 0.0265x Vd + 0.0123x VPP + 2.91x Ndr +0.331 xP- 0.0832 a104
art! opp104









R2 = 52.9%

where all variables as described in Chapter 6.

An important result of the analysis is that the models for the two-lane one-way

arterials have reasonable goodness-of-fit measure. In the case of the two-lane two-way

arterials the goodness-of-fit measure of the models is low. This is mostly due to the

enclosed degree of complexity in two-way operations, which results from the interactions

between the two opposing traffic streams. In two-way arterials it is not always possible to

isolate traffic operations by movement of direction, especially in the case of two-lane

arterials, where the left turns have greater influence to the oncoming through vehicles.

The final travel time models derive from substituting the mid-block delay equations

into the equation presented in Chapter 6 (Equation 6 6). Thus, the necessary steps for

estimating the arterial travel time with this method are:

Calculate the intersection control delay (can be calculated by applying the HCM

methodology (Chapter 16)).

Estimate the arterial link travel time under free-flowing conditions.

Calculate the discharge to demand ratio at the downstream segment of the arterial

link under study to determine its operational characteristics define whether it is

operating under congested or uncongested conditions and apply the corresponding

equation. Refer to Chapter 6 for guidelines regarding the measurement of the discharge to

demand ratio.

Obtain all pertinent variables that apply to the mid-block equation. These may be

the turning movements to and from all the driveways, the arterial degree of saturation

(measured just upstream of the intersection), the arterial throughput (measured at the









beginning of the arterial link) with the corresponding demand, the arterial free-flow

speed, the driveway density (per direction of travel), the average arterial volume that

opposes left turns from the arterial, the parking frequency for every 20 ft of available

parking space, and the bus dwell time.

The research findings provide a good insight to the various parameters that affect

the travel time within an arterial link, however it is important that the equations presented

above are validated with additional field data.

Furthermore, it is recommended that the development of travel time models with

consideration of mid-block delays are extended to other arterial configurations such as

four-lane or six-lane two-way arterials.














APPENDIX A
PHASING-TIMING DIAGRAMS


INTERSECTION OF NORTH
COLLEGE, PA


ATHERTON STREET AND PARK AVENUE, STATE


PHASE 2+5 PHASE 2+6 PHASE 4 PHASE 3


Maximum 38 20
Minimum 2 19 10
Yellow 3 3 3
All-Red 2 2
Passage 2 2
Pedestrian 8 7* 10*
Memory Non-Locking Ped. Recall Non-Locking
Cycle 1 25 43 7 25
Cycle 2 31 32 7 20
Cycle 3 22 49 7 22
*Upon pedestrian actuation only


Plan No. Time Cycle Offset Remarks
1 1:00 Flash
2 6:00 100 sec 86 sec Cycle 1
3 7:00 90 sec 39 sec Cycle 2
4 10:00 100 sec 86 sec Cycle 1
5 16:00 100 sec 68 sec Cycle 3
6 18:00 100 sec 86 sec Cycle 1
Offsets referenced to start of Phase 2+6 yellow


_ I


C


-i'~t


t








INTERSECTION OF PARK AVENUE
COLLEGE, PA


AND NORTH ALLEN STREET, STATE


PHASE 1 PHASE 2 PHASE 3


Maximum 0 0
Minimum 2 0 2
Yellow 3 3 3
All-Red 1 1
Sec/act. 2
Max init. 23
Passage 1.5 5.4 2
To reduce 10
Before red. 23
Min gap 3
Pedestrian 16 16*
Memory Non-Locking Ped. Recall Non-Locking
Cycle 1 7 47 20
Cycle 2 7 43 24
Cycle 3 11 43 20
*Upon pedestrian actuation only


Plan No. Time Cycle Offset Remarks
1 6:00 Free Cycle 1
2 7:15 85 sec 10 sec Cycle 1
3 8:15 Free Cycle 1
4 11:30 85 sec 10 sec Cycle 2
5 13:30 Free Cycle 1
6 14:15 85 sec 13 sec Cycle 3
7 17:45 Free Cycle 1
8 24:00
Offsets referenced to start of Phase 2 yellow


t


4:n


r
ir


,1-









INTERSECTION OF PARK AVENUE AND SHORTLIDGE ROAD, STATE
COLLEGE, PA
PHASE 1 PHASE 2 PHASE 3


Maximum 0 0
Minimum 2 0 2
Yellow 3 3.5 3.5
All-Red 1.5 1.5
Sec/act. 2
Max init. 16
Passage 1.5 3.6 2
To reduce 10
Before red. 16
Min gap 3
Pedestrian 15 15*
Memory Non-Locking Ped. Recall Non-Locking
Cycle 1 7 45 20
Cycle 2 7 45 20
Cycle 3 7 39 26
*Upon pedestrian actuation only


Plan No. Time Cycle Offset Remarks
1 6:00 Free Cycle 1
2 7:15 85 sec 22 sec Cycle 1
3 8:15 Free Cycle 1
4 11:30 85 sec 26 sec Cycle 2
5 13:30 Free Cycle 1
6 14:15 85 sec 26 sec Cycle 3
7 17:45 Free Cycle 1
8 24:00
Offsets referenced to start of Phase 2 yellow


r,'


N


f








INTERSECTION OF WEST BEAVER AVENUE
COLLEGE, PA


AND SPARKS STREET, STATE


PHASE 1 PHASE 2




I:
Max G 0
Min G 3 3
Yellow 3 3
All-Red 1 1
Passage
Pedestrian 7 14
Memory Non-Locking Ped. Recall
Cycle 1 32 25
Cycle 2 60 30
Cycle 3 64 36

Plan No. Time Cycle Offset Remarks
1 1:00 Flash
2 6:00 65 sec Free Cycle 1
3 7:00 90 sec 1 sec Cycle 2
4 9:30 65 sec Free Cycle 1
5 16:00 100 sec 71 sec Cycle 3
6 18:00 65 sec Free Cycle 1
Offsets referenced to start of Phase 1 yellow










INTERSECTION OF SOUTH ATHERTON STREET AND
AVENUE, STATE COLLEGE, PA


WEST BEAVER


PHASE 1+6 PHASE 2+6 PHASE 4


Maximum 0 0
Minimum 0
Yellow 3 3 3
All-Red 1 1
Passage 3
Pedestrian 11 19
Memory Max recall Min recall Ped. Recall
Cycle 1 20 42 38
Cycle 2 15 35 40
Cycle 3 19 40 41

Plan No. Time Cycle Offset Remarks
1 0:00 100 sec 56 sec Cycle 1
2 7:00 90 sec 32 sec Cycle 2
3 10:00 100 sec 56 sec Cycle 1
4 16:00 100 sec 90 sec Cycle 3
5 18:00 100 sec 56 sec Cycle 1
Offsets referenced to start of Phase 2+6 yellow


11









INTERSECTION OF EAST BEA
STATE COLLEGE, PA


VER AVENUE AND SOUTH PUGH STREET,


PHASE 2 PHASE 4+8


Maximum 22
Minimum 32
Yellow 3 3
All-Red 1.5 2.5
Passage 3
Pedestrian 7 14
Memory Min recall Ped. recall
Cycle 1 41 39
Cycle 2 22 23
Cycle 3 58 42

Plan No. Time Cycle Offset Remarks
1 3:00 Flash
2 6:00 80 sec 3 sec Cycle 1
3 7:00 45 sec 20 sec Cycle 2
4 10:00 80 sec 3 sec Cycle 1
5 16:00 100 sec 75 sec Cycle 3
6 18:00 80 sec 3 sec Cycle 1
Offsets referenced to start of Phase 2 yellow


W:


t

t I













INTERSECTION OF EAST BEA
STATE COLLEGE, PA
C


VER AVENUE AND SOUTH GARNER STREET,


PHASE 2 PHASE 4+8


Maximum 18
Minimum 37 3
Yellow 3.5 3.4
All-Red 1.5 1.6
Passage 3
Pedestrian 15 14
Memory Min recall Ped. recall
Cycle 1 52 28
Cycle 2 59 31
Cycle 3 63 37

Plan No. Time Cycle Offset Remarks
1 0:00 80 sec 33 sec Cycle 1
2 7:00 90 sec 56 sec Cycle 2
3 10:00 80 sec 33 sec Cycle 1
4 16:00 100 sec 90 sec Cycle 3
5 18:00 80 sec 33 sec Cycle 1
Offsets referenced to start of Phase 2 yellow


W:


_LL


_TT



















APPENDIX B
TURNING MOVEMENT AND LOOP DETECTOR DATA


PARK AVENUE, 04/20/2004, 11:45AM-12:45PM
INTERSECTION DATA


ST: Atherton Stre t


ST: Park Avenue


ST: Atherton Stre t


ST: Prk AenueST: ark venu


ST: Park Avenue


SEastbound Westbound
Time Left Thru Right Truck Total Left Thru Right Truck Total Total Int.
11:45-12:00 0 0 0 0 0 33 1 92 9 126 659
12:00-12:15 0 0 0 0 0 45 0 85 7 130 715
12:15-12:30 0 0 0 0 0 47 1 136 8 185 750
12:30-12:45 0 0 0 0 0 32 1 94 4 127 664
SUM 0 0 0 0 0 157 3 407 28 568
% 0.28 0.01 0.72 0.05


ST: Alien Street ST: Allen Street
Northbound Southbound
Time Left Thru Right Truck Total Left Thru Right Truck Total
11:45- 12:00 13 3 18 1 34 0 1 4 1 5
12:00- 12:15 26 2 30 2 57 1 1 1 0 3
12:15- 12:30 25 2 20 1 47 0 1 0 0 1
12:30 12:45 11 0 19 3 31 1 1 0 0 2
SUM 75 7 88 7 169 2 4 5 1 11
% 0.44 0.04 0.52 0.04 0.19 0.38 0.43 0.09


ST: Park Avenue ST: Park Avenue
__ Eastbound Westbound
Time Left Thru Right Truck Total Left Thru Right Truck Total Total Int.
11:45 12:00 2 95 4 3 101 33 112 0 5 145.01 284
12:00-12:15 0 102 11 4 113 25 107 1 4 132 306
12:15 -12:30 6 73 11 2 90 20 156 0 12 176 314
12:30 -12:45 2 104 10 3 116 24 118 1 6 143 292


% 0.02 0.89 0.09 0.03 0.17 0.83 0.00 0.05


Time Left Thru Right Truck Total Left Thru Right Truck Total
11:45- 12:00 0 212 35 9 247 88 198 0 7 286
12:00- 12:15 0 249 34 8 283 97 204 1 9 302
12:15-12:30 0 223 32 16 255 99 211 0 8 310
12:30- 12:45 0 199 42 15 241 99 196 0 6 295
SUM 0 883 144 47 1027 384 809 1 29 1194
% 0.00 0.86 0.14 0.05 0.32 0.68 0.00 0.02


10 374 36 I 13 I 420 102 493 2 I 28 I 597


ST: Atherton Street ST: Atherton Street-----


Northbound


Southbound


SUM













ST: Shortlidge Road
Northbound


ST: Grove Alley
Southbound


Time Left Thru Right Truck Total Left Thru Right Truck Total
11:45 -12:00 15 0 10 8 25 1 0 0 0 1
12:00 -12:15 14 0 8 5 22 1 0 1 0 2
12:15 -12:30 16 0 10 5 27 0 0 0 0 0
12:30 -12:45 11 0 11 4 21 0 0 1 0 1
SUM 57 0 38 22 95 2 0 2 0 4
% 0.60 0.00 0.40 0.23 0.46 0.00 0.54 0.00


ST: Park Avenue


ST: Park Avenue


Time Left Thru Right Truck Total Left Thru Right Truck Total Total Int.
11:45- 12:00 1 100 13 4 113 13 133 0 7 146 285
12:00- 12:15 0 125 6 5 131 5 121 0 4 126 281
12:15- 12:30 0 82 10 3 93 13 164 0 14 177 296
12:30- 12:45 0 117 8 3 126 13 135 1 8 150 298
SUM 1 424 38 15 463 44 553 1 33 598
% 0.00 0.92 0.08 0.03 0.07 0.92 0.00 0.06


DRIVEWAY DATA


FIRST LINK: Between Atherton and Alien
Driveway 3 (Lischer Rd)
Time EB-R WB-L NB-L NB-R
veh IHV veh HV veh HV veh HV
11:45-12:00 30 4 0 8 2
12:00-12:15 23 1 2 2
12:15-12:30 15 2 0 6 2
12:30-12:45 30 3 1 1 7
TOTAL 98 11 3 27


Driveway 4 (N. Borrowes Str)
Time EB-L WB-R SB-L SB-R
veh HV veh HV veh HV veh HV
11:45-12:00 2 2 2 1 3
12:00-12:15 6 0 0 1
12:15-12:30 2 1 1 2 1 2 1
12:30-12:45 4 2 0 1
TOTAL 14 6 6 8


SECOND LINK: Between Alien and Sortlidge
Driveway 1 (McKee)
Time EB-L WB-R SB-L SB-R
veh HV veh HV veh HV veh HV
11:45-12:00 1 5 1 5
12:00-12:15 3 1 4 3 1 4
12:15-12:30 5 5 1 5 5 1
12:30-12:45 4 4 4 4
TOTAL 14 19 11 8


I


ST- Park Avenue ST: Park Avenue


Eastbound


Westbound














Driveway 2 (Hohles)
Time EB-L WB-R SB-L SB-R
veh HV veh HV veh HV veh HV
11:45-12:00 1 1 0 0
12:00-12:15 1 3 1 2
12:15-12:30 1 5 2 1 1
12:30-12:45 0 2 1 0
TOTAL 3 11 4 4


LOOP DETECTOR DATA

BURROWES & ALLEN
EB WB
11:45-12:00 101 129
12:00-12:15 113 128
12:15-12:30 90 186
12:30-12:45 116 130
TOTAL 420 573


LISCHER & ATHERTON
EB WB
123 126
140 130
99 185
143 127
505 568


HOLMES & SHORTLIDGE
EB WB
113 148
131 134
93 184
126 148
463 615


TURNING MOVEMENT AND LOOP DETECTOR DATA FOR PARK AVENUE,
04/20/2004, 04:30PM-05:30PM


INTERSECTION DATA


ST: Atherton Stre t


ST: Atherton Street' --ST: AthertonStree


ST: Atherton Stre t


Time Left Thru Right Truck Total Left Thru Right Truck Total
04:30-04:45 0 314 45 3 360 94 256 0 8 350
04:45-05:00 0 326 61 4 386 78 243 0 6 321
05:00-05:15 0 343 70 9 413 69 240 0 5 309
05:15-05:30 0 376 65 6 440 73 243 0 2 316
SUM 0 1359 241 22 1600 314 983 0 21 1297
% 0.00 0.85 0.15 0.01 0.24 0.76 0.00 0.02


Time Left Thru Right Truck Total Left Thru Right Truck Total Total Int.
04:30-04:45 0 0 0 0 0 37 2 148 5 187 897
04:45-05:00 0 0 0 0 0 47 4 164 8 214 922
05:00-05:15 0 0 0 0 0 41 1 160 7 202 924
05:15-05:30 0 0 0 0 0 36 1 159 4 196 953

SUM 0 0 0 0 0 160 8 631 24 799
% 0.20 0.01 0.79 0.03


: ar venu : ar venue
e I


Northbound


Southbound


cT. P klr Airn..fl


CT. P klr Airn..a


Eastbound


Westbound