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Wang, Xiaodong.
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Effects of U-turns on capacity at signalized intersections and simulation of U-turning movement by synchro
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by Xiaodong Wang.
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Thesis (M.S.C.E.)--University of South Florida, 2008.
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ABSTRACT: The primary objective of this study is to evaluate the operational effects of U-turn movement at signalized intersections. More specifically, the research objectives include the following parts: To identify the factors affecting the operational performance of U-turning vehicles. In this case, we are particularly interested in the U-turn speeds of U-turning vehicles; To evaluate the impacts of U-turns on capacity of signalized intersections, and; To simulate U-turn movement at signalized intersections using Synchro and validate the simulation results. To achieve the research objectives, extensive field data collection work was conducted at sixteen selected sites at Tampa Bay area of Florida. The data collected in the field include: U-turning speed; Left turning speed; Turning radius Queue discharge time; Control delay; Hourly traffic volume, and; Percentage of U- turning vehicles in left turn lane.Based on the collected field data, a linear regression model was developed to identify the factors affecting the turning speeds of U-turning vehicles at signalized intersections. The model shows the turning speed is significantly impacted by the turning radius and the speed of U-turning vehicles increases with the increase of turning radius. On the basis of field data field data collection, a regression model was developed to estimate the relationship between the average queue discharge time for each turning vehicle and the various percentages of U-turning vehicles in the left turn traffic stream. Adjustment factors for various percentages of U-turning vehicles were also developed by using the regression model. The adjustment factors developed in this study can be directly used to estimate the capacity reduction due to the presence of various percentages of U-turning vehicles at a signalized intersection.The developed adjustment factors were used to improve the simulation of U-turn movement at signalized intersection by using Synchro. The simulation model was calibrated and validated by field data. It was found that using the developed adjustment factors will greatly improve the accuracy of the simulation results for U-turn movement.
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Mode of access: World Wide Web.
System requirements: World Wide Web browser and PDF reader.
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Co-advisor: Jian John Lu, Ph.D.
Co-advisor: Liu Pan, Ph.D.
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U-turn adjustment factor
Linear regression model
U-turn speed
Synchro simulation
Control delay
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Effects of U-Turns on Capacity at Signalized Intersections And Simulation of U-Turning Movement by Synchro by Xiaodong Wang A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Civil Engineering Department of Civil and Environmental Engineering College of Engineering University of South Florida Co-major Professor: Jian Lu, P.E., Ph.D Co-major Professor: Liu Pan, Ph.D Manjriker Gunaratne Date of Approval March 28, 2008 Keywords: (U-Turn Adjustment Factor, Linear Regression Model, U-Turn Speed, Synchro Simulation, Control Delay) Copyright 2008, Xiaodong Wang
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Dedication This work dedicates to all the people who ever gave me the help.
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Acknowledgements I am hereby grateful to my major professor Dr. Jian Lu who gave me a lot of advice and help in the completion of this thesis and in my two-year academi c program as well. Meanwhile, I really would like to thank Dr. Pan Liu who is my co-professor of thi s thesis. I appreciate Dr. Pan LiuÂ’s guidance and encourage in the processing of fulfilling this work. I also would like to thank my committee member Dr. Manjriker G unaratne for his spending time to take care of my thesis defense. In addition to the people I mentioned above, I would like to thank all the staffs in the Graduation School and De partment of Civil and Environmental Engineering of University of South Florida for their hard wor k.
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i Table of Contents LIST OF TABLES iii LIST OF FIGURES vi ABSTRACT vii CHAPTER 1 INTRODUCTION 1 1.1 Background 1 1.2 Problem Statement 3 1.3 Research Objective and Outline of the Thesis 3 CHAPTER 2 LITERATURE REVIEW 6 2.1 The HCM Capacity of Signalized Intersection 6 2.2 Past Studies on Saturation Flow Rate 11 2.3 Past Studies on Saturation Headway 13 2.4 Past Studies on Safety and Operational Impacts 15 2.5 Summary of Past Studies 16 CHAPTER 3 METHODOLOGY 18 3.1 Methods to Analyze the U-turn Speed 18 3.2 The Method to determine the U-turn Adjustment Factors 19 3.3 Method to Validate the U-turn Adjustment Factors 20
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ii CHAPTER 4 DATA COLLECTION 21 4.1 Field Data Collection for Turning Speed Regression Model 21 4.2 Field Data Collection for Determining the U-turn Adjustment Factors 24 4.3 Data Collection for Calibration and Validation 26 4.4 Measurement Technique for Obtaining the Field Control Delay 27 CHAPTER 5 DATA ANALYSIS 30 5.1 Data Analysis on U-turn Speed 30 5.2 Determination of U-turn Adjustment Factors 37 5.3 Synchro Simulation 44 5.3.1 Introduction of Synchro Simulation Software Package 44 5.3.2 Models Calibration 46 5.3.3 Models Validation 48 5.3.4 Sensitive Test 49 CHAPTER 6 SUMMARY, CONCLUSIONS AND RECOMMODATTIONS 55 6.1 Summary 55 6.2 Conclusions 56 6.3 Practical Meaning of This Study 57 6.4 Limitations 58 6.5 Discussions and Recommendations 59 REFERENCE 61 APPENDIX 64
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iii List of Tables Table 2-1. Summary of Saturation Flow Results in Some Countries 12 Table 4-1. Description of Selected Study Sites 1 23 Table 4-2. Description of Selection Sites 2 25 Table 4-3. Description of Selected Sites 3 26 Table 4-4. Acceleration Â– Deceleration Delay Correction Factor, CF (s) 28 Table 5-1. Descriptive Statistics of Dependent and Independent Variables Disaggregate Regression Model 31 Table 5-2. Summary for Disaggregate Regression Model 32 Table 5-3. AONVA Test for Disaggregate Regression Model 32 Table 5-4. Statistical Results for Disaggregate Regression Model 32 Table 5-5. Descriptive Statistics of Dependent and Independent Variables fo r Aggregate Regression Model 34 Table 5-6. Summary for Aggregate Regression Model 35 Table 5-7. ANOVA Test for Aggregate Regression Model 35 Table 5-8. Statistical Results for Aggregate Regression Model 35 Table 5-9. U-turn Adjustment Factors for Varying Percentages of U-turning Vehicles 41 Table 5-10. Descriptive Statistics for Data Collection in the field 41 Table 5-11. Regression Results (R Square Value) for Average Queue Dischar ge Model 42 Table 5-12. Regression Results (ANOVA) for Average Queue Discharge Model 42 Table 5-13. Regression Results (t-Statistics) for Average Queue Dis charge Model 42
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iv Table 5-14. Description of Selected Sites for Measuring Control Delay 46 Table 5-15. Computation Procedure for Control Delay of Site 1 47 Table 5-16. Computation Procedure for Control Delay of Site 2 47 Table 5-17. Computation Procedure for Control Delay of Site 3 48 Table 5-18. Comparison of Control Delay 48 Table 5-19. Summary of Sensitive Test for Site 1 52 Table 5-20. Summary of Sensitive Test for Site 2 53 Table 5-21. Summary of Sensitive Test for Site 3 54 Table A-1. Descriptive U-turn Speed Data of Bruce B Downs Blvd @ Commerce Palms Blvd 64 Table A-2. Descriptive U-turn Speed Data of Fowler Ave @ 56 th Street 65 Table A-3. Descriptive U-turn Speed Data of Bruce B Downs Blvd @ Cross Creek Blvd 66 Table A-4. Descriptive U-turn Speed Data of Bearss Ave @ Florida Ave 67 Table A-5. Descriptive U-turn Speed Data of Bruce B Downs Blvd @ Highwoods Preserve PKWY 68 Table A-6. Descriptive U-turn Speed Data of CR 581 (Bruce B Downs Blvd) @ County Line 69 Table A-7. Descriptive U-turn Speed Data of Dale Mabry HWY @ Fletcher Ave 70 Table A-8. Descriptive U-turn Speed Data of Dale Mabry HWY @ Stall Rd 71 Table A-9. Descriptive U-turn Speed Data of Waters Ave @ Dale Mabry HWY 72 Table A-10. Descriptive U-turn Speed Data of Dale Mabry HWY @ Waters Ave 73 Table A-11. Descriptive U-turn Speed Data of Dale Mabry HWY @ Mapledale Bl vd 74
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v Table A-12. Descriptive U-turn Speed Data of Dale Mabry HWY @ Bearss Ave (Ehrlich Ave) 75 Table A-13. Descriptive U-turn Speed Data of Dale Mabry HWY @ Carrollwood SPGS 76 Table A-14. Descriptive U-turn Speed Data of Hillsborogh Ave@ Armenia Ave 77 Table A-15. Descriptive U-turn Speed Data of Hillsborogh Ave @ Lois Ave 78
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vi List of Figures Figure 2-1. Signalized Intersection Queue Discharge Model 13 Figure 4-1. Aerial Map for Typical Selected Site Location 23 Figure 5-1. Plot of Average Queue Discharge Time Versus Various Percentages of U-turning Vehicles 39 Figure 5-2. Plot of Unstandardized Residuals Versus Independent Variable (PU T) 43 Figure 5-3. Plot of Unstandardized Residuals Versus Independent Variables ( 2 PUT ) 43 Figure 5-4. Trend of Control Delay Variation under Different Percentages of U-turning Vehicles for Site 1 50 Figure 5-5. Trend of Control Delay Variation under Different Percentages of U-turning Vehicles for Site 2 51 Figure 5-6. Trend of Control Delay Variation under Different Percentages of U-turning Vehicles for Site 3 51
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vii Effects of U-Turns on Capacity at Signalized Intersections And Simulation of U-Turning Movement by Synchro Xiaodong Wang ABSTRACT The primary objective of this study is to evaluate the operational effects of U-turn movement at signalized intersections. More specifically, the re search objectives include the following parts: To identify the factors affecting the operational performance of U-turning vehicles. In this case, we are particularly interested in the U-turn speeds of U-turning ve hicles. To evaluate the impacts of U-turns on capacity of signalized intersections, and To simulate U-turn movement at signalized intersections using Sync hro and validate the simulation results. To achieve the research objectives, extensive field data collect ion work was conducted at sixteen selected sites at Tampa Bay area of Flori da. The data collected in the field include: U-turning speed Left turning speed Turning radius Queue discharge time Control delay
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viii Hourly traffic volume, and Percentage of Uturning vehicles in left turn lane. Based on the collected field data, a linear regression model was developed to identify the factors affecting the turning speeds of U-turning vehicles at signalized intersections. The model shows the turning speed is significantly impacted by the turning radius a nd the speed of U-turning vehicles increases with the increase of turning radius. On the basis of field data field data collection, a regression model was developed to estimate the relationship between the average queue discharge time for each turning vehicle and the various percentages of U-turning vehicles in the lef t turn traffic stream. Adjustment factors for various percentages of U-turning vehicles w ere also developed by using the regression model. The adjustment factors developed in this s tudy can be directly used to estimate the capacity reduction due to the pre sence of various percentages of U-turning vehicles at a signalized intersection. The developed adjustment factors were used to improve the simulation of U-turn movement at signalized intersection by using Synchro. The simulat ion model was calibrated and validated by field data. It was found that using the developed adjustment factors will greatly improve the accuracy of the simulation results for U -turn movement.
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1 CHAPTER 1 INTRODUCTION 1.1 Background In Florida, the increase of the use of restrictive median and direc tional median openings has generated many U-turns at signalized intersections For estimating the operational effects of U-turns, there are still no widely accept ed theories or methods. As we all know, U-turning movements are considered as left turns for es timating the saturation flow rate. However, according to the real traffic f eatures, the operational effects caused by U-turns and left turns are different. The F lorida Department of Transportation mandated that all the new or reconstructed arteria ls of which the design speeds over 40 mph must be applied with restrictive medians. Moreove r, Florida has replaced a lot of conventional median openings by directional openings. And accordin g to the access management standards in Florida, direct left turn onto t he major arterials are prohibited. As a result, direct left turn onto the roadway was ta ken place by the right turn followed by U-turn at the downstream signalized intersections. So, t he quantity of U-turning movements keeps increasing. Apparently, the usage of rest rictive median openings and directional median openings can improve the safety perform ance of arterials. However, the controversial issue has also been presented. The increasing of U-turn at the signalized intersection will negatively affect the capacity and Level of Service of the intersection. This is a pair of conflicts which n eed to be solved. But before
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2 resolving the problem, we need to understand what are the operational eff ects of U-turn and how does U-turn impact the intersection on capacity. In this study, I chose the turning speed as the major feature of U -turning movements. Data were collected from 16 sites in Tampa Bay area. Basic ally, 375 U-turn speeds were collected along with the traffic volume, signal timing, and queue le ngth for calculating the control delay. Three sites were selected to record queue discha rge time. On the basis of the field data collection, one regression model was developed to es timate the relationship between U-turn speed and turning radius. From this model, it can be found that the U-turn speeds are significant related to turning radius and quantify the relationship between them. Another regression model was established based on the field data for estimating the relationship between average queue discharge time for each turning vehicle under different U-turning vehiclesÂ’ percentages in the U-turn and left turn mixed traffic stream. Also, U-turn adjustment factors for variable percentages of U-tur ning vehicles were determined by the regression model. The U-turn adjustment factors can be used to estimate the capacity reduction result from variable percenta ges of U-turning vehicles at signalized intersections. Furthermore, 15 signalized intersections were selected to cali brate the Synchro simulation models. The simulation models created based on the field dat a. The results from Synchro simulation validated that the U-turn adjustment factors can be used to estimate the impact on capacity at signalized intersections.
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3 1.2 Problem Statement In terms of Highway Capacity Manual 2000, the U-turning movement is treated as left turn for estimating the saturation flow rate. Saturation f low rate is one of the most critical and important factor in evaluating the capacity of a la ne or a lane group at a signalized intersection. However, based on the field data and rea l situation, the operational impacts of U-turns are different from which of left t urns. From the field data, it is easily to find that the turning speed of U-turns and the tur ning speed of left turns are different. Thus, the saturation headway will be interrupted if the U-turning vehicles mix in the left lane. Due to the U-turn speed is lower than the left t urn speed, the capacity of the lane will be reduced. According to the field data review a nd analysis, it is found that U-turning movement will increase the delay of the approach. As the c ontrol delay is the criteria for evaluating the Level of Service of a signalize d intersection, thereby the U-turning movements have an adverse effect on Level of Service. At present, there is no widely accepted theory or method for estima ting the effects on capacity caused by U-turning movements. It is necessary to analyze the f eature of U-turns and find out a method to estimate the effects of U-turning vehicles on capacity at a signalized intersection. 1.3 Research Objective and Outline of the Thesis In this study, the essential part is that the U-turn adjustment factors for different percentage of U-turning vehicles were determined. The purpose of ca lculating these adjustment factors is to quantify the effects of U-turning moveme nt on capacity at signalized intersections. The reduction of capacity will directly res ult in the descending of
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4 Level of Service. The results of this study might help transpor tation practitioner to estimate the Level of Service of signalized intersection mor e adequately and to analyze the operational impacts for signalized intersection more rationally. The primary objective of this study is to evaluate the operational effects of U-turn movement at signalized intersections. More specifically, the objec tive of this study can be summarized as following: To identify the factors affecting the operational performance of U-turning vehicles. In this case, we are particularly interested in the U-turn speeds of U-turning ve hicles. To evaluate the impacts of U-turns on capacity of signalized intersections, and To simulate U-turn movement at signalized intersections using Sync hro and validate the simulation results. This thesis consists 6 chapters. The Introduction states the backgr ound of this research and presents the problems. The Literature Review goes ove r the past studies related to U-turning movements at signalized intersection which have been conducted. In the literature review, some important basic concepts were ill uminated. Some methods for researching U-turn were also illustrated. The chapter on Methodology explains the methods in this study. It includes the methods for field data collecti on, regression model, filed control delay observing technique, Synchro simulation and sensitive analysis, etc. In the following chapter on Data Collection, the field data and the se lection of study sites and field observational procedures for this study are presented. T he next chapter focuses on data analysis, analyzing the related factor to impact the U-t urn speed, developing model to estimating the U-turn adjustment factors under different p ercentage if U-turning vehicles and calibrating, validating the Synchro simulation models bas ed on the field data.
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5 The final chapter summarizes the results and findings of this st udy, draws conclusions, and proposes recommendations for future studies. Reference and Appendix f ollow at the end of the thesis.
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6 CHAPTER 2 LITERATURE REVIEW As discussed in the previous chapter, this thesis concentrates on the effects of U-turning movements on capacity at signalized intersections. In c hapter 2, the contents of signalized intersections in Highway Capacity Manual (HCM) are briefly reviewed and the past researches related to U-turn at signalized intersec tions are reviewed as well. Specifically, the concerns are saturation flow rate, saturati on headway, operational impacts of U-turn, conflicts between U-turning vehicles and left-t urning vehicles, and some concepts or methods for analyzing the operational impacts by U -turn at signalized intersections. 2.1 The Capacity of Signalized Intersection In the Highway Capacity Manual [HCM 2000], the analysis of capa city at signalized intersections focuses on the computation of saturation flow rates, capa cities, v/c ratios, and level of service for lane groups. In this study, we consider the c apacity of a certain lane group as the major factor for analyzing the operational impac ts by U-turn. The capacity for each lane group is defined as the maximum rate of f low for a given lane group that may pass through an intersection under prevailing traffi c, roadway, and signal conditions. The flow rate is generally measured or projected for a 15-min period, and capacity is stated in vehicles per hour (vph). Capacity at signal ized intersections is based
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7 on the concept of saturation flow and saturation flow rate. Traffic conditions include volumes on each approach, the distribution of vehicles by movement (lef t, through, and right), the vehicle type distribution within each movement, the locat ion and use of bus stops within the intersection area, pedestrian crossing flows, and parking movements on approaches to the intersection. Roadway conditions include the basic geom etrics of the intersection, including the number and width of lanes, grades, and lane u se allocations (including parking lanes). Signalization conditions include a full def inition of the signal phasing, timing, and type of control, and an evaluation of signal progressi on for each lane group. The analysis of capacity at signalized intersections focus es on the computation of saturation flow rates, capacities, v/c ratios, and level of service for lane groups The saturation flow rate is defined as the maximum rate of tr affic flow that may pass through a given lane group under prevailing traffic and roadway conditions assuming that the lane group has 100 percent of real time available as effec tive green time. The flow ratio for a given lane group is defined as the ratio of the actual or projected demand flow rate for the lane group (vi) and the saturation flow rate (si ). The flow ratio is given the symbol (v/s)i for lane group i. The capacity of a given lane group may be stated as shown in Equation: (/) iii CSgC = Where, i C = capacity of lane group i, vph; i S = saturation flow rate for lane group i, vphg; and Green ratio defined as, / i gC = effective green ratio for lane group i.
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8 The capacity formula indicates that the capacity at a sign alized intersection determined by saturation flow rate and effective green ratios for the subject lane group. Specifically, Saturation flow rate is a basic parameter used to derive capacity. It is defined as above. It is essentially determined on the basis of the minimum headway that the lane group can sustain across the stop line as the vehicles de part the intersection. Saturation flow rate is computed for each of the lane groups establ ished for the analysis. A saturation flow rate for prevailing conditions can be determined directly from field measurement and can be used as the rate for the site without adj ustment. If a default value is selected for base saturation flow rate, it must be adj usted for a variety of factors that reflect geometric, traffic, and environmental conditions spec ific to the site under study. The computation of saturation flow rate begins with the selection of an ideal saturation flow rate. And then adjust for a variety of prevailing conditions which are not ideal. The equation is stated as below: 0wHVpbbaLULTRTLpbRpb s = sNg fffffffffff Where, s = saturation flow rate for subject lane group, expressed as a total for all la nes in lane group (vph); 0 s = base saturation flow rate per lane (pc/h/ln); N = number of lanes in lane group; w f = adjustment factor for lane width; HV f = adjustment factor for heavy vehicles in traffic stream; g f = adjustment factor for approach grade;
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9 p f = adjustment factor for existence of a parking lane and parking ac tivity adjacent to lane group; bb f = adjustment factor for blocking effect of local buses that stop w ithin intersection area; a f = adjustment factor for area type; LU f = adjustment factor for lane utilization; LT f = adjustment factor for left turns in lane group; RT f = adjustment factor for right turns in lane group; Lpb f = pedestrian adjustment factor for left-turn movements; and Rpb f = pedestrian-bicycle adjustment factor for right-turn movements The ideal conditions at a signalized intersection approach are: 12 foot lane witch level approach grade all passenger cars in the traffic stream no left or right turning vehicle in traffic stream, no parking adjacent to a travel lane within 250 ft of stop line, intersection located in a non-CBD area. The procedure of directly measuring the saturation flow rate in field is described in the HCM 2000. The principle of direct measurement is based on the saturation flow rate and minimum departure headway (saturation headway) 3600/ s sh =
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10 Where, s h =saturation headway, sec. In this procedure, the HCM 2000 indicates that saturation headway is usua lly achieved after fourth to seventh vehicle has entered the intersect ion from a standing queue. The HCM 2000 recommends estimating the saturation headway by a verage the total time elapsed between the fifth vehicle and the vehicle at the end of the queue. The cycle for given lane group has two simplified components: effec tive green time and effective red time. Effective green time is the time that may be used by vehicles on the subject lane group at the saturation flow rate. Effective r ed time is defined as the cycle length minus the effective green time. The effective gr een time is another important variable affecting the capacity of a signalized intersecti on. The effective green time for a lane group can be determined by subtracting the start-up lost time (experience at the beginning of the phase) and the clearance lost time (experienced a t the end of the phase) from the total time (Green + Yellow + All-red) available for a lane group. It can be stated as: () iislcl gGYtt =+-+ Where, G = actual green time, sec; i Y = sum of actual yellow time plus all-red clearance time, sec; i g = effective grren time for movement i, sec; sl t = start-up lost time, sec/cycl. cl t = clearance lost time, sec/cycle.
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11 Meanwhile, the start-up lost time is typically measured as the cumulative extra time it takes for the th n vehicle to pass the stop line (where n=4 as is assumed in the H CM 2000). Therefore, the start-up lost time can be calculated as: 4 4 sls tth =- Where, 4 t = total time from signal turning green to the rear axle of the fourth vehicle passing the stop line, sec; and s h = saturation headway, sec. 2.2 Past Studies on Saturation Flow Rate Saturation flow rate is the maximum flow rate that can pass t hrough a given lane group under prevailing traffic and roadway conditions, assuming that t he lane group has 100 percent of real time available as effective green time. As previously discussed, saturation flow is fundamentally important in signalized intersecti on capacity estimation. It is the basic for determining traffic-signal timing and evaluating intersection performance. The saturation flow rate computations under prevailing conditions ar e based on the saturation flow rate under ideal conditions as well as on the adjustment factors for prevailing conditions. Ideal conditions assume clear weather, all pas senger cars in the traffic stream, good pavement conditions, level terrain, 12ft minimum lane width, no heavy vehicle in traffic stream, and no local buses stopping within the intersecti on area. The following Table 2-1 shows the saturation flow rate in some countries:
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12 Table 2-1 Summary of Saturation Flow Results in Some Countries [Niittymaeki and Prusula 1997] Saturation Flow Values (per hour of green time per lane) Country passenger car unit (pcu) / vehicle(veh) Author, Year United Kingdom 2080 pcu Kimber 1986 Canada 1900 veh Teply 1991 Australia 2475 veh Troutbeck 1994 Australia 2000 veh Troutbeck 1994 Israel 2176 veh Hakkert 1994 Poland 1890 veh Tracz, Tarko 1991 Yugoslav 2290 veh Stanic 1994 South Africa 1928 veh Stander 1994 Indonesia 600 pcu/m Baeng 1994 Germany 2000veh Brilon 1994 Hong Kang 1895 veh Lam 1994 Lithuania 2045 veh Noreika 1994 Japan 2000 pcu Fujiwara 1994 Finland 1940 veh Niittymaeki, Purula 1995 HCM 1994 1900 pcu TRB 1994 In the past study, basically 2 alternatives were applied for e stimating saturation flow rate. One is the queue discharge model, and the other is the dischar ge headway model. One of the most widely accepted queue discharge model is Webste rÂ’s model. The following Figure 2-1 illustrates the discharge of vehicles at a loaded signalized intersection.
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13 Figure 2-1 Signalized Intersection Queue Discharge Model [Shantaeu 1988] It indicates when the vehicle queue is released by a traffic light turning to green; the flow rate gradually increases and reaches a steady average departure rate after several seconds. The departure flow remains around this value until the lights changes to yellow, then, it falls steadily to zero. This uniform departure flow rate is termed as the saturation flow rate, S [Shantaeu 1988]. 2.3 Past Studies on Saturation Headway As defined in Highway Capacity Manual (HCM), saturation flow ra te is the equivalent hourly rate at which previously queue vehicles can traverse an intersection approach under prevailing conditions, assuming that the green signal is ava ilable at all time and no lost time is experienced [HCM]. HCM estimates a laneÂ’s Â“idealÂ” saturation flow rate to be 1,900 passenger cars per hour of green time per lane Different adjustment factors are applied to address the impacts of prevailing conditions that do not meet the definition of Â“idealÂ” conditions, including lane width and lateral clear ance, number of
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14 lanes, the presence of heavy vehicles and grades, turning movements, int erchange density, lane distribution, and environmental factors. The discharge headway method is widely used to estimate the saturation flow rate at a signalized inte rsection. Numerous studies have indicated that the discharge headway would converge to a constant headway after the fourth to sixth discharged passenger car crossing the stop line after the beginning of the green phase. The constant headway is defined as the saturati on headway, which can be measured in the field by recording the discharge headway aft er the fourth or fifth discharged vehicle. The relationship between saturation flow rate and saturation headway is shown in the following equation: S=3600/h Where, s = saturation flow rate (vehicles per hour per lane); h = saturation headway (s); and 3,600 = number of seconds per hour. In HCM 2000, U-turns are treated as left turns for estimation of the saturation flow rate. However, the operational effects of U-turns and left turns a re different. U-turning vehicles have slower turning speeds than left-turning vehicles. Thus, the increased U-turns at signalized intersections may adversely affect the intersection capacity. A study conducted by Adams and Hummer in 1993 evaluated the effects of U-turns on left-turn saturation flow rates. The research team selected four interse ctions with exclusive left-turn lanes and protected signal phasing and recorded the satura tion flow rates and U-turn percentages for 198 queues during midday peaks on weekdays. T he data analysis showed that Â“a saturation flow reduction factor appears necessary for left-turn lanes that had large percentages of U-turns. Saturation flow rates were si gnificantly lower when
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15 queues have more than 65% U-turnsÂ”. However, the analyses also showed no c orrelation between the saturation flow rate and the percentage of U-turns f or queues with 50% U-turning vehicles or less. The results of this study suggested tentative saturation flow rate reduction factors of 1.0 for U-turn percentages below 65, 0.90 for Uturn percentages between 65 and 85, and 0.80 for U-turn percentages exceeding 85. The invest igators also recommended that a follow-up investigation focus on intersections that have high percentages of U-turns, restrictive geometries, or high percent ages of U-turning heavy vehicles. In 1996, Tsao and Chu recorded 600 headways of left-turning passeng er cars and 160 headways of U-turning passenger cars in Taiwan Their re search revealed that the average headways of U-turning passenger cars are significantl y larger than those of left-turning passenger cars. The effects of U-turning vehicles depend on the percentage of U-turning vehicles in the left-turn lane, as well as the order of formation in the traffic stream. When it is preceded by a left-turning vehicle, the aver age headway of a U-turning passenger car is 1.27 times that of a left-turning passenger car. When it is preceded by a U-turning vehicle, however, the average headway of U-turning passen ger cars is 2.17 times that of a left-turning passenger car. In their study, Ts ao and Chu assumed that the discharge flow rate of the vehicle reaches a saturation state after the fourth or fifth discharged vehicle, and only the headways after the fifth discharge d vehicle were recorded. 2.4 Past Studies on Safety and Operational Impacts In the evaluation of safety and operational impacts of two alter native left-turn treatments from driveways/side streets, the research team s elected 133 directly left turn sites and 125 right turn followed by U-turn sites, respectively. Cra sh data corresponding
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16 to these sites were compared. The results is that average numbe r of crashes for sites with directly left turn is 16.35 and the average crash number for sites with right turn followed U-turn is 13.90, respectively. When crashes per million vehicle mil es are considered the respective numbers of 3.2 and 2.63. Thus, the results of this research indicate that safety was greater for right turns followed by U-turns than for direct left turns The National Cooperative Highway Research Program (NCHRP) Â– R eport 420 clarified the basic concept of alternative, summarized the safe ty and operational experiences in current practice, and presented application guidelines The report indicated that directional median openings experienced 50% and 40% reductions in major and minor conflicts respectively compared with full median openings. Th ey presented the main advantages of right turn followed by U-turns as compared wit h direct left turns as following: 1) Under moderate to high traffic volume, travel and delay could be less. 2) The capacity of a U-turning movement at the median opening is m uch higher than the capacity of a direct left-turning movement. 3) Right turn followed by U-turns have fewer conflicts than direct left turns. 4) A left turn lane at a median opening for facilitating direct ional left turn and U-turning movements can be designed to store several vehicles because storage i s parallel to the through traffic lanes. 5) A single directional median opening can be used to accommodate traf fic from several upstream driveways, especially when the driveway spacing is ve ry close. Thus, when volumes are from moderate to heavy, the right turn followed by U-tur n may demonstrate more advantages than direct left turns.
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17 2.5 Summary of Past Studies The past researches related to safety evaluation and operational e ffects of U-turn provide the basis for the decision maker to decide on the design mode for the future median opening and access management. If the designers take the res ults of the researches into consideration, apparently, more and more conventional full median openings will be replaced by directional median openings. Meanwhile, fr om the point of view for access management, more direct left turn onto the major a rterial will be prohibited. Consequently, left turn egress maneuver from a driveway or side street will be converted to a right turn followed by U-turn at downstream median ope nings or signalized intersections. That means the number of U-turns will inc reases and the capacity of the signalized intersections which provide with U-tu rn will be effected negatively. Therefore, it is necessary to conduct researches for evaluating the effects of U-turns on capacity of signalized intersections. The past studies on the saturation flow rate provides us with the basis, fundamental concepts and some useful a nalytical methods for estimating the capacity of a lane or a lane group at signalized i ntersections. In this thesis, the features of U-turning movements are presente d and the regression model is developed to explain how the geometric factors affect the U -turn features. Moreover, the essential of this thesis is developing the regre ssion model to determine the U-turn adjustment factors under varying percentages of U-turning vehicles. Eventually, the U-turn adjustment factors are validated by using Synchro Si mulation based on the field data. Briefly, this study can be summarized as three parts: Present the relationship between the U-turn speed and turning radius;
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18 Computing the U-turn adjustment factors; Calibrate the Synchro models and validate the U-turn adjustment factors.
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19 CHAPTER 3 METHODOLOGY In a left turn and U-turn mixed lane at a signalized intersec tion, the turning speed is a main conflict between the left-turning vehicles and U-turning vehicle s and most of crashes in the left turn and U-turn mixed lane are rear-end cr ashes. In the first part of this study, the regression model is developed to analyze that the how the U -turn speed changes under different sites. In the second part of this thesis, anothe r regression model is developed for determining the U-turn adjustment factors under varying percentages of U-turning vehicles. Finally, the third part focuses on calibrating the models in Synchro simulation software and validating the U-turn adjustment factors unde r some typical situations. 3.1 Methods to Analyze the U-turn Speed By the observation on the selected research sites, it can be easi ly found that the turning speed of left-turning vehicles is significantly higher than t he turning speed of U-turning vehicles. As a result, the phenomenon is usually that the l eft turn vehicle will apply a brake when it approaches the stop bar if there is a U-turn vehicle in front of it. So, it can be interpret as the difference between the left tur n speed and U-turn speed causes the main conflict. Since the turning speed is the concern, thus the turning speed is treated as the major feature of U-turning movements. It is necessary t o find out what kind of fact
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20 has a significant relationship to the turning speed and how the fact ors affect the turning speed as well. At the same time, it can be found that the turnin g radius has a highly significant effect on U-turn speed. In this study, 15 signalized intersections with relatively high percentage of U-turning vehicles are selected as research sites. 375 U-turn speeds and the turning radius for every site are measured. The regression model is developed to describe the relations hip between the U-turn speed and turning radius. 3.2 Method to determine the U-turn Adjustment Factors Firstly, the average queue discharge time for each tuning vehicl e was defined as the queue discharge time divided by the number of turning vehicles in the queue. Secondly, several regression models were taken into consideration, and the re gression results were compared. It was found that three different kinds of regression model s were appropriate in describing the relationship between the average queue discharge ti me and U-turn percentages. Specifically, they are a simple linear regress ion model, a linear regression model with an exponential form, and a linear regression model with a quadratic form (second degree polynomial regression model). Statistical analysis found that the second degree polynomial regression model had the best regression results and the best goodness of fit to the field data. Finally, on the basis of the regression results above and the definition of the adjustment factors for turning movements, the equation for calculati ng U-turn adjustment factors for the left turn saturation flow rate can be presente d. With this equation, the U-turn adjustment factors for various percentages of U-turning vehicl es could be
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21 calculated. The U-turn adjustment factors developed in this study c an be directly used to estimate the capacity reduction in a left turn lane due to the pre sence of U-turning vehicles when the signalized intersection has only one left turn la ne in the subject approach. 3.3 Method to Validate the U-turn Adjustment Factors In this part, the major method to validate the U-turn adjustment fac tors is using Synchro simulation. Specifically, three typical sites were se lected to be calibrated. Because the Level of Service (LOS) of a signalized interse ction depends on the control delay of every approach, the criteria for validating the U-turn adjustment factors focused on the control delay which was output from running the Synchro simulation. Therefore, another field data collection was conducted for measuring and calcula ting the control delay. Three typical signalized intersections were selecte d for calibrating. The method for measuring the control delay in the field will be specified in the following chapter. Consequently, the results from Synchro simulation indicates that by adjusting the saturation flow rate based on the U-turn adjustment factors, the contr ol delay output from Synchro simulation will get closer to the real control delay va lues which were measured from field. This result means that by applying the U-turn adjustm ent factors, the capacity reduction due to U-turning movements in a left turn and U-turn mixed la ne can be estimated.
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22 CHAPTER 4 DATA COLLECTION Field data collection at signalized intersection was very import ant in this study. Some aspects need to be considered before conducting the field data collection: Study Objective: different study objectives require different types of dat a. Study sites: the study sites should be chosen according to the study objective and data requirements. Methodology for data collection: in order to get the high quality f ield data, a detailed data collection plan is prepared before performing the field data collection. 4.1 Field Data Collection for Turning Speed Regression Model In this part, the purpose of the field data collection is to get the U-turning speed and left turn speed at different signalized intersections, and compare the two groups of speeds for identifying the difference between the U-turn speed and left speed. Also, the turning radius needs to be measured for developing the regression model for de scribing the relationship between U-turn speeds and turning radius. Specifically, t he followings criteria were used in the sites selection: 1. Grade of approaches were Level; 2. Protected signal phasing for U-turns and left turns; 3. U-turns and left turns share one lane; 4. Only one lane accept U-turns;
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23 5. Relatively high percentages of U-turning vehicles. The specified information of the selection study sites is listed as the foll owing Table 4-1: Table 4-1 Description of Selected Study Sites 1 Signalized Intersections N1 N2 Left Turn Phase Bruce B Downs Blvd @ Commerce Palms Blvd Single 1 P Fowler Ave @ 56th Street Dual 0 P Bruce B Downs Blvd @ Cross Creek Blvd Single 1 P Bearss Ave @ Florida Ave Single 1 P Bruce B Downs Blvd @ Highwoods Preserve Pkwy Single 1 P CR 581 (Bruce B Downs Blvd) @ County Line Single 1 P Dale Mabry HWY @ Fletcher Ave Single 0 P Dale Mabry HWY @ Stall Rd Single 0 P Waters Ave @ Dale Mabry HWY Single 1 P Dale Mabry HWY @ Waters Ave Single 0 P Dale Mabry HWY @ Mapledale Blvd Single 0 P Dale Mabry HWY @ Bearss Ave( Ehrlich Ave) Single 0 P Dale Mabry HWY @ Carrollwood SPGS Single 0 P Hillsborough Ave @ Armenia Ave Single 1 P Hillsborough Ave @ Lois Ave Single 0 P Notes: N1 = number of exclusive left turn lanes; N2 = number of exclusive right turn lanes from other approach of the interse ction;
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24 P = Protected Signal Phasing. The following aerial map is a typical study site. It shows t he location when I was measuring the speed and queue discharge time; the location of digita l camera is marked up in the map as well. Figure 4-1 Aerial Map for Typical Selected Site Location The U-turn speeds were measured by using the speed radar gun when the U-turning vehicles turn around and reach the stop bar. The left turn speeds w ere measured by using speed radar gun as well when the left-turning vehicles move to the c enter of the intersections. The turning radius were measured by the hand whee l from the edge the travel lane of the exclusive left turn lane to the edge the p avement of the corresponding exit lanes including width of medians.
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25 4.2 Field Data Collection for Determining the U-turn Adjustm ent Factor In this study, the effects of U-turns on the capacities of sig nalized intersections were quantified by analyzing the relationship between the percentage of U-turning vehicles the left-turn lane and the 76 Transportation Research Record 1920 average q ueue discharge time for each turning vehicle. Data were collected at three signalized intersections in the Tampa area of Florida. To separate the effects of U-turning ve hicles from other factors that may influence intersection capacity, the following criteri a were used in the selection of the study sites: 1. Lane widths were 12 ft; 2. The approach grade was level; 3. There was no parking adjacent to a travel lane within 250 ft of the stop line; 4. The intersections were located in a non-central business district area; 5. The intersections had exclusive left-turn lane and protected left-turn phasing for left turns; 6. There was insignificant disturbance from a bus stop; 7. There was insignificant disturbance from the right-turning vehic les during the U-turn phase in the other approach of the intersection (right-turning ve hicles in the other approach of the subject signalized intersection are supposed to yield to U-turning vehicles when U-turns are accommodated by a protected left-turn phase; if s ignificant disturbance was observed, the data were excluded from analysis); and 8. The selected street segment needed to have at least three t raffic lanes (including through traffic lanes and an exclusive right-turn lane in the other approach) in each direction; passenger cars can normally make U-turns along a divided six-la ne road (three
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26 lanes each direction) without any geometric restrictions. The sel ected sites are listed in Table 4-2. The traffic flow data and signal timing were recorde d by using two video cameras. Data collection typically started at 4:00 in the aft ernoon. Before recording began, the video cameras were synchronized so that the data extra cted from the different videotapes could be matched. Data collection was conducted during weekday peak periods. Data were not gathered during inclement weather or under unus ual traffic conditions. The following information was gathered by reviewing the videotapes: ( a ) the number of U-turning vehicles and left-turning vehicles in each queue a nd ( b ) the discharge time required for each queue, which was measured as the time that elapsed from the time that the green signal was initiated until the ti me that the rear wheel of the last vehicle in the queue crossed the stop line. The discharge t ime for each queue was recorded by using a Radio Shack liquid crystal display stopwatch, w hich could record discharge times with 0.01-s accuracy. To focus on the characterist ics of passenger car flows, the data related to heavy vehicles and all vehicles behi nd a heavy vehicle were excluded from the analysis. Additionally, only those vehicles that had come to a compl ete stop before the initiation of the green signal were included in the analysis. In total, the study team recorded the queue discharge times for 260 queues, incl uding 571 U-turning vehicles and 1,441 left-turning vehicles. Table 4-2 Description of Selection Sites 2 Signalized Intersection N1 N2 N3 Left Turn Phasing Fowler Ave @ 56th Street Dual 3 0 P Bruce B Downs Blvd @ Newtampa Blvd Single 2 0 P Bruce B Downs Blvd @ Cross Creek Blvd Single 2 1 P
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27 Note: N1 = number of exclusive left-turn lanes. N2 = number of through-traffic lanes in each direction. N 3 = number of exclusive right-turn lanes from other approach of the intersection. P = protected signal phasing. 4.3 Data Collection for Calibration and Validation As discussed in previous chapters, the control delay is the criteria for determining the LOS of a signalized intersection. So, the control delay was sele cted as the major criteria for validating the Synchro simulation models and verifying the correc tness the U-turn adjustment factors. In this part of field data collection, the measurement t echnique provided by HCM 2000 for obtaining the field control delay was applied. Three typical sites were taken into consideration for calibrating the models. The features of these 3 sites match the characteristics which were mentioned above. In addition, the turning ra dius of these 3 sites range from comparatively narrows to wide. Meanwhile, the U-turning vehicles percentages go from 40% to 55%. The Table 4-3 describes the selected sites in this field data collection Table 4-3 Description of Selected Study Sites 3 Signalized Intersection N1 N2 Left Turn Phase Turning Radius (FT) Percentages of U-turning vehicles Bearss Ave @ Florida Ave S 1 P 45 49% Bruce B Downs Blvd @ Highwoods Preserve PKWY S 1 P 72 53% CR 581 (Bruce B Downs Blvd) @ County Line S 1 P 153 41%
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28 Notes: N1 = number of exclusive left turn lanes; N2 = number of exclusive right turn lanes from other approach of the interse ction; P = Protected Signal Phasing; S = Single. The following information should also be measured in the field for cal ibrating the Synchro simulation models: 1. Geometric design and lanes configuration of the selected signalized inters ections; 2. Hourly traffic volume for each lane in each approach; 3. Signal timing; 4. Free-flow speed of the roadway. Based on the above information, the simulation models are able to be calibrated. 4.4 Measurement Technique for Obtaining the Field Control Delay In this study, the measurement technique for measuring the field control delay fol lows the method provided by HCM 2000. The procedure can be briefly stated as following: 1. Before going to the field, several initial parameters need to be determine d: 1) Number of observational lanes, N; 2) Free-flow speed, FFS (mph); 3) Survey count interval, s I (s); 2. Count the number of vehicles in queue for each time interval; Count t he hourly traffic flow in subject lane; Count the U-turning vehicles mixed in the left turn lane, and
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29 3. Calculate the percentages of U-turning vehicles. 4. Compute the field control delay: 1) Total vehicles arriving, tot V ; 2) Stopped-vehicles count, stop V ; 3) Total vehicles in queue, iq V ; 4) Time-in-queue per vehicle, 9. 0 ) ( x V V x I x I D tot iq s s vq = s; 5) No. of vehicles stopping per lane each cycle; stop cV NN ; 6) Accel/Decel correction factor, CF, (CF can be checked out in the following Table 4-4): Table 4-4 Acceleration Â– Deceleration Delay Correction Factor, CF (s) Free-Flow Speed 7 Vehicles 8 19 Vehicles 20 30 Vehicle 37 mi/h 5 2 -1 > 37Â–45 mi/h 7 4 2 > 45 mi/h 9 7 5 Vehicle-in-queue counts in excess of about 30 vehicles per lane are typically unreliable. 7) Number of cycles surveyed, c N ; 8) Fraction of vehicles stopping, stop tot V FVS V = ; 9) Accel/Decel correction delay, ad dFVSCF = (s); 10) Control Delay/vehicle, vqad ddd =+(s).
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30 By following the procedure specified above, the field control delay ca n be obtained, and these control delay values can be used as the criteria for vali dating the Synchro simulation models as well as verifying the correctness of U-turn adjustme nt factors.
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31 CHAPTER 5 DATA ANALYSIS The tasks conducted in the data analysis of this study include: de veloping the regression model for describing the relationship among U-turn speed and different types of vehicles, turning radius and effect of right turn; developing the r egression model for determining the U-turn adjustment factors under various percentages o f U-turning vehicles; calibrating and validating the Synchro simulation models. 5.1 Data Analysis on U-turn Speed As discussed in the previous chapters, the U-turn speed is significant ly lower than left turn speed. This is the major reason for producing the conflicts and c ausing the rear-end crashes between the U-turning vehicles and left-turning vehicles In this chapter, two linear regression models are developed to describe the relationship between U-turn speed and some other factors may affect U-turn speed. Disaggregate linear regression model indicates the relationship a mong U-turn speed and some other external various factors which are likely to affe ct the U-turn speed for every U-turn vehicle. In this study, a disaggregate model is devel oped for identifying the factors that contribute to U-turn speed. The Turning radius, types of vehicles, and effect by right turn vehicles are selected as independent variables, the dependent variable is U-turn speed. Some other variables were also considered, including the posted speed limit and the lane width of the major street. However, adding thes e variables did not
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32 significantly improve the 2 R value of the model. The following Table 5-1 lists the descriptive statistics of dependent and independent variables: Table 5-1 Descriptive Statistics of Dependent and Independent Vari ables for Disaggregate Regression Model Based on the selection of dependent variable and independent variables, an e xponential liner regression model was developed by using SPSS. The following t ables, from Table 5-2 to Table 5-4, show the model summary, ANOVA test and results of regression mode l. Variables Frequency Min. Max. Mean. Std. Deviation. U-turn Speed (MPH) 419 9 20 14 2.33 Number of Vehicles 419 NA NA NA NA (Sedan or Coupe) 235 NA NA NA NA (SUV) 111 NA NA NA NA (Van) 39 NA NA NA NA (Pick-up) 34 NA NA NA NA Turning Radius (FT) 15 43 153 71 29.14 Affected by Right Turn 31 NA NA NA NA
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33 Table 5-2 Summary for Disaggregate Regression Model R R Square Adjust R Square Std. Error of Estimate 0.452 0.204 0.195 0.15485 Table 5-3 ANOVA Test for Disaggregate Regression Model Sum of Squares Df Mean Square F Sig. 2.541 5 0.508 21.194 0.000 9.903 413 0.024 12.444 418 Table 5-4 Statistical Results for Disaggregate Regression Model Parameters Coefficients Std. Error t Sig. VIF Constant 1.787 .094 19.100 0.000 NA SUV -.020 .018 -1.140 .255 1.090 Van -.070 .027 -2.599 .010 1.077 Pick-up -.085 .028 -3.000 .003 1.055 LnRadius .197 .022 8.919 .000 1.020 Affected by -.052 .029 -1.833 .067 1.035 2 R = 0.204, 2 adj R = 0.195 Note: Dependent VariableLnSpeed The regression model has a relatively low R 2 value of 0.204 and an adjusted R 2 value of 0.195. This is because this exponential regression model is a disaggre gate model, so the comparatively low R square is reasonable. The t-statistics show that the selected explanatory variables are all statistically significant a t a 95% level of confidence. The VIF values are close to 1, it means that the collinearity amon g the independent variables
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34 is pretty low. The equation of the U-turn speed model was given as follows: Ln (Speed) = 1.787-0.02SUV-0.07Van-0.085Pick-up+0.197Ln (Radius)-0.052RTimp Also, the equation can be interpreted as: Where: SUV, Van, Pick-up are dummy variables; RTimp = Right Turn Impact, (dummy variable). The U-turn speed model shows that the U-turn speed has a positive rela tionship with turning radius. When turning radius increase, the U-turn speed will incr ease accordingly. At the same time, the model shows that the sedan and coupe vehicles ha ve the highest U-turn speed and the U-turn speed will decrease proportionally if th e turning vehicles are SUV, Van, or Pick-ups. From the regression model equation, it also ind icates that the U-turn speed has a negative relationship with effects of right tur n. Specifically, the U-turn speed will decrease when the turning vehicles are affected by the right turn vehicles from the other side of the approach. And the regression models quantify the v ariation among the U-turn speed and all the independents variables. From the result of the above disaggregate regression model, it can be found that the unstandardized coefficient values of turning radius is the highest in a ll the independent variables. It means turning radius has the most significant effec t to the U-turn speed. Since as discussed in the previous chapters, the major concern betw een U-turning vehicles and left-turning vehicles is turning speed. In the field obs ervation, the left-turning vehicles usually applied a brake suddenly and slow down i n emergency. This phenomenon happens just in couple of seconds, but it can indicate the issu e between the ) 052 .0 085 .0 07.0 02.0 exp( * 97.5 197 .0 RTimp Pickup Van SUV Radius Speed =
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35 U-turn and left-turning vehicles. Therefore, it is necessary t o develop a model to explain the relationship between U-turn speed and turning radius which is the m ost significant parameter to impact the U-turn speed. An aggregate regression model was developed for describing the relationship between U-turn speeds and turning radius. There are two variables in this aggregate regression model. U-turn speeds are the dependent variable and turning radius are independent variable. This regression model fo cuses on how U-turn speed varying under variable turning radius. It includes more details to tell us that how he turning radius effects the U-turn speeds. The sample size of U-turn speeds is 419 collected from 15 signalized intersections which have comparatively high percentages of U-turning vehicles. The following table 5-5 shows the statistical descripti on of sample: Table 5-5 Descriptive Statistics of Dependent and Independent Variable for Aggregate Regression Model Variable Frequency Min. Max. Mean. Std. Deviation. U-turn Speed (MPH) 419 9 20 14 2.33 Turning Radius (FT) 15 43 153 71 29.14 Once the dependent variable and independent variable are determined, the regression model can be run by using SPSS. In this case, the exponential linear regression model was developed since it has a relatively high R square value. Tabl e 5-6 is the summary of the aggregate regression model.
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36 Table 5-6 Summary for Aggregate Regression Model R R Square Adjust R Square Std. Error of Estimate 0.71 0.505 0.467 0.06976 Subsequently, ANOVA test was conducted for analyzing the variance and residuals. The results of AVOVA test is described in the table 5-7. Table 5-7 ANOVA Test for Aggregate Regression Model Sum of Squares df Mean Square F Sig. 0.064 1 0.064 13.249 0.003 0.063 13 0.005 0.128 14 According to the results which were stated, the results of regr ession model can be listed as following: Table 5-8 Statistical Results for Aggregate Regression Model Parameters Coefficients Std. Error t Sig. Constant 1.78 0.226 7.892 0.000 LnRadius 0.195 0.054 3.64 0.003 Notes: Dependent Variable: LnSpeed In the result of aggregate regression model, the t test value of independent is 3.640; meanwhile the significance value is 0.003. These 2 values can indicate that the independent variable LnRadius is highly related to dependent variable L nSpeed. The equation of the regression model can be given as following: Ln(Speed) = 1.780 + 0.195Ln(Radius)
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37 Also the equation can be converted into: 0.195 exp(1.780) SpeedRadius = In this aggregate regression model, the R square and adjusted R square values are 0.505 and 0.467, respectively. Thus, the R square values are satisfied and it means the independent variable can explain the dependent variable at a high level of percentage From the aggregate regression model, it can be found that U-turn speed has a positive relationship with turning radius. In another word, the U-turn speed will increase with the increment of turning radius. The variation has been indicated clearly in the above equations. In the disaggregate regression model, the results can tell us the U-turn speed is effected by some parameters such as type of vehicles, turning r adius, and effect of right turn vehicles. The sedan and coupe vehicles have the highest U-turn s peed comparing to other types of vehicles. It may because the sedan and coupe vehicles ha ve the smallest volume and least torque, but it is just an inference which werenÂ’t verified in this study. Another point in the disaggregate regression model is that the right turn vehicles from the other approach will effect the U-turn speed negatively. In the aggregate regression model, the results focus on explaining t he relationship between U-turn speed and turning radius. The turning radius will impact the U-turn speed positively. The U-turn speed will increase with the increment of turning radius which is provided at a signalized intersection. I have to point out that there probably some other factors will affect the U-turn speed, and the turning radius is not t he only effective parameter. This part of research can be the focus of future work.
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38 5.2 Determination of U-turn Adjustment Factors The determination of a U-turn adjustment factor depends on a number of var iables, including: 1. Whether U-turns are made from exclusive left-turn lanes or shared lanes, 2. The type of phasing (protected, permitted, or protected plus permitted), and 3. The proportion of U-turning vehicles in the left-turn lane. In this study, only the condition in which U-turns being accommodated at a n exclusive left-turn lane with protected signal phasing was considered. As indicated before, vehicles making U-turns have slower turning spee ds than those making left turns. Therefore, U-turning vehicles may cause the f ollowing left-turning vehicles to slow down because of the difference in speeds between these two movements When U-turning vehicles are mixed with left-turning vehicles i n a left-turn traffic stream, the discharging queue will consume more green time than those queues with only left-turning vehicles. Theoretically, the difference increases with the increase in the percentage of U-turning vehicles in the queue. In this study, a regre ssion model was developed to estimate the relationship between the various percentage s of U-turning vehicles in the left-turn lane and the average queue discharge ti me for each turning vehicle. The average queue discharge time for each turning vehicl e was defined as the queue discharge time divided by the number of turning vehicles in the queue as shown in Equation 1: ul T h NN = + (1) Where h = average queue discharge time for each turning vehicle (s);
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39 T = queue discharge time (the time that has elapsed from the time of initiation of the green signal until the time that the rear axle of the las t vehicle in the queue crosses the stop line) (s); u N = the number of U-turning vehicles in the queue; and l N = the number of left-turning vehicles in the queue. The data collected were plotted with the average queue discharge t ime for each turning vehicle as the dependent variable and the various percentages of U-turning vehicles as the independent variable. Several regression models wer e considered, and the regression results were compared. It was found that three diffe rent kinds of regression models were appropriate in describing the relationship, including a s imple linear regression model, a linear regression model with an exponential form, and a linear regression model with a quadratic form (second-degree polynomial re gression model). Statistical analysis found that the second-degree polynomial regre ssion model had the best regression results, for example, the best goodness of fit to the field data. The descriptive statistics are shown in Table 5-6, and the regression results are listed in Tables 5-7 to 5-8. The model is described in Equation 2: 2 0.0000330.0032.1399 UTUThPP =++ (2) Where h is the average queue discharge time for each turning vehicle (s), and UT P is the percentage of U-turning vehicles in the left-turn lane and is calculated as u UT ul N P NN = + (3) On the basis of the regression results, the model was statistic ally significant and the
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40 independent variables were also statistically significant. The a djusted R 2 value was 0.506. The unstandardized residuals were plotted against each independent variabl e. The residual plot for each independent variable was randomly distributed about the x -axis line, which indicated that the model was correctly specified and that the basic assumption about the homogeneous variance was not violated. By considering the interce pt, which represents the average queue discharge time under ideal conditions if it is assumed that no U-turning vehicles were in the left-turn traffic stream, this model provided a reasonable value of 2.14 s. Figure 5-1 Plot of Average Queue Discharge Time Versus Various Percentages of U-turning Vehicles
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41 On the basis of the definition of the adjustment factors for turning movements, the U-turn adjustment factor for the left-turn saturation flow rate c an be estimated by using the following equation: 0 0 2 (3600/) (3600/) 2.1399 0.0000330.0032.1399 UT UTUT h h f hh PP == = ++ (4) Where, UT f = adjustment factor for U-turning movement; h = average queue discharge time for U-turn and left-turn mix flow (s); 0 h = base average queue discharge time for left-turn-only flow (s); UT P = percentage of U-turning vehicles from inside left-turn lane. With Equation 3, the U-turn adjustment factors for various percentages of U-turning vehicles were calculated and are listed in Table 5-9. The data in Table 5-9 show that U-turning vehicles have a considerable effect on the left-turn sat uration flow rate, and the effect increases with the percentage of U-turning vehicles in the left turn lane. For example, the U-turn adjustment factor for the queue with 40% U-turning vehicles is 0.92, which implies an 8% capacity reduction in the left-turn lane. The a djustment factors developed in this study can be directly used to estimate the cap acity reduction in a left-turn lane due to the presence of U-turning vehicles when the s ignalized intersection has only one left-turn lane in the subject approach. When the signalize d intersection has dual left-turn lanes, the adjustment factors can be applied only to adjust the capacity of the inside left-turn lane, considering the fact that U-turns ar e usually accommodated from the inside left-turn lane. The adjustment factors developed in this st udy were compared
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42 with the results of the previous two studies cited in the literatur e review section. As shown in Figure 5-1, the curve of the proposed model generally conforms to but is somewhat lower than that in Adams and HummerÂ’s study ( 4 ). Among those adjustment factors, Tsao and ChuÂ’s study predicts more severe effects than t he other two studies ( 5 ). This finding is not a surprise, because their study was conducted i n Taiwan and the study results may not reflect the behaviors of motor vehicle drivers in the United Stat es Table 5-9 U-Turn Adjustment Factors for Varying Percentages of U-Turning Vehicles UT P (%) 5 10 20 30 40 50 60 70 80 90 100 UT f 0.99 0.98 0.96 0.94 0.92 0.90 0.87 0.84 0.82 0.79 0.76 Table 5-10 Descriptive Statistics for Data Collection in the Field Statistical Parameter h (sec) UT P (%) N Valid. 260 260 Missing. 0 0 Mean. 2.30 30.5 Median. 2.26 22.2 Mode 2.00 .00 Minimum. 1.83 .00 Maximum. 3.37 100
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43 Table 5-11 Regression Results ( 2 R values) for Average Queue Discharge Model R R Square Adjusted R Square Std. Error 0.714 0.510 0.506 0.18425 Table 5-12 Regression Results (ANOVA Test) for Average Queue Discharge Model Result Sum of Squares df Mean Square F Sig. Regression 9.085 2 4.542 133.813 .000 Residual 8.724 257 0.034 Total 17.809 259 Table 5-13 Regression Results (t-statistics) for Average Queue Disch arge Model Unstandardized Coefficients Standardized Coefficients B Std. Error Beta t Sig. Constant 2.140 .021 NA 100.324 .000 2 UT P 3.337E-05 .000 .355 2.480 .014 UT P .0033 .001 .367 2.564 .011
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44 Figure 5-2 Plot of Unstandardized Residuals Versus the Independen t Variable ( P UT) Figure 5-3 Plot of Unstandardized Residuals versus the Independent Variable ( 2 PUT )
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45 5.3 Synchro Simulation The tasks in this part of study are establishing the Synchro sim ulation models, calibrating and validating the models. The purposes of the simulat ion are verifying the correctness of U-turn adjustment factors and conducting a sensitive t est about the relationship between the various percentages of U-turning vehicles and control delay values. In this chapter, first of all, a brief introduction of Synchro simulat ion software package was stated. Subsequently, the contents showed the description of s elected calibrating sites and the data collected from field for model calibration. Finally, the simulation models were run based on the field data for validating the models. From the results of simulation, it can be found that if the U-turn adjustment factors are considered as the initial input in the simulation models, the output control delay will be close to the field value. It demonstrates the correctness of U-turn adjustment factors for evaluating the effects of U-turns on capacity at signalized intersections, and it means U-turning movements can be simulated by adjusting the saturation flow rate according to U-turn adjustment factors. The sensitive test was conducted at last part of this study for testing the sensitivity of the control delay variation with different U-t urn adjustment factors under various U-turn percentages of vehicles. 5.3.1 Introduction of Synchro Simulation Software Package Synchro is simulative software especially for synchronizing the signal timing. It was published by Trafficware Company. And the Simtraffic come with the Synchro s imulation software package. The main function of Simtraffic is simulat ing and analyzing the
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46 signalied intersection. The major output parameters are delay, que ue, capacity, emission, and gas consumption, etc. The Synchro simulation software package can che ck and evaluate the operational conditions at a complicated signalized inte rsection. Basically, the simulations are able to be conducted by Synchro include: Pre-timed signal timing design; Actuated (semi-actuated) signal timing design; Freeway; Roundabout; Different types of vehicles; Pedestrian. Synchro can provide us with enriched output report and detailed evaluation. It offers a lot of helpful information for the traffic practitioners. However, what I want to point out is Synchro follows the algorithm ba sed on HCM when it is simulating the signalized intersection. As discussed a bove, U-turning movements are treated as left turn for estimating the saturat ion flow rate. That actually means Synchro is not able to simulation the operational effects r esult from U-turning movements. Thus, it is necessary to find out a method to simulate t he operational impacts caused by U-turns at asignalized intersections. In this study, t o adjust the saturation flow rate by U-turn adjustment factors is applied for simulating the operational effects of U-turns.
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47 5.3.2 Models Calibration As discussed previously, the control delay value of the subject lane gr oup was considered as the criteria for validating the models. So, another wa ve of field data collection was conducted which focuses on obtaining the field control delay values. Three typical sites were described in the following Table 5-14: Table 5-14 Description of Selected Sites for Measuring Control Delay Signalized Intersection N1 N2 Left Turn Phase Turning Radius (FT) Percentages of U-turning vehicles in Left Turn Lane Bearss Ave @ Florida Ave S 1 P 45 49% Bruce B Downs Blvd @ Highwoods Preserve PKWY S 1 P 72 53% CR 581 (Bruce B Downs Blvd) @ County Line S 1 P 153 41% Notes: N1 = Number of exclusive left turn lanes; N2 = Number of exclusive right turn lanes from other approach of the inter section; P = Protected signal phasing; S = Single. Briefly, the main features of the selected study sites are that the turning radius provided for U-turn range from narrow to wide and the percentages of Uturning vehicles are relatively high. The details about hourly traffic volumes of eve ry lane in each approach, approach lanes configurations, signal timing, free-flow spe ed are collected as well for establishing the simulation models. The field control dela y measurement technique is provided by HCM 2000. The procedure for measuring and computi ng the field control delay has been stated in the previous chapters. The fi eld data and the computation procedures are presented in the following tables:
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48 Table 5-15 Computation Procedure for Control Delay of Site 1 Site 1 Bearss Avenue @ Florida Avenue CF 4 Nc 10 FVS 0.7 FFS (MPH) 45 dad=FVS*CF 2.9 Stopped Vehicles 87 U Percentage 49% Number of Lane 1 Survey Count Interval, Is(s) 15 Total Vehicles Arriving, Vtot 122 Total Vehicles in Queue, Viq 478 Time in Queue Per Vehicle, dvq 53 Control Delay/Vehicle, d=dvq+dad 55.7 No. of Vehicles stopping per lane per cycle 9 Table 5-16 Computation Procedure for Control Delay of Site 2 Site 2. Bruce B Downs Blvd @ Highwoods Preserve PKWY CF 4 Nc 10 FVS 0.7 FFS (MPH) 45 dad=FVS*CF 2.8 Stopped Vehicles 80 U Percentage 53% Number of Lane 1 Survey Count Interval, Is(s) 15 Total Vehicles Arriving, Vtot 116 Total Vehicles in Queue, Viq 444 Time in Queue Per Vehicle, dvq 52 Control Delay/Vehicle, d=dvq+dad 54.4 No. of Vehicles stopping per lane per cycle 8
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49 Table 5-17 Computation Procedure for Control Delay of Site 3 Site 3. Bruce B Downs Blvd @ County Line CF 7 Nc 10 FVS 0.6 FFS (MPH) 45 dad=FVS*CF 4.0 Stopped Vehicles 49 U Percentage 41% Number of Lane 1 Survey Count Interval, Is(s) 15 Total Vehicles Arriving, Vtot 86 Total Vehicles in Queue, Viq 320 Time in Queue Per Vehicle, dvq 50 Control Delay/Vehicle, d=dvq+dad 54.2 No. of Vehicles stopping per lane per cycle 5 5.3.3 Models Validation After calibrating the simulation models, the next step of work is to run the simulation and get the output reports for validating the models. The method for validat ing models which was used in this study it to run the Synchro simulation under al l parameters default and the left turn lane saturation flow rate adjusted according to t he U-turn adjustment factors, respectively. The following Table 5-18 compares the results of the s imulations: Table 5-18 Comparison of Control Delay Comparison of Control Delay (spv) Default Adjusted Calculated Site 1 Bearss Avenue @ Florida Avenue 49.3 55.3 55.7 Site 2 Bruce B Downs Blvd @ Highwoods Preserve 48.5 54.9 54.4 Site 3 Bruce B Downs Blvd @ County Line 46.6 54.2 54.8 Notes: spv = second per vehicle
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50 By the results from Synchro simulation, it can be found that the contr ol delay value is closer to the calculated value after adjusting the saturation fl ow rate of the left lane based on the U-turn adjustment factors. In this test, the results indica te the operational effects were simulated by adjusting the saturation flow rate in the object lane gr oup. Therefore, if evaluation of LOS is conducted by Synchro simulation in the future, mea suring the percentages of U-turning vehicles and adjusting the saturation flow r ate will make the results more accurate. Because conventional Synchro simulation also treated U-turns as left turns for estimating the saturation flow rate and the results do not inclu de the capacity reduction caused by U-turning movements in object lane group. However, t o ignore U-turnsÂ’ impacts will result in errors on evaluating LOS. Althoug h, the results of adjusting saturation flow rate can not be 100% accurate for esti mating the capacity reduction, this method works on reducing the errors on evaluating LOS and make the theoretical values closer to the field real values. 5.3.4 Sensitive Tests At the last part of this study, a sensitive test was conducted f or testing the sensitivity of the control delay values reacting to the adjustments of saturat ion flow rates. The procedure of this test is using the simulation models which have been c alibrated and validated in the previous work and keeping all the conditions unchangeable. And t hen assume that the percentages of vehicles in the object left lane varying from 10% to 100%. Subsequently, run the simulations and get the results. 10 reports were output for each sit e. Figure 5-4, 5-5, 5-6, indicates the overall situation for control de laying values reacting to the variation of U-turning vehicles percentages. From the vary ing trend of control delay
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51 Control Delay VS U-Turn Percentages50 52 54 56 58 60 62 64 66 68 70 0%20%40%60%80%100% U-Turn PercentagesControl Delay(s/v) 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% values under different percentages of U-turning vehicles, it can be roughly observed that the control delay values will increase with augment of U-tur ning vehiclesÂ’ percentages. It can be interpreted as when the number of U-turning vehicles increas es, the saturation flow rate of left turn lane will decrease accordingly. The U-turn adjustment factors which have been presented can be used to quantify this reduction of saturation fl ow rate. Thus, the capacity of the approach will reduce due to the decrease of saturation flow rate. The capacity of approach will directly affect on evaluating the LOS of a signalized intersection. Figure 5-4 Trend of Control Delay Variation under Different Percenta ges of U-turning vehicles for Site 1
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52 Control Delauy VS U-Turn Percentages 50 52 54 56 58 60 62 0%20%40%60%80%100% U-Turn PercentagesControl Delay (s/v) 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Control Delay VS U-Turn Percentages 50 52 54 56 58 60 62 64 66 68 70 0%20%40%60%80%100% U-Turn PercentagesControl Delay (s/v) 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Figure 5-5 Trend of Control Delay Variation under Different Percenta ges of U-turning vehicles for Site 2 Figure 5-6 Trend of Control Delay Variation under Different Per centages of U-turning vehicles for Site 3
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53 From the figures above, the trend of control delay under variable perc entages of U-turning vehicles can be indicated. But in the figure, only the var ying trends are shown up. By observing the figures above, several preliminary summaries can be obtaine d. 1: The control delay of the U-turn and left turn mixed lane at a s ignalized intersection will increase with the increment of the percentages of U-turning vehicles. 2: For each 10% variation of the U-turning vehicles, the value of c ontrol delay is about to increase 1.5s, accordingly. But this is just a rough inference based on the figures, more detai ls can be found in the following tables which list the values of control delay and U-tur n percentages. The following Table5-19, 5-20, 5-21, show the exact values of the sensitive tests: Table 5-19 Summary of Sensitive Test for Site 1 Site 1 Bearss Avenue @ Florida Avenue U Turning Vehicles Percentage Control Delay (spv) 10% 51.3 20% 52.3 30% 53.4 40% 54.2 50% 55.3 60% 57.1 70% 59.1 80% 60.5 90% 61.2 100% 64.2
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54 Table 5-20 Summary of Sensitive Test for Site 2 Site 2 Bruce B Downs Blvd @ Highwoods Preserve U Turning Vehicles Percentages Control Delay (spv) 10% 51.9 20% 52.5 30% 53.3 40% 54.2 50% 54.9 60% 56.2 70% 57.2 80% 58.2 90% 59.4 100% 61.1
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55 Table 5-21 Summary of Sensitive Test for Site 3 Site 3 Bruce B Downs Blvd @ County Line U Turning Vehicles Percentages Control Delay (spv) 10% 50.2 20% 51.5 30% 52.9 40% 54.8 50% 56.3 60% 58.8 70% 61.8 80% 63.5 90% 66.3 100% 70 Notes: spv = seconds per vehicle The objective of sensitive tests is quantifying the variation of c ontrol delay under varying percentage of U-turning vehicles. It indicates the sensitivity of varying percentages of U-turning vehicles to the LOS of a signalized intersection.
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56 CHAPTER 6 SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 6.1 Summary This study is composed by three major parts. In the first par t, the exponential linear regression model was developed to describe the relationship among the U-turn speed and some other external various factors. The factors which significant ly related to U-turn speed were indicated in the results of the regression model. And the m odel quantities the effects to U-turn speed. In the second part, the U-turn adjustment fac tors under various percentages of U-turning vehicles were determined by the quadrat ic regression model. The results of this part of study can be directly used for estim ating the saturation flow rate of a U-turn and left turn mixed lane. Furthermore, it can be us ed for estimating the reduction of capacity at a signalized intersection and evaluating the LOS. The last part of this study is verifying the correctness of U-turn adjustment fa ctors. The procedure includes calibrating models, validating models, and running the models. The results show that inputting the U-turn adjustment factors for adjusting the satura tion flow rate of a subject lane or lane group can make the results of simulation more a ccurate. A sensitive test was also conducted. The objective of the sensitive analysis i s to quantify the impacts of various percentages of U-turning vehicles on saturation flow rat e and reduction of capacity.
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57 6.2 Conclusions As a result of this research, the following conclusions can be made: 1. U-turning vehicles adversely affect the capacities of signa lized intersections; and the effect increases with the increase of percentages of U-turning vehicles in the left-turn lane. 2. When left-turning vehicles are mixed with U-turning vehicles in the left-turn traffic stream, the discharge flow rate does not display an easily ident ifiable steady maximum rate. Therefore, the traditional headway method, which measures the s aturation headway of U-turning vehicles and left-turning vehicles in the field, may not be suitable for estimation of the effects of U-turning vehicles on the left-turn traffic s tream. 3. U-turning vehicles consume more of the available green tim e and more of the laneÂ’s available capacity than left-turning vehicles. In addition, U-turni ng vehicles cause the following left-turning vehicles to slow down to avoid a rear-end colli sion. The extra time required by the queue to be discharged because of the presence of va rious percentages of U-turning vehicles can be quantified by use of the regression model developed in this study. 4. When the capacity of a signalized intersection is estimated, i t is essential to account for the capacity reduction due to the presence of U-turning vehicles, e specially when the percentage of U-tuning vehicles is relatively high (>40%). The eff ect can be quantified by applying the adjustment factors developed in this study.
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58 6.3 Practical Meaning of the Study As summarized in the previous contents, this study consists three major parts. The first part developed exponential linear regression models to identi fy the factors which affect the U-turn speed. From this segment of results, it can be found that the turning radius has a significant effect on U-turn speed. The U-turn speed i ncreases with the increase of turning radius. Thus, if longer turning radius is provided for the U-turning vehicle, the U-turn speed will be higher. It may reduce the possibil ity of rear-end crash between U-turning vehicles and left turning vehicles. Furthermor e, the results of the first part may offer some useful suggestions for traffic practitioner and roadway de signer. The second part focuses on presenting a method for estimating the r eduction of saturation flow rate due to U-turning movement. The method achieved by developing U-turn adjustment factors. The results of this part show varying U-turn adj ustment factors under various percentages of U-turning vehicles which change from 5% to 100% in the left turn lane. From the U-turn adjustment factors, it can be found tha t the reduction of saturation flow rate increases with increase of U-turning vehic les. The developed U-turn adjustment factors can be directly used to estimate the capacit y reduction due to the presence of various percentages of U-turning vehicles at a si gnalized intersection. This is the meaning of developing the U-turn adjustment factors. The third part of this study is Synchro simulation. First of al l, Synchro simulation software is the most widely used tool in the traffic industrial field. A lot of transportation consulting companies use Synchro to evaluate the Level of Service at intersections. Also, Synchro simulation software is especially used for signalized intersections. Simulating
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59 signalized intersections is the advantage of Synchro simulation sof tware comparing to other traffic simulation software. But the algorithm in Synchro f ollows by the Highway Capacity Manual. That causes the problems. As is discussed in the previous chapters, U-turns are treated as left turns for estimation of satura tion flow rate. However, the operational effects of U-turns and left turns are different. There fore, the results from Synchro simulation do not take the operational effects due to U-turns into consideration. ThatÂ’s why the control delay values output from Synchro without using U-turn adjustment factors do not match the measured control delay value. From the results of part three in this study, it can be found that the output control delay values are closer to field data if using U-turn adjustment factors to adjust the satur ation flow rate in exclusive left lane. This phenomenon indicates using U-turn adjustment factors will improve the accuracy of simulation. It may be a new, feasible, and reasona ble method for simulating the operational performance at signalized intersections and make i t more accurate. The meaning of this segment of research is that the results may be directly applied in the traffic industrial field as a useful method to improve performance of Synchro si mulation. 6.4 Limitations Note that the adjustment factors in this study were developed under some simplified conditions. The simplified conditions include 1. Vehicles make left turns and U-turns from an exclusive left-turn lane; 2. Vehicles make left turns and U-turns under a protected signal phase; 3. The street segment has enough of a turning radius to accommodate U-turns; 4. All the turning vehicles are passenger cars and there are no comm ercial vehicles in the
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60 left turn lane. 5. There is just minor disturbance from the right-turning vehicles duri ng the U-turn phase in the other approach of the intersection. 6. All the field data collection is conducted in urban area. So, the condi tion of rural area is not taken into consideration. 6.5 Discussion and Recommendation In this segment, three major concerns need to be pointed out and discussed: 1. Vehicle type 2. Study area, and 3. Disturbance by right-turning vehicles In this study, all the turning speeds were measured from passeng er cars. However, the features of commercial vehicles are different from those of pas senger cars. The commercial vehicles have larger volume and longer torque comparing t o passenger cars. In this study, the turning radius is enough to accommodate U-turning p assenger cars. But the turning radius of street segment may be not enough for the comme rcial vehicles. It can result in the commercial vehicles unable to make U-turns or havi ng U-turns in low speed. Obviously, this situation will cause more traffic problems. Another concern is the research area in this study is urban area All the selected sites are urban arterials or urban highways. However, the operational spe ed in urban area is different from that in rural area. Usually, the operational s peed is higher in rural area. Since the operational speed is an important parameter which needs to be input for calibrating the simulation model, the results of this study may not be applied to rura l area.
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61 The third concern is that U-turning vehicles only have minor disturbance by the right turning vehicles in this study. However, in most cases, the right tur ns from the other approach do not have protected phase. So, the right turns usually have impa cts on U-turning vehicles. If considering the disturbance from right turnin g vehicles during the U-turn phase in the other approach of the intersection, the saturation f low rate of left turn lane will decrease and the control delay of the intersection will increas e. But in this study, the impact from right turning vehicles was barely taken into consideration. Based on the limitations above, the future study can focus on enlargi ng sample size, bringing more types of vehicles into consideration, especially comm ercial vehicles, extending the study area to rural area, and considering the effec ts cause by right turning vehicles. In additional, Several issues were not addressed in this study, including the impa cts of U-turning vehicles on the start-up lost time and clearance lost time, the impacts of U-turning heavy vehicles on the capacities of signalized intersect ions, the impacts of U-turning vehicles under restrictive geometric conditions, and the impa cts of U-turning vehicles with significant disturbance from right-turning vehicles i n the other approach Further study should focus on these issues. Meanwhile, this study was conducted in central Florida. Validation of the model in other regions may prove useful.
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62 REFERENCES Sokolow, G. H. Practical Considerations for Beginning a Comprehensive Access Management Program. Proc., First National Access Management C onference (CD-ROM), Vail, Colo., 1993. Highway Capacity Manual. TRB, National Research Council, Washington, D.C., 2000. Roess, R. P., W. R. McShane and E. S. Prassas. Traffic Engineering, 2nd ed. Prentice Hall, Upper Saddle River, N.J., 1998. Adams, J. C., and J. E. Hummer. Effects of U-Turns on Left-Turn Sat uration Flow Rates. In Transportation Research Record 1398, TRB, National Research Council, W ashington, D.C., 1993, pp. 90Â–100. Tsao, S., and S. Chu. A Study on Adjustment Factors for U-Turns in Lef tTurn Lanes at Signalized Intersections. Journal of Advanced Transportation, Vol. 29, No. 2, 1996, pp. 183Â–192. Bonneson, James Allen (1992
). Â“Study of Headway and Lost Time at Single-Point Urban Interchanges.Â” Transportation Research Record 1365, TRB, National R esearch Council, Washington D.C., 1992. HCM (2000). Â“Highway Capacity Manual (2000).Â” Transportation Resear ch Board, TRB Special Report, Washington D.C., 2000. Niittymaeki, Jarkko and Pursula, Matti (1997). Â“Saturation flow at si gnal-groupcontrolled traffic signals.Â” Transportation Research Record 1572, Na tional Research Council, Washington D.C., 1997. Shantaeu, Robert M., (1998). Â“Using cumulative curve to measure saturation flow and lost time.Â” ITE journal, 10 1998. Self, D. R. Comparison of Crashes on Median-Divided and Five-Lane Roadw ays in Charlotte, North Carolina. Proc., 2 nd Urban Street Symposium, Anaheim, Calif., TRB, National Research Council, 2003. Phillips, S. Empirical Collision Model for Four-Lane Median Divided a nd Five-Lane with TWLTL Segments. M.S. thesis. North Carolina State University, Ralei gh, May 2004. Roess, R.P., W. R. Mcshane and E.S. Prassas. Traffic Engineering 2 nd ed. Prentice Hall, Upper Saddle River, N. J., 1998.
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63 Reid, J. Unconventional Arterial Intersection Design. Management, and O perations Strategies. Parsons Brinckerhoff, Inc., ITE, Washington, D.C., 2003, pp. 25-40. Thakkar, J., V. Reddy, M. Hadi, and F. Vargas. A Methodology to Evaluate the Impacts of Prohaibiting Median Opening Movements. Proc., 4 th National Access Management Conference, Portland, Ore., TRB, National Research Council, Washington, D .C., Aug, 2000. Xu, L. Right Turns Followed by U-turns Versus Direct Left Turns: A Comp arison of Safty Issues. ITE Journal. Vol. 71, No. 11, Nov. 2001, pp. 36-43. Dissanayake, S., J. Lu, N. Castillo, and P. Yi. Should Direct Left Tu rns from Driveways Be Avoided? A Safety Perspective. ITE Journal, Vol. 72. No. 6, June 2002, pp. 26-29. Institute of Transportation Engineers (1999). Traffic Engineering H andbook. 5 th Edition, pp. 306-347. Preston, H., Keltner, D., Newton, R. and Albrecht, C. (1998). Statistical Relationship between Vehicular Crashes and Highway Access, Final Report 1988-27, M innesota Department of Transportation, Office of Research Service, Minneapolis, Minnes ota. Bonneson, J.A and McCoy, P.T. (March 1998). Median Treatment selection for Existing Arterial Streets, Institute of Transportation Engineers Journal, pp. 26-34. Squires, C.A., and Parsonson, P.S. (1989). Â“Accident Comparison of Raised Median and Two-way Left-turn Lane Median TreatmentsÂ”, Transportation Rese arch Record 1239, Transportation Research Board, Washington, D.C. Parker, M.R. (1983). Design Guidelines for Raised and Traversable Med ians in Urban Areas, Virginia Highway and Transportation Research Council, Charlottesvil le, Virginia. Gluck, J., Levinson, H.S. and Stover, V.G. (1999). Impacts of Access Manage ment Techiques, Natioonal Cooperative Highway Research Program Report 420, Transportation Research Board, National Research Council, Washington, DC. Benekohal, R.F. (1991). Method for Estimating Accident Reduction From Highw ay Improvements. ITE 1991 compendium of technical papers, pp 419-423. Gattis, J.L. (1996) Â“Comparison of Delay and Accidents on Three Roadwa y Accident Design in A Small City, 1996 National Access Management Conferen ce, Vail, Colorado, pp 269-281. Himes, W. W. and Montgomery, DC., (1990) Â“Probablity and Statistics in E ngineering and Management ScienceÂ”, 3 rd edition, John Wiley and Sons, Inc., New York. Resende, P. and Benekohal, R.F. (1997), Â“Effects of Roadway Section Lengt h on
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64 Accident Modeling,Â” Challenges, Innovations and Opportunities: Proceedings on Traffic Congestion and Traffic Safety in The 21 st Century, New York, pp. 403-409.
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65 APPENDIX DESCRIPTIVE FIELD DATA OF U-TURN SPEED Table A-1 Descriptive U-turn Speed Data of Bruce B Downs Blvd @ Commerc e Palms Blvd Passenger Car (MPH) SUV (MPH) Van (MPH) Pick-up (MPH) Turning Radius (FEET) 15 9 12 10 64 8 17 11 9 14 18 10 15 15 12 12 14 12 12 15 13 15 13 14 13 14 14 9 13 11 14 14 12 11 13 10 16 8 16 11 Bruce B Downs Blvd @ Commerce Palms Blvd 10 Average Speed 13 13 11 11
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66 Table A-2 Descriptive U-turn Speed Data of Fowler Ave @ 56th Street Passenger Car (MPH) SUV (MPH) Van (MPH) Pick-up (MPH) Turning Radius (FEET) 15 11 10 14 43 17 16 10 10 13 12 11 12 12 11 18 14 14 17 15 13 15 10 14 16 12 14 13 10 11 Fowler Ave @ 56th Street 14 Average Speed 13 14 11 14
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67 Table A-3 Descriptive U-turn Speed Data of Bruce B Downs Blvd@CrossCr eek Blvd Passenger Car (MPH) SUV (MPH) Van (MPH) Pick-up (MPH) Turning Radius (FEET) 18 14 15 12 78 16 12 14 20 18 15 16 18 13 17 14 16 16 20 20 19 20 16 16 15 13 17 14 14 Bruce B Downs Blvd @ Cross Creek Blvd 13 Average Speed 17 15 15 12
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68 Table A-4 Descriptive U-turn Speed Data of Bearss Ave @ Florida Ave Passenger Car (MPH) SUV (MPH) Van (MPH) Pick-up (MPH) Turning Radius (FEET) 11 12 10 11 45 12 11 11 11 12 11 12 11 11 12 11 11 13 11 10 12 10 10 11 11 12 13 11 11 10 14 10 13 12 11 10 14 9 Bearss Ave @ Florida Ave 12 Average Speed 12 11 11 11
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69 Table A-5 Descriptive U-turn Speed Data of Bruce B Downs Blvd @ Highwoods Preserve PKWY Passenger Car (MPH) SUV (MPH) Van (MPH) Pick-up (MPH) Turning Radius (FEET) 18 14 12 20 72 16 15 13 13 14 17 14 18 17 18 11 16 13 17 16 17 18 19 16 18 16 15 14 15 18 Bruce B Downs Blvd @ Highwoods Preserve PKWY 9 Average Speed 16 15 15 20
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70 Table A-6 Descriptive U-turn Speed Data of CR 581 (Bruce B Downs Blvd) @ County Line Passenger Car (MPH) SUV (MPH) Van (MPH) Pick-up (MPH) Turning Radius (FEET) 19 15 16 12 153 17 17 15 13 16 16 15 14 16 16 15 12 18 15 13 13 14 15 14 15 17 18 14 15 16 CR 581 (Bruce B Downs Blvd) @ County Line 15 Average Speed 15 15 16 15
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71 Table A-7 Descriptive U-turn Speed Data of Dale Mabry HWY @ Fletcher Ave Passenger Car (MPH) SUV (MPH) Van (MPH) Pick-up (MPH) Turning Radius (FEET) 15 18 12 16 116 15 12 15 16 15 14 15 11 8 13 12 16 16 10 16 14 17 13 17 16 15 16 14 14 15 Dale Mabry HWY @ Fletcher Ave 12 Average Speed 14 14 14 16
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72 Table A-8 Descriptive U-turn Speed Data of Dale Mabry HWY @ Stall Rd Passenger Car (MPH) SUV (MPH) Van (MPH) Pick-up (MPH) Turning Radius (FEET) 15 12 12 15 72 11 15 10 16 14 17 17 15 12 15 12 17 17 16 15 15 16 13 13 12 15 16 16 14 13 Dale Mabry HWY @ Stall Rd 16 Average Speed 15 14 12 13
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73 Table A-9 Descriptive U-turn Speed Data of Waters Ave @ Dale Mabry HWY Passenger Car (MPH) SUV (MPH) Van (MPH) Pick-up (MPH) Turning Radius (FEET) 11 12 14 59 12 14 15 11 13 13 13 15 13 14 10 12 14 13 14 11 12 Waters Ave @ Dale Mabry HWY 15 Average Speed 13 13 14
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74 Table A-10 Descriptive U-turn Speed Data of Dale Mabry HWY @ Waters Ave Passenger Car (MPH) SUV (MPH) Van (MPH) Pick-up (MPH) Turning Radius (FEET) 16 14 11 11 66 16 16 12 13 16 12 14 10 12 10 12 12 13 12 14 12 16 15 Dale Mabry HWY @ Waters Ave 17 Average Speed 13 15 11 12
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75 Table A-11 Descriptive U-turn Speed Data of Dale Mabry HWY @ Mapledale Bl vd Passenger Car (MPH) SUV (MPH) Van (MPH) Pick-up (MPH) Turning Radius (FEET) 12 13 11 15 68 12 16 15 15 15 14 11 11 11 12 14 13 12 12 12 18 13 13 Dale Mabry HWY @ Mapledale Blvd 12 Average Speed 13 14 13 15
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76 Table A-12 Descriptive U-turn Speed Data of Dale Mabry HWY @ Bearss Ave( Ehrlich Ave) Passenger Car (MPH) SUV (MPH) Van (MPH) Pick-up (MPH) Turning Radius (FEET) 14 13 10 43 11 14 10 12 12 11 14 14 13 15 16 13 11 12 10 10 13 13 12 14 10 15 Dale Mabry HWY @ Bearss Ave( Ehrlich Ave) 12 Average Speed 13 13 11
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77 Table A-13 Descriptive U-turn Speed Data of Dale Mabry HWY @ Carrollwood SPGS Passenger Car (MPH) SUV (MPH) Van (MPH) Pick-up (MPH) Turning Radius (FEET) 11 14 11 11 73 15 13 17 13 12 16 14 15 15 13 16 14 10 15 15 10 13 9 13 13 Dale Mabry HWY @ Carrollwood SPGS 15 Average Speed 13 14 11 14
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78 Table A-14 Descriptive U-turn Speed Data of Hillsborough Ave @ Armeni a Ave Passenger Car (MPH) SUV (MPH) Van (MPH) Pick-up (MPH) Turning Radius (FEET) 13 12 12 14 53 11 13 11 14 11 11 12 13 10 12 13 13 12 13 13 11 13 Hillsborogh Ave @ Armenia Ave 12 Average Speed 12 12 12 13
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79 Table A-15 Descriptive U-turn Speed Data of Hillsborough Ave @ Lois Ave Passenger Car (MPH) SUV (MPH) Van (MPH) Pick-up (MPH) Turning Radius (FEET) 14 13 12 12 54 13 12 13 15 13 10 8 15 15 10 11 12 13 13 14 12 13 15 13 12 Hillsborogh Ave @ Lois Ave 12 Average Speed 13 12 13 12 71