UNIVERSITY OF CALGARY. Suman Mishra

Transcription

1 UNIVERSITY OF CALGARY Evaluating the Impacts of Existing Priority and Developing a Passenger Based Transit Signal Priority by Suman Mishra A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTERS OF SCIENCE GRADUATE PROGRAM IN CIVIL ENGINEERING CALGARY, ALBERTA APRIL, 2016 Suman Mishra 2016

2 Abstract Traffic congestion has been rapidly increasing in developing and developed countries. It leads to an increase in travel time and a loss in reliability. This research focuses on providing priority to buses and reducing the delay on side streets as well. Priority is given to buses based on the number of passengers on the bus. A survey is also conducted to determine the perception of transit users along the Centre street corridor in Calgary regarding the existing priority measures on the corridor. The results showed that there is a decrease in travel time and side street delay with the application of priority based on a number of passengers on the buses compared to the existing priority measures on the corridor. From the survey results, a multinomial logistic regression model was developed. The multinomial logistic regression model showed that respondent s encouragement to use transit because of the existing priority measures. ii

3 Acknowledgement I am highly obliged to my supervisors Dr. Lina Kattan and Dr. S.C Wirasinghe for their assistance, relentless support and continuous encouragement throughout the Master s program and the development of this thesis. I would like to thank the University of Calgary, NSERC-IPS, and Calgary Transit for providing financial support throughout the study. I would like to thank Neil McKendrick, Manager, Operational Transit Planning, Calgary Transit and Muhammad Arslan Asim, Transit Priority Engineer, Transit Planning/ Design & Special Projects, The City of Calgary, for their guidance during the study and for providing office space at the Calgary Transit office. I would also like to acknowledge the cooperation of the staff of Calgary Transit, City of Calgary. I am thankful to all my colleagues for their support and encouragement throughout my time at the University of Calgary. Finally, I would like to thank my family and friends, without their support, the study would not have been possible. iii

4 Table of Contents TITLE Abstract Acknowledgement Table of Contents List of Tables List of Figures List of Abbreviations PAGE ii iii iv vi vii viii 1. INTRODUCTION Background Research objective, scope and contribution Thesis organization 5 2. LITERATURE REVIEW Introduction Need and Objective of Transit Priority Measures Differences between Priority and Preemption Transit Signal Priority Control Strategies Components of TSP system Vehicle detection Priority Request Generator Priority Request Server Communication system Review of the literature on TSP Analytical studies for delay calculation for green extension and red truncation approaches: Field and Simulation Studies Different TSP approaches Adaptive Signal Control Strategies Passengers Perceptions towards bus service attributes Summary TRANSIT PRIORITY LOGIC Red truncation [Early green] Green extension Finding the number of passengers required on the bus to get priority CALIBRATION OF VISSIM MODEL VISSIM as the simulation software Study Area Modeling Process Data Collection Road Geometry Signal Timing data Traffic Volume data Transit data 47 iv

5 4.4.5 Bus stops, queue jump, reversible lanes, and bus only lanes Base Network coding Calibration and Validation of the Model Determining required number of simulation runs Validation of the model RESULTS AND DISCUSSION Simulation Scenarios Measures of effectiveness Results TRANSIT PRIORITY MEASURES ALONG CENTRE STREET NORTH: PASSENGER SURVEY Background Data collection and Research methodology Descriptive Statistics Results Demographics Important decision to ride public transit Comparison of survey attributes Rating of Transit priority measures Awareness of priority on Center Street TPM as providing incentive to use public transit more often Multinomial Logistic Regression CONCLUSION AND FUTURE RECOMMENDATION REFERENCES 93 v

6 List of Tables Figure Title Page Table 2-1 Queue dissipation cases (Source: Abdy and Hellinga,2011) 12 Table 4-1: List of Signalized Intersections 44 Table 4-2: Bus travel times 48 Table 4-3: Preliminary results of VISSIM model 54 Table 4-4: Mean and variance of travel time for the uncalibrated model 54 Table 4-5: Determination of the number of runs required for the model 55 Table 4-6: Range of VISSIM parameters 57 Table 4-7: Trial and error calibration of the car following model 58 Table 4-8: t-test 59 Table 4-9: Comparison of field and simulation travel time 60 Table 5-1: Travel time and its variance for different scenarios 63 Table 6-1: Demographics of the sample 74 Table 6-2: Student t-test to compare attributes with other routes 77 Table 6-3: Descriptive statistics for priority ratings 79 Table 6-4: Student t-test for pairwise comparisons of priority measures 80 Table 6-5: Awareness of priority 81 Table 6-6: Description of variables 84 Table 6-7: Parameter estimates of the model 86 vi

7 List of Figures Figure Title Page Figure 1-1: Queue jump (Source: Transport Canada web) 2 Figure 1-2: Bus only crossing (source: Jordan, kajdy, Mckendrick, 2010) 3 Figure 2-1 Impact of red truncation in the non-prioritized approach (Liu et al, 2008) 11 Figure 2-2: Impact of red truncation in the non-prioritized approach (Source: Abdy and Hellinga, 2001) 12 Figure 2-3: Structure of coordinated TSP system in a connected vehicle environment (Source: Hao et.al, 2014) 23 Figure 3-1: Prioritized approach (Red truncation) 30 Figure 3-2: Non-Prioritized approach (Red truncation) 31 Figure 3-3: Prioritized approach (Green extension) 32 Figure 3-4: Non-Prioritized approach (Green extension) 33 Figure 3-5: Flowchart for transit priority logic 39 Figure 4-1: Geographic location of study area (Source: google maps) 41 Figure 4-2: Study area showing major intersections (Source: Calgary Transit web). 42 Figure 4-3: Bus only lanes between 54 Avenue and McKnight Blvd (Source: Calgary Transit web). 43 Figure 4-4: Bus only lanes between McKnight Blvd and 43 Avenue (Source: Calgary Transit web). 43 Figure 4-5: Modeling process in VISSIM 45 Figure 4-6: A section of the VISSIM model network 49 Figure 4-7: Network in SYNCHRO 50 Figure 4-8:VISSIM network showing transit stops and transit lines transit priority logic 51 Figure 5-1: Side street delay at different sections 64 Figure 5-2: Major street delay at different sections 65 Figure 5-3: Average delay per person 66 Figure 5-4: Average speed of buses in the network 68 Figure 5-5: Average numbers of stops for buses in the network 69 Figure 5-6: Average delay per vehicle (bus) in the network 70 Figure 6-1: Important decision to ride public transit 75 Figure 6-2: Comparison of perceived measures of effectiveness with other routes 76 Figure 6-3: Average ratings for different priority measures 78 Figure 6-4: Encouragement to use transit more 81 vii

8 List of Abbreviations ITE US TTI NYC TSP TPM HOV FTA AVL APC Institute of Transportation Engineers United States of America Texas Transportation Institute New York City Transit Signal Priority Transit Priority Measures High Occupancy Vehicle Federal Transit Administration Automatic Vehicle Location Automatic Passenger Count viii

9 1. Introduction 1.1 Background Today, most large cities face traffic congestion, which leads to unreliable and inefficient transit systems. Traffic congestion negatively impacts transit as it causes travel time and delays to significantly increase. One of the cost efficient ways of improving transit service is to implement transit priority measures (TPM). TPM are traffic operation strategies, which include transit signal priority (TSP), bus-only lanes, bus only crossings, HOV lanes, and queue jumpers, that enhance transit attractiveness and competitiveness with respect to automobiles. Interest in transit signal priority (TSP) dates back to the 1970s mainly in Europe and United States. TSP is an operational improvement on signal timings that adjusts the signal timing to reduce delays for public transit. The mechanism of those measures is to extend green lights or shorten red lights to allow transit vehicles to bypass existing or anticipated congested areas and mitigate the delays caused by traffic signals. Providing TSP at major intersections results in improved bus travel times and reliability. TSP measures are used either separately or in combination with other transit control strategies, such as bus only lanes, queue jumps, reversible lanes and HOV lanes. Most of the TSP applications are implemented in areas with heavy traffic and heavy ridership (Ova, 2001). The impact of TSP varies depending on the surrounding traffic situation (Ngan, 2004). TSP strategies are generally associated with positive impacts for priority movements and negative impacts on non-priority movements. According to the US Federal Department of Transportation, the benefits of using TSP include reduced transit travel times, improved adherence to schedules, and efficient transit and road 1

10 networks, which measure a person s mobility (i.e. passenger s use of the mode). TSP helps to increase rider comfort as well and may increase ridership. The main disadvantage is increased delays for a non-priority approach. Queue jump is a short stretch of a roadway designed for transit vehicles to bypass a queue at signalized intersection. A queue jump lane is usually accompanied by a special queue jumper phase that allows a vehicle in the queue jump lane to move ahead of other queued vehicles and, therefore, the vehicle can merge into the regular travel lane earlier than the general traffic (Reza, 2012). Figure 1-1: Queue jump (Source: Transport Canada web) High occupancy vehicle (HOV) lanes are restricted-use roadway lanes reserved for vehicles with more than a predetermined number of occupants (Menendez and Daganzo, 2007). The benefits of providing high occupancy vehicle (HOV) lanes are: encouraging people to shift from automobiles with one or two occupants to carpools or buses, reducing vehicle trips, and thereby reducing congestion and air pollution (Dahlgren, 1998). 2

11 Due to the existence of mixed traffic, it is difficult for buses to easily maneuver, for example, to find gaps to change lanes after picking up passengers or dropping passengers at transit stops. Reserved bus lanes on major urban roads are provided to allow buses to move quickly and smoothly. Bus only lanes are lanes restricted to buses on certain days and times. Providing such exclusive bus lane makes transit modes more attractive (Arasan, et al 2010). Bus only crossings are traps made of a metal grate placed over a ditch or depression in the road with tines spaced far enough apart that small vehicles fall in between the tines but close enough for buses to pass. Figure 1-2: Bus only crossing (source: Jordan, kajdy, Mckendrick, 2010) Lane reversal is a lane management technique in which the direction of several lanes is reversed during different time periods of a day to increase the capacity of flow in one direction. 1.2 Research objective, scope and contribution The objective of this research is threefold: 1. Evaluate TPM based on microsimulation and using transit customers perspective. 2. Calibrate a transit corridor with various TPM. 3

12 3. Develop and evaluate a new passenger-based TSP strategy and conduct a comparative evaluation of various scenarios: no priority, unconditional priority, existing priority, and passenger-based priority. A significant body of research has been previously conducted to evaluate TPM using analytical models, simulations, and field studies. This study evaluates existing TPM in a microsimulation environment. In addition, it examines riders perceptions of TPM based on a revealed preference survey. The survey collected various information on trip characteristics, socioeconomic characteristics, and frequency of transit use. Respondents were asked to evaluate the perceived improvements resulting from the implementation of the TPM along the examined study corridor. A multinomial logistic model was developed to examine the factors that encourage riders to use public transit more often as a result of Transit TPM. The outcomes of the survey and model provided valuable recommendations. For the second part of this thesis, a microsimulation model of the study corridor is calibrated. Microscopic simulation is able to describe individual behaviors of a vehicle in detail, which is convenient for analyzing the behavior of buses. VISSIM was used to calibrate the existing TMP on the transit corridor. Extensive traffic data (e.g. traffic counts, traffic turning counts, and INRIX data), automatic passenger counts (APC), and transit GPS data were used to calibrate the model to closely reflect the real network. The third part of this research focuses on developing a new TSP measure that can improve transit performance without causing unnecessary delays to crossing traffic. Thus, a conditional TSP that grants priority based on schedule adherence and passenger occupancy is developed. This passenger-based TSP is evaluated using VISSIM and compared to three other control types: no priority, existing priority, and unconditional priority. 4

13 This study considers the Center street corridor in the City of Calgary as a case study in which a combination of different types of TPM are implemented and consist of: transit signal priority (TSP), lane reversals, HOV lanes, and queue jumpers. The current TSP used is a passive priority that provides unconditional priority every three minutes to buses on the corridor. As this priority is granted whether the transit is on schedule or not, it might have an adverse effect on crossing traffic and transit vehicles. An evaluation of transit travel times and the variance of travel times for both through and crossing transit buses due to TPM in different scenarios is necessary. 1.3 Thesis organization This thesis consists of seven chapters. The following chapter reviews the previous research conducted in the field of bus priority and the impacts on both crossing and through traffic. The results obtained from both field and simulation approaches are discussed. Chapter 3 discusses the transit priority logic based on the number of passengers on the bus. Chapter 4 discusses the research methodology, the development of a simulation model in VISSIM, and the calibration of the model. Chapter 5 discusses the results of the VISSIM model for the considered scenarios. Chapter 6 discusses the background of the survey, data collection, survey methodology, and results of the survey. Chapter 7 presents the conclusions and recommendations for future research. 5

14 2. Literature Review 2.1 Introduction There is a large body of research that examines the impact of TPM on transit reliability. This chapter provides a review of various TPM with the main focus on TSP, bus lanes only, and queue jumps. It also reviews the findings of the previous TPM related studies. 2.2 Need and Objective of Transit Priority Measures Today, most of the big cities face the problem of traffic congestion, which leads to an unreliable and inefficient transit system. TPM are techniques that help to improve transit service and decrease passenger and operating costs. TPM provide a cost effective method to improve transit performance, especially at signalized intersections and in regards to maintaining schedule adherence. The main objectives of TPM are as follows: Maintaining schedule adherence Increasing the efficiency of transit vehicles and the overall network Reducing delay times The system should have a minimal impact on other facility users including crossing traffic and transit vehicles on side streets (Deshpande, 2003). In addition, a priority system shall be part of a larger ITS system that includes improved rider information and other services (Chang et al., 2003). 6

15 2.3 Differences between Priority and Preemption The literature on bus priority and emergency vehicle preemption is extensive. In some studies, preemption is viewed as a special case of priority; thus, the terms priority and preemption are sometimes used interchangeably (Teng et.al, 2003). Preemption uses a special signal timing plan that requires the traffic signal controller to transition out of and back into the coordinated operation of a normal signal timing plan (Deshpande, 2003). Preemption is traditionally used at railroad crossings and at signalized intersections for emergency vehicles where a high degree of priority is warranted for safety and performance reasons (Deshpande, 2003). Owing to the importance of providing safety for emergency vehicles and the urgency of response to individuals in need, granting preemption typically has minimal restrictions and acts directly on the controllers preempt input (Chang, 2002). Unlike preemption, priority attempts to improve transit service quality and efficiency (Chang, 2002). TSP is meant to provide priority service to reduce delay times for transit vehicles while minimally affecting other traffic by coordinating the operation of traffic signals (Kamdar, 2004). 2.4 Transit Signal Priority Control Strategies Active priority, passive priority, and adaptive priority are the three different ways to implement TSP. Passive Priority: Passive priority operates continuously and is based on the historical knowledge of the transit route, ridership patterns, and frequency of transit vehicles. A transit detection / priority request generation system is not needed as the signal timings are fixed based on previous data (Smita et.al, 2005). Passive priorities operate efficiently if the transit flow is heavy, uniform, and 7

16 predictable. The passive priority strategies are low cost, easy to implement, and changeable depending on traffic and transit operations. Disadvantages of passive priority strategies include potential delays for crossing traffic, frequent signal maintenance, excessive allocation of green to priority vehicles, and passenger dissatisfaction due to unnecessary delays for the crossing street traffic. (Ova and Smadi, 2001). Passive priority strategies include the following methods. Adjustment of Cycle Length The cycle length can be adjusted depending on the time period of the day. Long cycle length is favorable for reducing lost times at intersections, but it may cause delays for vehicles at an intersection. Although short cycle length reduces the capacity at an intersection, it can be used as a passive priority measure to decrease delay times and improve transit performance. Phase Splitting Phase splitting refers to splitting priority phases into multiple phases and repeating these phases within one cycle (Yihua Zhang, 2001). Metering Vehicles Metering of a single phase can reduce the flow on a designated roadway, but TSP can allow transit vehicles to bypass the metered signal phases, thus providing a smoother flow for transit vehicles (Zhang, 2001) Area-Wide Timing Plans Area-wide timing plans can be designed in two forms. First, green time can be allocated based on the number of passengers, rather than vehicles, which pass through a network's intersections. Second, transit vehicles can be given priority by coordinating intersection signal plans to allow transit vehicle progression through a network (Garrow and Machemehl, 1997). Active Priority: 8

17 According to the Federal Transit Administration (FTA), an active priority strategy involves the detection of a transit vehicle and the provision of special treatment for the transit vehicle depending on the system logic and traffic situation. Unconditional priority and conditional priority are two types of active signal priorities. Unconditional priority is granted, subject to safety considerations, whenever a transit vehicle calls for priority; whereas, with conditional priority, a transit vehicle is not necessarily given priority at an intersection every time it is requested (Garrow and Machemehl, 1997). Schedule adherence, the volume of traffic on the cross street, and other factors are considered before granting the priority. Active priority strategies are implemented as follows: Early Green When a transit vehicle is detected, green time is shortened for preceding phases and early green is provided for transit vehicles. Green Extension Additional green time is provided to transit vehicles when they are in an intersection to allow the transit vehicle to clear the intersection without stopping. It is applicable if the green light is about to change to red. Phase Insertion When a transit vehicle is detected, a short green phase is inserted to allow the transit vehicle to move through an intersection without stopping. Phase Rotation The phase sequence can be changed to TSP for buses following priority request activation. Adaptive Priority: Adaptive signal priority is a strategy that takes into consideration the trade-offs between transit 9

18 and traffic delay and allows graceful adjustments of signal timing by adapting the movement of transit vehicles to the prevailing traffic condition (Smita et.al, 2005). To take advantage of adaptive signal control systems, TSP would typically require the early detection of a transit vehicle to provide enough time to adjust the signals and give priority while minimizing possible negative impacts (Jhang, 2007) 2.5 Components of TSP system Vehicle detection Vehicle detection is a system that detects transit vehicles and provides data to a priority request generator. The vehicle data may include the arrival time and location. A transit vehicle may be detected at a local intersection through a combination of an on-board transmitter and a receiver on the intersection approach (Smita et.al, 2005). Priority Request Generator After obtaining the vehicle data, a priority request generator (PRG) communicates with a priority request server (PRS) and initiates the request. A PRG is usually located on the vehicle whereas a PRS is located at the intersection. Priority Request Server Based on the pre-defined criteria, a PRS processes the request obtained from the PSG. Communication system The function of the communication system is to interconnect the PRG, PRS, and other components required for a transit priority system. 10

19 2.6 Review of the literature on TSP Analytical studies for delay calculation for green extension and red truncation approaches: Few analytical models have been developed to study the impacts of TSP (benefits to the prioritized approach and delay to the non- prioritized approach) for green extension and red truncation approaches. Liu et al (2008) proposed one such model. The main assumption of this model was the deterministic arrival and service rate of the vehicles. This model ignored the queue after the end of the second cycle. According to this model, the expression for an additional delay on the non-prioritized approach due to red truncation is as follows: Figure 2-1 Impact of red truncation in the non-prioritized approach (Liu et al, 2008) Abdy and Hellinga (2011) considered the shortcomings of the Liu et al. model. Their model also calculated the additional delay for crossing traffic due to priority in the main street, but it considered the queue that could form beyond the second cycle. The following cases were considered: 11

20 Table 2-1 Queue dissipation cases (Source: Abdy and Hellinga,2011) Figure 2-2: Impact of red truncation in the non-prioritized approach (Source: Abdy and Hellinga, 2001) 12

21 2.6.2 Field and Simulation Studies Columbia Pike Arterial Corridor, Arlington Dion et al. (2004) evaluated the benefits of TSP in Columbia Pike Arterial Corridor, Arlington. The impact of priority strategies (green extension and green recalls) was observed during the morning peak and midday traffic using an integration microscopic traffic simulation model. The model gave priority to express buses, regular buses, and to all buses within the study corridor. The transit priority was found to be beneficial to buses in the morning peak between Dinwiddie and Queen. For express buses, the simulation results showed a 2.3% - 2.5% reduction in travel time, 3.7% - 4.1% reduction in delays, 1.3% - 2.7% reduction in vehicle stops, and 1.1% - 2.7% reduction in fuel consumption. Regular buses benefitted with a 4.8%, 7.6%, 1.8%, and 1.9% reduction in travel time, delay, stops, and fuel consumption, respectively. However, the priority strategies showed negative impacts on general traffic. The other outcome of the study was that the negative impacts of transit priority would be minimal if provided at the signalized intersections where the volume to capacity ratio is less than 80%. The priority measures had neither a significant reduction in performance measures nor a significant increase in these measures when the overall system was considered Saint Michel Corridor, Montreal, 2013 Diab and El-Geneidy (2013) evaluated the impacts of various TPM implemented in the Saint Michel Corridor in Montreal by the Societé de Transport de Montréal. They examined the running time deviation from the schedule and variation in running time. The priority strategies that were implemented in the study corridor included a smart card fare collection system, reserved bus lanes, the use of articulated buses, the introduction of limited- stop bus service, and 13

22 the operation of TSP. The study compared the data from regular route 67 and the transit prioritized route 467, which ran parallel to route 67 and was almost equal in length. The results of the evaluation were as follows: Bus running time increased by 16.1 seconds (or by 3.8 %) compared to the initial situation with the application of a smart card fare collection system. Using the smart card fare collection system increased the running time deviation from the schedule by 3.9%, the running time variation by 0.7%, and the variation in running time deviation from the schedule by 1.1%. The introduction of articulated buses increased the running time by 13 seconds (or by 3.1%) and increased the running time deviation from the schedule by 6.6%. However, the results showed a decrease in running time variation and a decrease in running time deviation from the schedule by 0.9% and 2.5%, respectively. The presence of articulated buses on the roads had negative impacts on other buses as their running time increased by 9 seconds (2.2%) per trip segment along with the increase in deviation from schedules by 5.3%. Eleven seconds (2.7%) was saved during peak hours with the introduction of exclusive bus lanes. The running time deviation from the schedule decreased by 4.3%, and these buses were found to be more variant in travel time (0.5% more than buses not using these lanes). The TSP equipped buses were found to be faster than other buses by 1.4 seconds (0.3%) per trip segment, but there was no significant effect on running time deviation from the schedule, running time variation, and variation in running time deviation from the schedule. The results also showed the decline of running time by 1.37 seconds (0.3%) per trip for non-tsp equipped buses. These buses also suffered a 0.5% increase in running time variance and a 0.6% increase in variation of running time deviations from the schedules. On high-frequency routes, a mixed flow 14

23 of TSP equipped and non-tsp equipped buses were not recommended due to increased unreliability Bay Street, Downtown Toronto Shalaby (1999) used the TRANSYT-7G simulator to examine changes in performance measures of through buses and adjacent traffic following the introduction of reserved lanes in urban arterials. The study area considered was Bay Street in downtown Toronto. The simulation results showed a decrease in total vehicle travel (vehicle-km) for both morning and evening peak periods. However, passenger travel (passenger-km) increased by 12% in the morning due to an increase in the total passenger volume as bus only lanes improved the average bus performance, but remained unchanged in the evening. Total vehicle delay decreased in the morning, but it increased in the evening. The ridership and transit share of passengers increased while adjacent traffic volumes decreased with the introduction of a reserved bus lane together with increased bus frequency. The simulation results showed that introducing bus lanes was successful as it increased the ridership and transit share of passengers King Street, Toronto The case study of King Street in Toronto (Shalaby et al. 2003) analyzed the impacts of various transit priority schemes in conjunction with streetcar operation using microsimulation. Different scenarios were considered, such as unconditional TSP, priority already in operation, turning off existing TSP prohibiting all left turns, and prohibiting private traffic on King Street. The cycle 15

24 time for a streetcar, transit travel time, average speed per hour, headway standard deviation, average service frequency per hour, person quantity, transit vehicle bunching, overall traffic, and transit average speeds were considered as the measures of effectiveness in the model, which were helpful to determine the impacts of different scenarios considered in microsimulation. The results showed that the use of all examined TPM had positive impacts and were beneficial to transit compared to the case without the implementation of transit priority. An increase in transit travel time (round trip) by 11% occurred when the transit priority was turned off. Prohibition of left turns showed a reduction in round trip travel time by 13%; whereas, in the case of banning vehicular traffic on the route, travel time was reduced by 30%. The results indicated an increasing pattern of transit speeds and a decreasing pattern of average headway due to the addition of transit priority, banning left turns, and exclusive dedication of King Street to transit. The transit priority alone was found to be effective in terms of reducing headway deviation. An increase in the average service frequency was minimal when transit priority was introduced by itself, but the average service frequency improved considerably with the addition of banning left turns. The trend of person throughput followed the similar trend of service frequency. Both transit priority and a banning of left turns reduced the level of bunching. However, bunching could increase due to a full traffic prohibition because of the lack of traffic cushioning between streetcars when traffic is permitted. The results showed that there was a slight drop in the average speed of the traffic due to the implementation of transit priority as an increase in transit speed was associated with a decrease in vehicular traffic speed. 16

25 Fargo, ND The work of Ova and Smadi (2001) provides the theoretical evaluation of TSP strategies in medium sized cities. They used VISSIM microsimulation to model the transit routes in downtown Fargo, ND. The study evaluated different scenarios involving two TSP strategies: existing and reduced bus headways and two traffic peak periods. The following results were found: The results showed a reduction in side street delay during midday, but side street delay increased during the afternoon period. For 30-minute headway at midday, the increases in side street person-delays were 7.6 % for early green to 8.7% for extended green. However, for 15- minute headway at midday, the delays increased to 16.9 % for early green and 14.8% for extended green. The results for the afternoon periods followed the same trend where increased headways caused longer side street delays. The impact of extended green was found to be less than that of early green for network delays during the afternoon periods. A maximum of 22.6% network delay was experienced with an early green strategy during the afternoon periods. During midday, the results showed that the impacts of both early green and extended green are similar. The travel time savings for all midday scenarios ranged from 12.4% to 14.2%. For 15 minute and 30-minute headways, the early green strategy resulted in a 10.6% and 9.5% savings in time, respectively, in the afternoons. Extended green resulted in a 2.4% reduction for a 15- minute headway during the afternoon period. 17

26 Salt Lake country, Utah Reza (2012) studied the impacts of queue jumpers with TSP on Bus Rapid Transit in Salt Lake County, Utah. Various models were developed in VISSIM and included a base scenario, no TSP with queue jumpers, TSP with no queue jumpers, and TSP with queue jumpers. Green extension and red truncation were also studied in this research. The results of the research are summarized below: For TSP with queue jumpers, an average travel time of BRT decreased by 13%-22%, while the travel time for private traffic increased by 1.3%-2.1%. A reduction in travel time for no TSP with queue jumpers varied from 5%-16%, whereas a reduction in travel time of 9%-12% occurred for TSP with no queue jumpers. Implementing both TSP and queue jumpers together were found to be more effective than when only one strategy was used. For the overall network for TSP with queue jumpers, the average delay for BRT was reduced by 33.5%, and the delay for traffic increased by up to 12.5%. The cross streets of main corridors were largely affected by the implementation of queue jumpers Beijing, China Zhu et al. (2012) also used a simulation approach to evaluate the impacts of planned exclusive bus lanes in expressways in Beijing, China. The study area was about 8.5 km long in the southnorth direction in the main portion of the western 3 rd ring expressway network. A curbside bus lane scenario and median bus lane scenario were modeled in VISSIM and compared with the base scenario without bus lanes. 18

27 For curbside bus lanes, the average travel speed of the buses increased by 34.8%, and the speed of the general traffic increased by 12.2%. The average delay per unit distance for the buses, general traffic, and all mixed traffic decreased by 57.8%, 24.4%, and 43.5%, respectively. The results for the median bus lane scenario followed the same pattern. The average travel speed for the buses and general traffic increased by 37.8 % and 20.8%, respectively. The average delay per unit distance decreased by 60.2%, 40%, and 51.2% for the buses, general traffic, and all mixed traffic, respectively. The median bus lane scenario performed marginally better than the curbside bus lane scenario New York, U.S Teng et.al (2002) performed a simulation for bus priority control by providing early green and extended green. The locations considered for the study were: (1) Cross Bay Boulevard and Belt Parkway (Brooklyn), (2) Adams Street and Tillary Street (Brooklyn) and (3) Broadway, Chambers, Park Row, and Vesey-Barclay (Manhattan). Cross Bay Boulevard and Belt Parkway (Brooklyn) During the morning peak hour, midday peak period, and afternoon peak hour, bus delays were reduced, but they remained the same for the late night period. This result may be due to the low volume of traffic at late night. In contrast, general traffic delays were reduced during the morning peak, but they increased at the midday and afternoon peak periods. They remained the same for the late night period. Adams Street and Tillary Street (Brooklyn) 19

28 For all the observed time periods, the results showed a reduction in bus delays, but an increase in general traffic delays. Broadway, Chambers, Park Row, and Vesey-Barclay (Manhattan) Significant reductions in bus delays were found at the midday and afternoon peak periods, while the corresponding general traffic delay increased during the afternoon peak and remained almost the same at the midday peak period. Bus priority had a minimal impact on bus delays during the morning peak and late night period, but general traffic delays increased during the morning peak (Teng et.al (2002) Washington DC In the Northern Virginia area (or Washington DC metropolitan area), the green extension was used as a TSP method to find its effect on transit performance and overall impact on the system (Rakha and Ahn, 2006). The field data was initially used to measure the impact on transit performance, and the study was later expanded using computer simulation techniques. Five scenarios were evaluated using the INTEGRATION simulation model: the base demand case, increased mainline traffic demand, increased side street demand, increased transit vehicle service frequency, and modified bus stop location. The field study results are as follows: Improved travel time by 3%-6% for TSP operated buses. Reduced intersection delays of up to 23%. Increased transit vehicle travel time of up to 2.5 % after implementing green extension during congested periods. 20

29 The simulation results showed that TSP had no impact on transit vehicle travel times, systemwide travel times, and side street queues. Generally, the system-wide detriment (in travel time, queues and delays) increased with an increase in traffic demand. However, a minimal increase in system-wide delays was found (less than 1.37%). The results showed no significant changes in transit vehicle travel times even after the implementation of TSP. The delays were reduced by up to 3.2% by decreasing the headway or by increasing the number of transit vehicles, which benefitted the buses. The study also evaluated the impacts of the locations of bus stops on network performance. Near-side bus stops resulted in more delays (increased up to 2.85%), whereas mid-block and far-side bus stops were found to be more beneficial (network-wide savings in delays in the range of 1.62%) (Rakha and Ahn, 2006) Sacramento, California Haas (2006) conducted before and after studies to assess the impact of TSP on travel time and reliability. Floating car runs were conducted on Watt Avenue and cross streets in the study area. The results showed that the average travel time savings ranged between 14 and 71 seconds for TSP equipped buses compared with the non-tsp buses running on the same section. The results also showed that TSP did not have a significant impact in terms of travel time reliability. Only two out of six intersections with TSP had improved travel time reliability. In addition, inconclusive findings were obtained for traveler mobility Granville Street, Vancouver The research performed by Ngan, et al. (2004) studied the effect of various parameters on the effectiveness of TSP applications. VISSIM was used as the microsimulation software to analyze different scenarios in 98 B- Line rapid buses along Granville Street. The impacts of seven traffic 21

30 and transit parameters on TSP effectiveness were examined. These parameters were bus approach volume, cross street volume/capacity (v/c) ratio, bus headway, bus stop location, bus check-in detector location, left turn condition, and signal coordination. The results showed that the most effective TSP applications were in traffic conditions that had moderate-to-heavy bus approach volumes, little or no turning volume hindering bus movement, light-to-moderate cross street v/c ratios, far side bus stops, and signal coordination for traffic running in the peak direction (Ngan et.al, 2004). 2.7 Different TSP approaches Different traffic control scenarios such as fixed-time control, adaptive splits (splits adjustment performed every five minutes), and adaptive splits and offsets (split adjustment every five minutes along with an offset adjustment every cycle) were considered in the simulation study along Columbia pike in Arlington. The results showed the benefits(less number of stops, fuel consumption, vehicle emissions) for buses in all the control scenarios; the results also showed that although adaptive signal control reduced negative impacts on general traffic, it did not negate all the negative impacts (Dion and Rakha, 2015). Ding et.al, 2013 developed a microscopic simulation environment for multimodal traffic signal operations. Several requests were sent from several vehicles at a time, and these requests were identified and prioritized with the use of advanced communication technologies such as connected vehicles, global positioning systems (GPS), and different wireless technologies. The results showed that use of these systems could provide safe and effective multimodal traffic operations. 22

31 Yang et.al (2013) studied two control strategies for priorities: signal priority using advanced detection and transit speed control. Signal priority using advanced detection is a flexible control algorithm that detects vehicles one cycle earlier than normal and adapts accordingly; transit speed control regulates the speed of buses and predicts the arrival of the buses at an intersection. The results showed that the proposed strategies improved the efficiency of the BRT more than conventional active signal priority and exclusive bus lanes (Yang et.al, 2013). The paper by Hao et.al, 2014 focused on a coordinated model for transit signal priority in a connected vehicle environment. The control unit was in between two successive stops, and there was a dedicated bus lane as well. This model focused on schedule adherence and minimizing impacts on general traffic. The results showed a decrease in the variation of bus travel time and delay. Delays experienced by crossing street traffic increased, but, considering the overall intersection, delays remained the same or decreased (Hao et.al, 2014). Figure 2-3: Structure of coordinated TSP system in a connected vehicle environment (Source: Hao et.al, 2014) 23

32 2.8 Adaptive Signal Control Strategies The following section gives an overview of the most relevant adaptive traffic control methods. Urban Traffic Control System (UTCS) UTCS was developed by the Federal Highway Administration (FHWA) in the 1970s and 1980s. The first generation of UTCS used a fixed timing signal, and the plans could be selected by an operator from the library of plans. The later UTCS systems were based on real-time data collected from the detectors. The latest UTCS system uses uses a traffic prediction model as well as a signal time transition model. The signal time transition model minimizes the transition time between various plans (Jinwoo Lee, 2007). Sydney Coordinated Adaptive Traffic System (SCATS) SCATS is a real-time adaptive traffic control system that maximizes the use of the road network and is self-calibrating software. The SCATS controller collects the vehicle count and total time a detector is unoccupied from the loop detectors, which are placed at each lane. It adapts the signal based on the degree of saturation, which is the ratio of effective green time use to the available green time (scats.com.au). Split, Cycle, Offset Optimization Technique (SCOOT) SCOOT is an adaptive traffic control system that improves the progression of vehicles in the network by coordinating traffic signals. SCOOT obtains data from detectors placed on each link, and it responds according to the change in flow. It has three optimizers, which continuously optimize cycle time, offset, and split. These optimizers work continuously to minimize the wasted green time at intersections and reduce the number of stops and delays by synchronizing 24

33 the signals. There is a common cycle length for all intersections within the same control area (scoot-utc.com). Optimization Policies for adaptive Control (OPAC) OPAC is a distributed real-time traffic control system that adapts signal timings to minimize the performance function of intersection delay. It was first developed in 1979 and has been continuously enhanced. The latest OPAC controller switches the phase sequence in response to the traffic demand. The latest OPAC has a distinct feature that varies cycle time within a bound to help in signal coordination (Jinwoo Lee, 2007). Real-time Hierarchical Optimized Distributed Effective System (RHODES) RHODES is an adaptive control system developed by a research group from the University of Arizona. It has three levels. The highest level is dynamic network loading, which captures slow-varying characteristics of traffic. The medium level, network flow model, allocates a green split to each link depending on the traffic demand. The lower level, intersection control, selects the appropriate phase based on observed and predicted arrivals for each intersection. It predicts traffic using detector and sensor information (Jinwoo Lee, 2007). 2.9 Passengers Perceptions towards bus service attributes The survey conducted by Tyrinopoulosa and Antoniou (2008) found that the quality of service and transfer quality are the two most important priorities for customers. They also found that for different transit systems, the particular characteristics (service frequency, vehicle cleanliness, the behavior of staff, waiting conditions, and network coverage) play an important role in the 25

34 satisfaction of the customers. The research conducted in Delhi showed that crowdedness is the number one reason why people do not use the buses (Suman et. al 2014) and that transit passengers dislike waiting at stops (Yoh et.al 2010). The willingness of riders to use transit more often depends on their previous experience (Jen and Hu, 2003) and the frequency of bus service (Fuji and van, 2209). The declining use of public transit caused transit companies to think more about improving the quality of service to attract more riders (Eboli and Mazulla, 2007). Improving bus reliability and the frequency of buses were found to be the two main factors that help to increase ridership in Australia (Currie and Wallis, 2008). The reliability of buses can be improved with the use of different bus priority measures to reduce delays and help buses reach their destinations on time. Grdzelishvili, Sathre (2011) conducted a survey to understand the travel attitudes and behaviors of Tbilsi residents. The respondents were both car drivers and public transit users. The results of the survey showed that most of the respondents prefer private cars than public transit because of time issues, schedule and frequency, and comfort and safety. A survey conducted on Beijing s southern axis BRT line 1 investigated passenger attitudes towards BRT. The results of the survey showed that BRT gained popularity over time and the value of property also increased near the BRT stations. Most of the users were work commuters, and the frequent users were satisfied more than choice users in terms of reliability, comfort, cleanliness, and overall satisfaction (Deng and Nelson, 2012). A survey was conducted in Nanjing, China to know transit passengers perceptions and the role the perceptions played in mode choice. The results of the survey showed that reliability and comfort played a greater role in mode choice than availability and safety played (Xiaojian Hu et.al, 2015) 26

35 Service provided by the bus agencies plays an important role in mode choice and passengers perception towards public transit. A study was conducted in Montreal to determine passengers perception towards service improvements such as smart card fare collection, limited stop bus service, reserved bus lanes, articulated buses, and TSP. The results showed that the respondents were generally satisfied with the service improvements. Riders tended to overestimate the savings in travel time by 3-6 min and min for regular and limited stop bus routes, respectively (Diab and El-Geneidy, 2011) Summary This chapter reviewed the previous field studies and simulation studies for assessing the impacts of various TPM on transit travel time, time reliability, and the effects on general traffic. Most of the studies showed an improvement in transit performance without considerably affecting the general flow of traffic after the application of TPM. However, some studies showed that the negative effects experienced by crossing street traffic outweighed the benefits to public transit. The literature also showed that the application of TSP measures together with other priority measures, such as queue jumpers, was more effective. The most common TSP measures are early green and green extension. Different factors, such as frequency of transit vehicles, capacity of a road network, bus stop locations, and left turn conditions played a big role in the success or failure of a transit priority system. Some studies also used advanced wireless and communication technologies to provide priority so that transit became more reliable and safer. Some studies were conducted to examine transit passengers perceptions towards bus service attributes and, in general, the research showed that frequency, reliability, and safety were the most important factors in determining mode choice. 27

36 3. Transit Priority Logic One of the main objectives of this research is to develop a conditional priority for buses by considering the delays experience by crossing street traffic. In the existing priority, even if the bus is empty, it gets priority, which leads to unnecessary delays on the corresponding crossing street. The conditional priority developed in this research provides early green or red truncation based on both bus passenger occupancy and bus deviation from the schedule to reduce unnecessary delays for crossing street traffic and buses. Early green and red truncations are the most used TSP methods (Dion et al. (2004), Teng et.al (2002), Rakha and Ahn (2006)) In this chapter, similar assumptions as in Liu et al, (2008) and Abdy (2011) were considered to estimate the benefits and delays during green extension and red truncation. The main assumptions for estimating the delays for crossing street traffic and the benefits for major street traffic are fixed cycle length C, no recovery algorithm, and that the queue will be served by the end of the next cycle. As the developed priority depends on the number of passengers on the bus, it is necessary to estimate the minimum number of passengers required to grant priority. The delays experienced by the crossing street traffic and the corresponding benefit to the major street traffic can be estimated based on a deterministic queuing theory. The calculation to determine the minimum number of passengers required on a bus is explained in section 3.3. The hatched area shown in the figures below (3-1, 3-2, 3-3, and 3-4) indicates an additional delay for non-prioritized approaches or an additional benefit for prioritized approaches resulting from TSP. The following variables are used in the calculation of the additional delay and delay savings. 28

37 q1, q2 = vehicle arrival rate for priority and non-priority approaches, respectively S = vehicle departure rate or service rate rp,gp = red and green interval for priority approach rnp,gnp = red and green interval for non-priority approach ge = green extension time gt = red truncation time 3.1 Red truncation [Early green] For red truncation in the prioritized approach, the total reduction in delay is calculated using cumulative arrival and departure curves and is represented by the shaded area in Figure

38 Priority approach Figure 3-1: Prioritized approach (Red truncation) Total delay reduction = 1 2 g tq 1 (t 2 -t 1 + g t ) st 1 ( 2g t + t 1 ) 1 2 st 1 2 Where, t 1 = q 1(r p g t ), t s q 2 = q 1r p 1 s q 1 Non- Priority Approach The delay on the non-prioritized approach is calculated based on the queuing diagram shown in Figure 3-2. The total additional delay in the non-prioritized approach due to priority for buses is represented by the shaded area in the figure below. 30

39 Figure 3-2: Non-Prioritized approach (Red truncation) Total additional delay = 1 2 q 2(c + r np + t 2 ) s (g np g t ) 2 s(g np g t )(r np + g t + t 2 )- 1 2 s 2 st q 2r np s q 2 Where,t 2 = s((g np g t ) q 2 (c+r np ) q 2 s, t 1 = q 2r np s q 2 31

40 3.2 Green extension Priority approach Similar to red truncation, cumulative arrival and departure curves are used to estimate the reduction in delay on the major street due to priority for buses. The shaded area represents the delay savings in the priority approach when green extension is implemented. Figure 3-3: Prioritized approach (Green extension) Total delay savings = 1 2 q 1(r p + t 2 ) st ( 1 2 q 1(r p g e + t 1 ) st 1 2 ) 32

41 Where, t 1 = q 1(r p g e ) s q 1, t 2 = q 1r p s q 1 Non-priority approach: The additional delay in the non-prioritized approach is represented by the hatched area in Figure 3-4 below. Figure 3-4: Non-Prioritized approach (Green extension) Total additional delay = 1 2 q 2(c + r np + t 2 ) 2 - s(r np + t 2 )(g np g e ) 1 2 st ( 1 2 q 2(r np + t 1 ) st 1 2 ) q 2(r np + t 3 + g e ) s(t 3 + g e ) s(g np g e ) 2 33

42 Where, t 3 = q 2(r np +g e ) sg e, t s q 1 = q 2r np, t 2 s q 2 = q 2(c+r np ) s(g np g e) 2 s q Finding the number of passengers required on the bus to get priority In general, research has been focused on determining the benefits of priority to buses, but it has neglected taking into account the effect on crossing street traffic. In this research, we consider giving priority to buses only if the benefits are equal to or outweigh the negative consequences in terms of delays experienced by crossing street traffic. The benefits outweigh the negative consequences if the number of people using transit is more than those using cars. If the bus is empty or has very few passengers, giving priority to buses is not justified as the delays per person will be greater for the crossing street traffic. The benefits and delays for major street and crossing street traffic were equated to find out the minimum number of passengers required on the bus to grant priority. If this priority criterion is used, the overall delay per person in the network will decrease and the delays per person at the crossing street should also decrease. Let n i = the minimum number of passengers required on the bus to get priority (to be determined) o v = average occupancy of vehicles (assumed to be 1.2) γ c = the value of travel time of passengers per second (assumed to be $20 per hour) n j = the average number of passengers waiting in the next stop (calculated using the Automatic Passenger Counters (APC); data provided by Calgary transit) W= constant that provides more weight to transit passengers D as = average delay savings per vehicle on the major street 34

43 Da = average delay per vehicle on the crossing street D as = total delay No.of vehicle in a cycle [qc] The case of a bus being on schedule: If a bus is on schedule, the savings in delays for buses and other vehicles that can proceed when priority is granted are estimated as follows: Total cost savings = D as n i γ c + qg e o v γ c (i) Total cost of extra passenger delays for travelers on the crossing street = D a o v γ c (ii) An additional weight W was given to the cost of delay for transit passengers. The minimum number of passengers required on the bus to provide priority is obtained by equating cost savings and total cost. If the number of passengers is low, the person delay on the crossing street will increase and vice versa. Equating these two equations: D as n i γ c + qg e o v γ c = D as o v γ c n i = D aso v γ c qg e o v γ c D as γ c = (D a qg e )o v γ c D as γ c p1 = n i = (D a qg e )o v..(iii) D as The case of a bus behind schedule: 35

44 If the bus is late, the cost of additional delays for passengers waiting at subsequent bus stops is also considered. Here, g elapsed is the green time of the crossing street when the bus requests priority. Total number of passengers waiting up to the next time point= n j Total cost savings = D as n i Wγ c + ( n j ) γ c (R g elapsed )+ qg e o v γ c (iv) Total cost on crossing street = D a o v γ c (v) Equating equations (iv) and (v) D as n i Wγ c + ( n j ) γ c (R g elapsed )+ qg e o v γ c = D a o v γ c D as Wγ c n i + γ c (R g elapsed ) ( n j ) = D a o v γ c - qg e o v γ c.(vi) The bus is given priority in the following case: An i + B n j C (vii) Where, A=D as Wγ c, B=γ c (R g elapsed ), C=D a o v γ c - qg e o v γ c For modeling in VISSIM in this research, we only considered the number of passengers waiting at one bus stop ahead of the intersection. So n j =n j. From equation (v), we get the following:n i = D ao v γ c qg e o v γ c n j r c (R g elapsed ) D as Wγ c p2 = n i = (D a qg e )o v n j (R g elapsed ).. (viii) D as γ c Out of all the parameters in the equations (iii) and (viii), the arrival rate of vehicles has the most impact on the number of passengers required on the bus to get priority. The minimum number of 36

45 passengers for both green extension and red truncation are calculated using the above equations. The average of the two minimums was taken for simplicity in coding VISSIM, and the average was fixed as a minimum number of passengers required to grant priority. The minimum number of passengers required to get priority varied depending on whether the bus was early, on schedule, or late. The early buses were not provided with priority whereas the buses later than three minutes were granted priority even without fulfilling the passenger requirement. With this priority method, it is less likely to have the problem of bus bunching as early arrival and lateness are considered. The flowchart below describes the priority logic used in the research. From the expressions derived in equations (iii) and (viii) we can see that ni depends on the traffic volume at the intersection. Therefore, the minimum number of passengers required on the bus will be different for different intersections. First, the algorithm checks whether the bus is detected in the detector or not. If bus is detected, it checks whether the bus is early or not. If the bus is early, no priority is granted. Then, if the bus is on time or late by up to one minute, it checks whether the minimum passenger requirement (p1 calculated using the above expression) is fulfilled or not. If the criteria are met, then the bus is given priority. If the criteria are not met, there is no change in signal timings. If the bus is late by one to three minutes, it again checks if the passenger criterion (p2) is fulfilled or not. If the criteria are met, the bus is given priority; if the criteria are not met, then no priority is given. If the bus is late by more than three minutes, the algorithm does not check the number of passenger, and it gives priority to the bus. This priority logic also addresses the problem of bus bunching as the early buses do not get priority, whereas buses three or more minutes later than scheduled get priority irrespective of the number of passengers on the bus. 37

46 Vehicle Actuated Programming (VAP) was used as the programming tool to code the above mentioned logic and imported in the VISSIM to evaluate the developed priority. VAP is an addon module of VISSIM, which can be used for programming actuated signal controls. The program was written in a text file using a simple programming language (VAP manual). 38

47 Figure 3-5: Flowchart for transit priority logic 39

48 4. Calibration of VISSIM Model 4.1 VISSIM as the simulation software VISSIM is a microscopic, time step and behavior-based simulation model developed to replicate urban traffic and public transport operations and pedestrian flows (VISSIM manual 5.3). It is commercially available software developed by the PTV group in Karlsruhe, Germany. VISSIM uses a psychophysical driver behavior model developed by Wiedemann (1974) (VISSIM manual 5.3). The psycho-physical driver behavior model states The basic concept of this model is that the driver of a faster-moving vehicle starts to decelerate as he reaches his individual perception threshold to a slower moving vehicle. Since he cannot exactly determine the speed of that vehicle, his speed will fall below that vehicle s speed until he starts to slightly accelerate again after reaching another perception threshold. This results in an iterative process of acceleration and deceleration. (VISSIM Manual 5.3, 2011). VISSIM was chosen as the simulation software as it can simulate multimodal environments. Traffic and transit operations can be analyzed in VISSIM under various constraints and in different environments. 4.2 Study Area The study area for this study is Center Street North, a heavily used transit corridor in Calgary. The section between 5 Avenue and McKnight Blvd is chosen as the study network. The study corridor, Center Street North, is one of the busiest transit corridors in the city, and more service is anticipated in the future due to the expanding communities on the north side of the city. 40

49 The study area has several transit routes including bus rapid transit (BRT). Routes 300 and 301 are the BRT routes that currently operate along the corridor. Other regular routes include routes 3, 62, 64, 109, 116, and 142. Route 2, 17, and 275 also use a portion of the study corridor. Figure 4-1: Geographic location of study area (Source: google maps) 41

50 Figure 4-2: Study area showing major intersections (Source: Calgary Transit web). The Center Street North corridor has bus lanes from 43 Avenue to 54 Avenue and reversible lanes from 5 Avenue to 22 Avenue. The study area also has a high occupancy vehicle (HOV) lane (Southbound: 19 Avenue to 5 Avenue for morning peak) and queue jumps at McKnight Blvd and Centre street north. 42

51 Figure 4-3: Bus only lanes between 54 Avenue and McKnight Blvd (Source: Calgary Transit web). Figure 4-4: Bus only lanes between McKnight Blvd and 43 Avenue (Source: Calgary Transit web). The modeled section is 5.3 km long and has 43 intersections (11 signalized intersections and 32 un- signalized intersections). 43

52 Table 4-1: List of Signalized Intersections Number of Intersection (North to South) Name Description 1 McKnight Centre Street North 2 40 Avenue@ Centre Street North 3 32 Avenue@ Centre Street North 4 20 Avenue@ Centre Street North 5 16 Avenue@ Centre Street North 6 12 Avenue@ Centre Street North 7 10 Avenue@ Centre Street North 8 2 Avenue@ Centre Street South 9 3 Avenue@ Centre Street South 10 4 Avenue@ Centre Street South 11 5 Avenue@ Centre Street South 44

53 4.3 Modeling Process The process followed to model the network in VISSIM is shown in the figure below. Figure 4-5: Modeling process in VISSIM 45

54 4.4 Data Collection Various types of data were needed to develop and calibrate the base VISSIM model: road geometry, signal timing data, traffic volume data, and transit data Road Geometry The road geometry data, such as the number of lanes, width of lanes, and distance between intersections, were obtained using Google maps Signal Timing data Signal timing data for all signalized intersections were provided by the City of Calgary, Transportation Department. The data provided by the city were the recently updated. For most of the intersections, the data were from 2014; some data were from The data included signal timing for morning peak, evening peak, and off- peak hours. The signal timing currently used along the study corridor is different at different intersections. In some intersections, there is fixed signal timing, whereas in other intersections there are actuated and semi actuated signal timings Traffic Volume data Traffic volume data for the study area, including signalized and un-signalized intersection turning movement counts, were provided by the City of Calgary, Transportation Department. The date of data collection for different intersections varied. For some minor intersections, the data collected by the city dated back to 2008 or 2009, but for major intersections, the data were collected in late 2013 or early As it would take a long period of time to collect recent 46

55 traffic volume data, the data obtained from the City of Calgary was used despite it not being recent. The data included 15 minute traffic count, peak hour flow, truck flow, pedestrian count, and bike count in all directions for morning peak, daytime peak, and evening peak periods Transit data Automatic Passenger Count (APC) data was provided by the City of Calgary, Calgary Transit. From the data, bus travel time, dwell times of buses, the average speed of buses, and the number of boarding and alighting passengers at every bus stop were calculated. In addition, GPS data collection was conducted for 3 days during morning and evening peak hours. The data were collected on February 17-19, 2015 by graduate students who alighted on the bus. The data collected between September to May was expected to represent typical traffic scenarios in Calgary as it includes the academic session and students are one of the main users of Calgary Transit. Therefore, the data collected in February should represent the typical Calgary traffic. The GPS was turned on at the origin intersection and turned off when the destination was reached. The bus travel time was calculated from the GPS data, which was used to validate the model. Twelve sets of data were collected over three days. As the data obtained from INRIX was for certain sections, McKnight- 32 Avenue, 32 Avenue-16 Avenue, 16 Avenue-7 Avenue, and 7 Avenue-4 Avenue, the bus travel times for these sections were calculated from the raw GPS data. 47

56 Table 4-2: Bus travel times Average Travel times (sec) Section from GPS data North Bound South Bound Mcknight-32 Ave Ave-16 Ave Ave-7 Ave Ave-4 Ave Bus stops, queue jump, reversible lanes, and bus only lanes The location of the bus stop, type of bus stop, length of bus bay, queue jump, bus only lanes, and reversible lanes were found using Google maps. 4.5 Base Network coding Base network coding was needed to draw the existing road geometry in VISSIM as the evaluation intended to compare the advantages and disadvantages of the existing TSP with the purposed one. Google maps were used to draw the road geometry. The map of the study area was scaled and links and connectors were drawn on the background map. The lane width and number of lanes were also found using Google maps. 48

57 Figure 4-6: A section of the VISSIM model network After the skeleton network was completed, the vehicle composition, vehicle characteristics, speed of the different classes of vehicles, dimensions of different vehicle types, acceleration, and deceleration functions were needed. The truck flow, number of bikes, and number of cars were provided in the traffic volume data sheet obtained from the City of Calgary. The vehicle dimension and vehicle characteristics such as acceleration and deceleration were left as default values, whereas other parameters were changed based on the available data. The traffic volumes, routing decision, priority rules, traffic signals, and transit information were needed as input in the model. The traffic volume data and pedestrian data obtained from the City of Calgary was inputted in the model. 49

58 VISSIM has the option of conducting static or dynamic vehicle routing. Static routing involves defining the starting point and the destination points using percentages for each destination. Dynamic routing involves defining the origin-destination matrix on the idea of iterated simulation, which means there are repetitive simulations, and drivers choose routes based on the cost of using the routes of previous simulations (Russo, 2007). For this study, static routing was adopted for the model. The same skeleton network was built in SYNCHRO 6 light as well, and all the basic data, such as traffic, pedestrians, signal timings, and lane width, were inputted in the model. There was a change in traffic pattern throughout the day, and the signal timings were changed to address the unique traffic pattern. As signal optimization cannot be conducted in VISSIM, the signal timings were optimized in SYNCHRO. As SYNCHRO 6 light can optimize up to 10 intersections, SYNCHRO 7 was used to optimize the network. There was a reduction in delays and fuel consumption with the use of improved signal timings. The optimized signal timings were then imported into the VISSIM model. The stops and yields sign along the study network were also created in the model. Figure 4-7: Network in SYNCHRO 50

59 After importing all signal timings, transit lines and transit stops were created. The required transit information, such as dwell time distribution, the schedules of transit buses, and the number of boarding and alighting passengers, were provided by Calgary Transit and the City of Calgary. All the information was inputted in the model. In VISSIM, the dwell time distribution can be either a normal distribution or empirical distribution with minimum and maximum values. The empirical distribution was chosen and the minimum and maximum values of the observed dwell times were provided based on the APC data obtained from Calgary Transit. The base network was then ready. The figure below shows the transit stops and transit lines drawn in VISSIM. The yellow line represents the transit line, and the red line represents the bus stops. Figure 4-8:VISSIM network showing transit stops and transit lines transit priority logic 51

60 4.6 Calibration and Validation of the Model Simulation models need to be calibrated before they are used to test future scenarios since an uncalibrated model might lead to skewed and biased results [Smith et.al, 2009]. Calibration is a process of modifying the values of some parameters so that results from the simulation are closer to the real observed data. It is an iterative process and may need thousands of iterations before obtaining the desired output. Consequently, it is necessary to select the parameter that has a substantial effect on model output. The car following parameters in VISSIM have some default values and can be changed to obtain the simulation results that are closer to the observed values. The car following parameters were changed: standstill distance (the distance between two successive vehicles when stopped), headway time, and the following variation, which restricts how much more distance than the desired safety distance a driver allows before he intentionally moves closer to the car in front (Wu Zhizhou et al. (2005). Car travel time from INRIX was used as the calibration parameter. INRIX is a company that provides various traffic information such as historical, real-time traffic information, travel time, and traffic forecasting. Model output was then compared with the field car travel times to check the closeness of the data from the two sets Determining required number of simulation runs It is important to determine the minimum number of simulation runs required to obtain reliable results. The randomness of the simulation results depends on the random seed number used during the simulations. The random vehicle properties in each simulation are based on the seed number used. Therefore, due to this variance in simulation runs, multiple numbers of runs with 52

61 different seed numbers were required to estimate the mean result from the simulations with a certain level of confidence. Standard deviation, confidence level, and the confidence interval are required to estimate the required number of simulation runs. Initially, the number of simulation runs required was unknown; therefore, 20 simulation runs were performed and a 95% of confidence level was selected. The confidence intervals represented the 5% value above and below the target mean value in this case. The standard deviation for the preliminary data was calculated using the following equation: (X x)2 Var = N 1 S.D= Var Where, Var= Variance of the sample S.D= Standard deviation of the sample N= Number of simulation runs X= Individual model output X= Average of model output The minimum number of simulations required was calculated using the following equation: 53

62 S.D CI (1 α)% =2 t (1 α), N 1 N.(ix) Where, CI (1 α)%= Confidence interval. For this study, it was chosen as 5%. t (1 α), N 1 = t-statistics value. From the preliminary simulation runs, the following results were obtained: Table 4-3: Preliminary results of VISSIM model Run Simulated Travel Time(Sec) Run Simulated Travel Time(Sec) The mean, standard deviation and confidence interval is reported in Table 4-4. Table 4-4: Mean and variance of travel time for the uncalibrated model Simulated Travel Time (South Bound) Mean Variance S.D % of mean Equation (1) must be solved by an iterative method to determine the minimum number of simulations required. We started by assigning the number of a simulation as 2, finding the corresponding t- value, and obtaining a confidence interval from equation ix. Another iteration was carried out by assigning N=3 and the iterations continued by increasing the value of 54

63 N by 1 until we obtained a confidence interval of less than the 5% of mean ( for Southbound). The iteration is shown in Table 4-5 below. Table 4-5: Determination of the number of runs required for the model N t-value CI

64 From the table, it is clear that at least 17 runs are required to obtain statistically significant results (CIN=17<28.008). Consequently, the initial approach of 20 simulation runs was sufficient Calibration Parameters There are ten input parameters needed to calibrate the Wiedemann 99 car following model. The parameters are as follows (VISSIM manual): 1. CCO (Standstill distance): the distance between two successive vehicles when stopped. 2. CC1 (Headway time): the desired headway in seconds between the lead and following vehicles. 3. CC2 (Following variation): the time in seconds the driver starts to decelerate to reach the safety distance. 4. CC3 (Threshold for entering following ): restricts how much more distance than the desired safety distance a driver allows before he intentionally moves closer to the car in front. 5. CC4 (Negative following thresholds): controls the negative speed differences during the following state. Smaller values result in more sensitive reactions of drivers to acceleration or deceleration. 6. CC5 (Positive following thresholds): controls the positive speed differences during the following state. Smaller values result in more sensitive reactions of drivers to acceleration or deceleration. 56

65 7. CC6 (Speed dependency of oscillation): the influence of distance on speed oscillation while in the following process. Larger values lead to a greater speed oscillation with increasing distance. 8. CC7 (Oscillation acceleration): the actual acceleration during the oscillation process. 9. CC8 (Standstill acceleration): the desired acceleration when starting from a standstill. 10. CC9 (Acceleration at 80 km/h): the desired acceleration at 80 km /hr. It is necessary to determine which of the above mentioned parameters are more sensitive and have substantial impacts on traffic operations. From the literature, it is found that the parameters CCO, CC1, and CC2 are more sensitive (Wu Zhizhou et al. (2005). The default value and range according to Park et al. (2006) are shown in the table below. Table 4-6: Range of VISSIM parameters Parameter Default value Range CC0 1.5 m 1 m to 3 m CC1 0.9 s 0.5 s to 3 s CC2 4 m 0 to 15 m These parameters were then changed by a trial and error method to obtain simulated results as close as possible to the field data. The southbound travel time obtained from the simulation for a car was then compared with the car travel time obtained from INRIX. The INRIX data was collected from March 17 - March 23, The table below reports the different trial values of CC0, CC1, CC2, the corresponding southbound travel time, and the % error in southbound travel 57

66 time compared to the INRIX data. For each trial value, 20 simulation runs were performed with different random seeds. Table 4-7: Trial and error calibration of the car following model 58

67 The acceptable error for travel times in the microsimulation is within 15% ( Although the percentage error in travel time was less than 10% for the default values, it failed in the t-test for mean with unequal variance at a 95% confidence interval. The trial and error gave different values for CC0, CC1, and CC2. The result of trial 12 was accepted as it had a minimum % error and was accepted statistically by the t-test. The field data was obtained from INRIX. The raw data was then used to find the car travel time between the desired sections. 74 observations were obtained from INRIX and 20 simulation runs were performed. The result of the t-test is shown in Table 4-8. Table 4-8: t-test t-test: Two-Sample Assuming Unequal Variances Field Simulation Mean Variance Observations Hypothesized Mean Difference 0 Df 86 t Stat 0.15 t Critical two-tail

68 4.6.2 Validation of the model Validation is a process of checking whether the calibrated model replicates real field behaviors. Bus travel times were used as a measure to validate the model. The bus travel times from the calibrated model and field data were compared. The field bus travel time data was collected using GPS for three weekdays during morning peak periods. The variation of bus travel time between the field and simulation results is shown in Table 4-9. The results of the calibration were found to be less than 10% different compared with the field data for all sections. A student t-test was performed to check whether the field and simulation data were significantly different or not. Out of the 8 sections considered, only 3 sections displayed data that were statistically different. Table 4-9: Comparison of field and simulation travel time 60

69 5. Results and Discussion The objective of this chapter is to evaluate the existing TPM and the passenger based TSP. The results for different scenarios are averaged from the twenty simulation runs with different random seeds. A summary of the findings and a discussion are also included at the end of this chapter. 5.1 Simulation Scenarios The simulation was run for different scenarios with varying priority methods for transit buses. As we did not have data on what percentage of buses asks for priority, it was assumed that all the buses ask for priority whether they are on schedule, ahead of schedule, or later than scheduled. The examined scenarios were as follows: 1. No priority: this is a hypothetical scenario that assumes that TSP is turned off on the examined corridor. 2. Existing priority: every three minutes one transit vehicle gets priority whether it is on schedule or not. 3. Unconditional priority: every transit vehicle that requests priority gets priority whether they are on schedule or not. 4. Passenger based conditional priority: only those buses that have the minimum number of passengers get priority. 5.2 Measures of effectiveness Measures of effectiveness are needed to measure the results, evaluate the performance of the examined priority measures, and examine whether the objectives and desired outputs are met. The measures of effectiveness used for this study are as follows: 61

70 1. Bus travel time (in sec) 2. Side street and on street delay (in sec) 3. Network performance such as average speed of the bus, average delay per bus, and an average number of stops per bus 5.3 Results Bus travel time Average bus travel times were measured using the VISSIM simulator along the bus routes from the entry point of a bus to the exit point. Different travel time sections were defined. The travel time on the entire corridor was also reported. The results are reported in Table 5-1 and show that there is a decrease in travel time for buses when priority is provided. However, the results differ based on the examined scenario. Compared to the existing priority, the conditional passenger-based TSP increased travel time by 0.2% and reduced travel time by 5.4% for northbound and southbound traveling buses, respectively. The t-test showed that the decrease in travel time for southbound buses is statistically significant, but the result was not significant for northbound traveling buses. Although the result was not statistically significant, this increase in travel time might be attributed to the implementation of reversible lanes during the morning peak period (i.e. only one lane northbound versus 3 lanes southbound). The unconditional priority resulted in the most significant reduction in bus travel time, which was expected. The resulting reductions in travel time in terms of unconditional TSP when compared to the no TSP scenario is 7.3% and 12.1% for northbound and southbound buses, respectively; these results were statistically significant. The results indicate the provision of a 62

71 new priority tends to decrease the travel time for buses. Table 4-1 shows the travel times and variance for different scenarios. If we analyze each section separately, 6 out of 8 sections have decreased travel times with the introduction of passenger-based TSP compared to the existing one. However, the results showed a higher variance for the travel time variance with the passenger-based TSP compared to the existing priority on 4 out of the 8 sections and also on the entire corridor, for both directions. However, these results differ from section to another. If we compare the three priority scenarios, unconditional priority has less variance in travel time. The higher variance in passenger-based priority may be because only those buses fulfilling the minimum number of passengers get priority rather than one bus every 3 minutes. Table 5-1: Travel time and its variance for different scenarios Delay Both side street delay and major street delay are considered in this study. A major street is normally the street carrying the higher volume of traffic, whereas the side street has a lower 63

72 volume of traffic. In this study, the north-south direction of Centre Street is considered as the major street, and all the crossing streets are considered as side streets. Side Street Delays This section reports the effects of various bus priorities on crossing street traffic. The crossing street delay for major intersections for different scenarios is shown in the figure below. Figure 5-1: Side street delay at different sections 64

73 As expected, all TSP types resulted in delays for the crossing street traffic. However, these delays vary significantly depending on the TSP logic. Figure 5-1 shows that in most sections (14 out of 20) unconditional priority would result in the most significant delays for crossing street traffic. Compared with a no TSP scenario, reduced delays on the crossing streets were obtained for 18 and 17 sections out of 20 sections for the cases of conditional passenger-based TSP and existing priority, respectively. The increase in delays varied between 3 to 131 seconds for the existing priority condition, 1 to 137 seconds for conditional passenger-based TSP, and 3 to 172 seconds for unconditional priority. Thus, compared with existing priority, conditional passengerbased TSP would result in further delays at some sections, but these delays are not considerable. Major Street Delays Figure 5-2 compares the delays at different sections in different examined TSP scenarios. Figure 5-2: Major street delay at different sections 65

74 As expected, the delays for major street traffic decreased due to the introduction of priorities for buses. The results showed that, for 7 out of 10 sections, the delay resulting from the passengerbased TSP decreased compared to the delays corresponding to the existing priority. This result indicates that the delay along the prioritized approach is lower when passenger-based priority is used rather than the existing priority. As there are many bus routes operating along the Centre Street corridor, during the peak period, more than one bus might arrive in the intersection in the span of 3 minutes. According the current priority rules, in this case, only one bus gets priority every three minutes and the other buses have to wait at the intersection. In contrast, with the passenger-based priority, more than one bus can get priority within the span of 3 minutes, which results in fewer delays along the major street. Average total delay per person (in seconds), not including passenger stop times at transit stops Figure 5-3 shows the average total delay per person (in seconds), not including passenger stop times at transit stops. Figure 5-3: Average delay per person Figure 5-3 shows that 18 out of 20 sections increased in the delay per person on the side street if conditional priority was implemented. However, when existing priority and passenger-based 66

75 priority were compared, there was an increase in side street delay per person at 11 intersections out of 20 sections with the implementation of passenger- based priority. As more passengers travel on the major street, there might be a slightly longer delay for passengers on the side street, but there was less delay on the major street as well with the implementation of passenger-based priority. The increase in delays per person varied between 3 to 91 seconds, 1 to 83 seconds, and 3 to 97 seconds for the cases of existing priority condition, conditional passenger-based TSP, and unconditional priority, respectively. Network Performance The performance of buses on an overall network (one intersection on either side of Centre Street was considered in the model) was compared to evaluate the different simulation scenarios in terms of speed, the number of stops per vehicle, and average delay per vehicle. Speed of Buses Figure 5-4 shows the average speed of buses in the network for all considered scenarios. 67

76 Figure 5-4: Average speed of buses in the network Compared to the without priority scenario, the speed of buses increased in the network for all examined priority logics. The speed increased by 1.1%, 6%, and 1.2% for existing priority, unconditional priority, and passenger-based TSP, respectively. A student t- test was conducted to check whether the data for different scenarios were different or not. For all the data pairs other than existing priority and priority based on passengers, the result showed that the improvement in bus travel speeds are statistically significant. The speeds were higher when unconditional priority was provided, which was expected because all buses got priority and, thus, traveled more smoothly with a decrease in acceleration/deceleration. The speed for the existing priority scenario and passenger-based priority were statistically indifferent. 68

77 Number of Stops per Vehicle for Buses The figure below shows the number of stops per vehicle for buses. Figure 5-5: Average numbers of stops for buses in the network With the introduction of all types of TSP, the number of stops made by the buses decreased. The number of stops were reduced by 17.1%, 27.7%, and 19.5% using existing priority, unconditional priority, and priority passenger-based TSP, respectively. The t-test showed that these results were statistically significant for all scenarios. There were fewer stops for passengerbased priority compared to the existing priority; this decrease might be due to the fact that during the morning peak, the number passengers were high and more buses were likely to get priority every cycle rather than every 3 minutes as in the existing priority. 69

78 Average Delay per Bus The figure below shows the average delay per vehicle (bus) in the network. Figure 5-6: Average delay per vehicle (bus) in the network The average delay for buses decreased by 5.9%, 20.5%, and 7.1% for the existing priority scenario, unconditional priority scenario, and priority based on the number of passengers on the bus, respectively. A student t-test was conducted, and it showed that the decreases in bus delays were statistically significant. The average delay per vehicle for the network was less for unconditional priority; this result may due to the fact that the vehicular volume was more in the major street and, with unconditional priority, both buses and other vehicles were able to also pass the intersection during the extended green. 70

79 6. Transit Priority Measures along Centre Street North: Passenger Survey 6.1 Background As large numbers of riders use public transit, it is important to know their perception towards the existing priority and their perceived experience of TPM in terms of making transit faster, more reliable, and more attractive. Travel time and reliability are two important measures of service quality. Riders satisfaction can be defined as the overall level of attainment of a customer s expectations, measured as the percentage of the customer expectations, that have actually been fulfilled (Tyrinopoulos and Antoniou, 2008, p: 260). In this study, both online and paper surveys were conducted to examine the perceptions of users towards the existing priority measures along the Centre Street corridor. The study aimed to determine if the riders were aware of the priority for buses in the corridor, their satisfaction in terms of different attributes, and their willingness to use transit more often. Depending on the preferences of the people and the type of the trip and travel conditions, the value of travel time varied accordingly (Todd Litman, 2008). 6.2 Data collection and Research methodology The attitudes and perceptions of riders towards transit priority measures along the Centre Street corridor were examined. The revealed preference (RP) survey intended to determine what differences riders perceived with the implementation of various priority measures for buses. It also examined whether respondents were using transit more often as a result of TPM. The survey was conducted by providing scenarios to the respondents. The survey included 18 questions to gather information on demographic characteristics (age, gender, income), trip 71

80 characteristics (purpose of trip, mode of travel, time of day, decisions to ride public transit), service attributes as compared with other routes, respondents perception towards transit priority measures on the corridor, and the encouragement to use public transit. The question relating to the number of years the respondent has been using the corridor was also included. The survey was conducted on the buses and at bus stops along the Centre Street North corridor in Calgary between July 10 and August 15, It was carried out during the days except the days with bad weather. Only transit passengers of the study corridor were chosen as survey respondents. Those who did not use the study corridor may not have known about transit priority measures and may not have noticed any difference in travel time and smoothness of the ride with the introduction of a priority measure. Determining a sample size is one of the key parts in any kind of survey as the sample size affects the results of the survey. A higher sample size might lead to more cost, whereas lower sample size might lead to insignificant results. There is no fixed rule to determine the minimum sample size. The minimum sample size required according to Bartlett et al. (2001) can be obtained from the following equation: N= t 2 *(p)*(q)/ (d) 2 Where N is the sample size; t = z-value for particular interval; (p)(q) = Estimate of variance =.25; (Maximum possible proportion (.5) * 1-maximum possible proportion (.5) produces maximum possible sample size). d= sampling error. Considering a 95% confidence interval and a 5% sampling error, we get N=

81 Both online and field surveys were conducted. During the field survey, individuals at the bus stop or on the bus were approached and asked whether they would like to participate in the survey. Those respondents who agreed were given the survey questionnaire. They were also informed that they could withdraw from the survey at any time even after starting to fill out the questionnaire. A total of 401 responses were collected with 254 field responses (63.35% of total responses) and 147 online responses (36.65% of total responses). The average time for respondents to complete the survey was 10 minutes. The technical and ethical approval to conduct the study was received from the Office of Research Ethics at the University of Calgary. Verbal consent was received from each participant in the study following the ethical norms and values. An electronic copy of the sample survey questionnaire can be found in APPENDIX C. Data records were checked and rechecked after the collection of the data to find and rectify errors. Data was coded and edited, and then the data were entered in datasheet created in IBM SPSS statistics 22. The survey included the following question: Do the existing transit priority measures along the Centre Street corridor (Routes 3, 300, and 301) encourage you to use public transit more often? The responses were presented in a 5 point Likert scale as: strongly agree, agree, neutral, disagree, and strongly disagree. A multinomial regression model was developed to examine the factors affecting the use of transit as a result of TPM measures implemented on the corridor. The odds ratio was used to determine the strength of association between dependent and independent variables. 73

82 6.3 Descriptive Statistics Results Demographics Table 6-1 summarizes the demographic characteristics of the respondents. The results showed that 53.87% of the respondents were male, 42.64% were female, and 3.49% preferred not to disclose their gender. As per Calgary Transit customer survey reports (2013), there were 52% female and 48% male respondents. The over representation of the males in the survey may be explained by the fact that the survey conducted by Calgary Transit was for the whole city and this one was focused only on Centre Street with most of the trips destined downtown. A considerable number of participants in the survey were under 55 years of age. The respondents pattern in terms of age closely matched the survey conducted by Calgary Transit. The sample showed the family income of the respondents varied; the majority of respondents had average annual income of $35,000 and over. Variables Table 6-1: Demographics of the sample Frequency (%) Variables Frequency (%) Gender Income Male Under $ Female $15000-$ Not responded 3.49 $25000-$ Age $35000-$ yrs $50000-$ yrs $75000-$ yrs $ or more yrs Not responded yrs or over 4.00 Not responded

83 6.3.2 Important decision to ride public transit Respondents were asked what the important factors that made them use public transit were. The price of parking (23.66%) was found to be the main factor, but other factors, such as avoiding traffic congestion, unavailability of car, do not drive, better for the environment, and the price of fuel, also had a considerable effect on their decision. The high responses that related transit use to parking fare is expected as most of the buses on Center Street pass through Downtown Calgary, which has one of the highest parking rates in North America. The graph below shows the percentage of factors contributing to riding public transit from the sample of collected data. The values shown in the graph are in percentages. Important decision to ride public transit Others 1.25 Employer pays for all or part of transit pass 3.07 Faster travel time 9.29 Ability to read/ work while riding No car available/do not drive Better for environment Price of fuel 9.48 Price of parking Avoid traffic congestion Figure 6-1: Important decision to ride public transit 75

84 6.3.3 Comparison of survey attributes Respondents were asked to compare some perceived performance measures related to riding the bus on Centre Street with other routes they use often. The attributes were perceived number of times the bus stopped at the signal, perceived waiting time at stops, perceived average travel speed, and overall satisfaction. A five point Likert scale was used with 1 representing much more perceived improvements in the above. The majority of the respondents indicated a positive response as 40.89% were more satisfied with the routes on the Center Street corridor, whereas 40.52% did not find any differences compared to other routes they used. In terms of perceived number of times the bus stopped at signals and waiting time at stops, the majority of the people did not find any difference. However, 38.03% of respondents associated a higher travel speed of buses on the Center Street corridor compared to other routes. Overall Satisfaction Much more Number of stops at the signals More Equal Waiting time at stops Less Very less Average travel speed % 20% 40% 60% 80% 100% Figure 6-2: Comparison of perceived measures of effectiveness with other routes 76

85 T-statistics were used to check the null hypothesis, which was whether or not the four attributes were statistically different from those saying equal (i.e. those saying they don t find any difference in the given attributes compared with other routes). The t-value is shown in the table below. Table 6-2: Student t-test to compare attributes with other routes The results showed that overall satisfaction and average travel speed were statistically different from those saying equal, whereas the number of stops at signals and waiting time at stops were not different from those saying no changes in the attributes Rating of Transit priority measures Respondents were asked to rate the different priority measures contribution to more enjoyable trips. They were asked to rate the contributions on the Likert scale, where 1 represented the most contributing and 5 represented the least contributing. The priority measures they were asked to rate were TSP, HOV lanes, bus only lanes, queue jumps, and bus only crossings. Bus only lanes got the lowest rating of 1.87, while bus only crossing received the highest rating of 3.77, which indicated that respondents felt that bus only lanes had the highest contribution towards the perception of enjoyable trips and bus only crossing contributed the least. The ranking is shown in the following bar graph. 77

86 Bus only crossing 3.77 Queue jump 3.46 Bus only lanes 1.87 HOV lanes 3.38 Traffic signal priority Figure 6-3: Average ratings for different priority measures An ANOVA test was used to check whether the mean scores for different priority measures contributing to more enjoyable trips were significantly different or not. The ANOVA test showed that the null hypothesis mean scores were not significantly different was rejected (F=121.51> Fcri (2.378), (p =1.3E-89) < 0.05)), which indicated that at least one priority measure had a significantly different contribution towards trip enjoyment. 78

87 Table 6-3: Descriptive statistics for priority ratings Groups Count Sum Average Variance Transit signal priority HOV lanes Bus only lanes Queue jumps Bus only crossings Table 6.4 shows the significance level for pairwise comparisons for priority ratings. The results showed that there was a significant difference in ratings among TSP, bus only lanes, and bus only crossings (p<0.05), but no significant differences between HOV lanes and queue jumps (p=. 428) as p>

88 Table 6-4: Student t-test for pairwise comparisons of priority measures Awareness of priority on Center Street Table 6-5 below shows the percentage of people who are aware of the provision of bus priority measures in the study area % of the respondents were aware that buses have priority over other traffic for some sections of Centre Street, whereas % were unaware of the priorities given to transit buses. Out of those respondents who were aware, 40.05% of them knew about bus only lanes, while only 10.66% knew about queue jumps. 80

89 Table 6-5: Awareness of priority Priority Measures Frequency (%) Transit signal priority Bus only lanes Queue jumps HOV lanes Bus only crossings TPM as providing incentive to use public transit more often The results showed that 21.91% of people strongly agreed and 43.32% of respondents agreed that the existing priority measures encouraged them to use transit more, whereas 27.46% of respondents neither agreed nor disagreed. Do the existing priority measures along the the Centre Street corridor encourage you to use public transit more? Strongly agree Agree Neither Disagree Strongly disagree Figure 6-4: Encouragement to use transit more 81

90 The hypothesis that whether the encouragements in respondent to use transit more due to existing priorities were different from those who responded as neither was tested. The results showed that the number of respondents who agreed or disagreed were statistically different (t=-16.6) from those who responded neither (t-critical value is 1.96 for a 95% confidence interval). 6.4 Multinomial Logistic Regression Using public transit more because of the existing priority measures Respondents were asked if the existing TPM encouraged them to ride public transit more often. The options given to them were strongly agree, agree, neither, disagree and strongly disagree. Since the likely actions selected by the respondents were nominal in nature, the multinomial logit model was selected for statistical modeling. Multinomial logistic regression is an extension of binomial logistic regression and is used when the dependent variable has more than two levels and is nominal in nature. The probability of the respondent being involved in an action i, can be defined as: Pni=P (Uni > UnI) Where, Uni is a function that determines the likelihood of respondent n, being involved in action i, and i is the set of mutually exclusive actions available to the respondent. If Uni is assumed to be linear then, Uni = βi Xni + εni Where, βi is a vector of estimable coefficients; 82

91 Xni represents the measurable characteristics that can determine the categories of the possible actions; εni is an error term that is used to address the unobserved factors that influence actions taken by individuals. This assumption leads to the multinomial logit model expressed as follows: Pn(i)= eβi Xni I eβi Xni i=1,i=1,.,i Where, Pn(i) is the logit function for respondent n choosing action i. The coefficients βi can be estimated by the method of maximum likelihood. A multinomial regression model was developed in SPSS on encouragement to ride transit more [i.e. 1= agree (grouping strongly agree and agree), 2= disagree (grouping disagree and strongly disagree), and 0= neither]. The table below describes the independent variables that had an effect on the model. As shown in Table 6-6, almost all of the explanatory variables are nominal and selfexplanatory. In addition, they can only take a value of 1 or 0, so they are also known as dichotomous variables. Some variables are grouped into one factor categories since they describe the possible attributes of a factor. 83

92 Table 6-6: Description of variables Description of variables Variables Park and ride Transit with walk or bike( bus) Car driving Better for environment Are buses arriving earlier than scheduled time? Yes No On time Don t know Description of variables 1= main travel mode is park and ride; otherwise=0 1= main travel mode is transit with walk or bike(bus); otherwise=0 1= main travel mode is car driving; otherwise=0 1= Main decision to ride public transit; otherwise=0 1= Buses arriving earlier than scheduled time; Otherwise=0 1= Buses are not arriving earlier than scheduled time; Otherwise=0 1= Buses are arriving on time; Otherwise=0 1= don t know; otherwise=0 Buses arriving late <1 min 1= buses arriving late less than 1 min; otherwise=0 1-2 min 1= buses arriving late in between 1-2 min; otherwise=0 2-3 min 1= buses arriving late in between 2-3 min; otherwise=0 >3 min 1= buses arriving late more than 3 min; otherwise=0 Aware of bus priorities? Yes No Contribution to more enjoyable trips( HOV lanes) Aware of transit signal priorities? 1= aware of bus prioriteis; otherwise=0 1= not aware of bus prioriteis; otherwise=0 1= HOV lanes contributes to more enjoyable trips; otherwise=0 1= Aware of transit signal priority; otherwise=0 One of the most important assumptions of multinomial logistic regression is that there should be no multicollinearity. Multicollinearity was checked in SPSS by looking at the VIF (Variance Inflation Factor) values. There is no fixed value to determine the presence of multicollinearity, but values greater than 10 are often regarded as the presence of multicollinearity. The independent variables considered in the model have VIF values of less than 2. Consequently, there is no multicollinearity. Coefficients were estimated for the dependent variables. Coefficients with p-values greater than 0.05 were considered insignificant. Some variables, although insignificant, were kept in the model, as at least one of the variables in a category was statistically significant. The model fitting information showed that the final model was highly significant to the nonmodel (intercept only) as significance is 3.82E-08, which is <0.05 with -2log 84

93 likelihood= , Chi-square=88.166(DF=28). The assumption that there is no difference between the model with and without independent variables is rejected and, therefore, there is a relationship between the independent variables and the dependent variables. The independent variables have statistically improved the model compared to the model without any variables. The impact of the independent variables (factors) on the determination of the likelihood of people agreeing that existing priority measures encourage them to use public transit more is 24.4 % (Pseudo R-square, Nagelkerke=0.244). The goodness of fit test showed that our data fits the model perfectly as Pearson chissquare= , DF=314, p>0.05). The table below shows the estimated coefficients of the multinomial logistic regression. Treating the neutral response (i.e. neither agree not disagree) as the reference group, SPSS estimated the model for people agreeing and disagreeing that the existing priority measures encourage people to use transit. The multinomial model summarizes the impact of the existing TPM, travel habits, travelers experience with Calgary transit, socio-economic attributes, and the selected alternatives of the stated use of public transit. Multinomial logit model estimations are provided in Table

94 Table 6-7: Parameter estimates of the model In the following sections, the analysis is based on the multinomial logit model. Mode of travel Although it was not significant statistically, the results showed that passengers whose main mode of travel was by car-driving, a combination of transit (bus) and walk or bike, and park and ride are less likely to agree to ride transit more often compared to those who revealed using other modes (car-passenger, walk all the way, bike all the way, and a combination of walk or bike and C-train). The results also showed that those whose main mode of travel is park and ride are more likely to disagree to riding transit more often compared to those passengers who use another mode to travel. This result might be because passengers use park and ride to avoid the high cost of parking in downtown rather than finding transit fast and reliable. 86

95 Important decision to ride public transit Those passengers who ride public transit because it is better for the environment are 2.1 times more likely to use transit if TPM are implemented compared to those who use transit for other reasons. Awareness of bus priority measures The results showed that those passengers who were aware of bus priorities were 2.1 times more likely to agree that the existing priority measures encourage them to use transit more than those who were unaware. The model also showed that those who were unaware of bus priorities were 2.15 times more likely to disagree that the existing priority measures encourage them to use transit more than those who were aware; however, the latter was not statistically significant. This finding indicated that respondents use of public transit increases with their awareness of priority measures. This finding is important as it shows that familiarizing transit riders with the existence of such features has a positive effect on the use of transit. Buses arriving earlier than scheduled time The likelihood of passengers agreeing that the existing priority measures encouraged them to ride public transit more were 2.14, 2.62 and 2.50 times for those passengers who believed the bus arrived earlier, the bus did not arrive earlier, and the bus was on time respectively relative to those who did not know whether the bus arrived earlier or not. Buses arriving late The model results showed that those passengers who perceived buses arriving only one minute late were more likely to agree to use public transit more because of TPM compared to those who 87

96 thought that buses arrived 3 minutes late or more. This result indicated the importance of perception towards transit service reliability and TPM s contribution to increase ridership. Contribution to more enjoyable trips by HOV lanes Users who perceived HOV lanes as more enjoyable were less likely to ride transit more often. The possible reason may be that this group enjoyed the same priority as transit, and the group avoids the disutility of transit, such as waiting for a bus to arrive. 88

97 7. Conclusion and Future Recommendation This chapter presents the conclusions of this research as well as some suggestions for future research. This research summarized the past works on TPM, evaluated the existing transit priority measures on the Centre Street corridor in Calgary, and developed transit priority logic based on the number of passengers on a bus. The new developed passenger based transit priority logic was tested on the Centre Street corridor between 5 Avenue and McKnight Boulevard using a simulation. Different scenarios were developed in VISSIM. They were without priority, with existing priority, unconditional priority, and passenger-based priority. The results showed that TPM implementation helps to reduce bus travel time. When comparing the TSP scenarios for the existing condition and the TSP based on the number of passengers on the bus, the latter reduced travel time more. The reduction in travel time for conditional passenger-based TSP for southbound traffic during the morning peak period was 5.4% The maximum reduction in southbound travel time was found to be 12.1% for unconditional priority when compared with no priority. The reduction in travel time may lead to a reduction in operating costs, which ultimately can reduce the transit investment recovery period (Soo, Collura, Hobeika, and Teodorovic, 2004). The comparison of side street delays for different cases showed a variation in the results for different sections considered in the model. The existing priority method showed less delay compared to the TSP based on the number of passengers on the bus. This result may be due to the high morning peak ridership and, thus, buses would get priority more often than every 3 89

98 minutes as in the existing TSP. The unconditional priority caused more side street delays compared to the other TSP simulation scenarios. The increase in delay showed a variation of 3 to 131 seconds for existing priority, 1 to 137 seconds for passenger-based priority, and 3 to 172 seconds for unconditional priority. The major street delay was found to be less for TSP based on the number of passengers on the bus when compared to the existing TSP in the corridor. The overall network performance was also considered. There was a 17.1%, 27.7%, and 19.5% reduction in the number of stops for buses for existing priority, unconditional priority, and passenger-based priority, respectively. The average reduction in delay was found to be 5.9%, 20.5%, and 7.1% for the existing priority scenario, unconditional priority, and passenger-based priority, respectively. The speed of the buses also increased due to the introduction of TSP. The increases in the speed of buses for existing priority and passenger-based priority were 1.1% and 1.2%, respectively, but there was increase of 6% in the speed of buses for unconditional priority. To conclude, among all considered simulation scenarios, the TSP based on the number of passengers on the bus is more efficient. The disadvantage of unconditional priority is the large delay in crossing street traffic. One of the main purposes of this research is to find the TSP measure that can reduce the travel time of buses taking into consideration the delays in the crossing street as well. Although the delays experienced by crossing street traffic are slightly longer for the passenger-based priority compared to existing priority, the travel time, average delay in the network and average number of stops per bus is lower and the average speed of a bus in the network is higher. The revealed preference survey was also conducted for the Centre Street corridor in Calgary to determine users perceptions towards transit priority measures along the corridor. Both online 90

99 and field surveys were carried out, and a total of 401 responses were collected. The results showed that 35.5% of people were not aware that there was a priority for buses, and out of the other 64.5% who were aware of the priorities, most of them only knew about bus only lanes. Awareness of bus only lanes might be explained by the clear visibility of exclusive bus lanes compared to other priority measures. Respondents believed that bus only lanes provided more enjoyable trips as they ranked them lower (1.87) % either agreed or strongly agreed that the existing priority measures encouraged them to use transit more often. The price of parking and avoiding traffic congestion were found to be the two most important reasons behind using transit. The multinomial logistic regression model was also developed to determine if the existing priority measures encouraged people to use public transit more often. The results showed that respondents considered the existing priority measures as an incentive to use public transit more, but this result was highly dependent on their awareness of the existing priority, the reason for using transit, whether the bus arrives early or late, and the mode of transport they use regularly. The model also showed that the passengers who were aware of bus priorities are more likely to use transit more often. These findings suggest that Calgary Transit needs to promote the existing priority measures for buses to attract more ridership. This conclusion seems to be particularly important for special target groups such as transit riders who use transit for environmental reasons. However, the results showed that those passengers whose main mode of travel is by car were unwilling to use transit more. The open suggestions from those respondents show the following most frequent statements: road surface is not very good, and there is more shaking in the buses, they don t find it comfortable for doing other activities such as reading while riding 91

100 the bus, and buses are too full in the peak hours. It is suggested that Calgary transit focus on improving road conditions as well as managing the high flow of passengers during peak hours. Recommendations for Future Research This research evaluated the impacts of providing priority based on the number of passengers on a bus and considered the delay in the crossing street. It equated the delays on the non- priority approach with benefits of using the priority approach to find the minimum number of passengers required on a bus to get priority. The priority provided was green extension and red truncation. This research can be expanded upon as follows: Further research can be done to find out the benefits of transit priority in terms environmental impacts and benefits in terms of monetary value. The minimum number of passengers required was calculated by assuming the deterministic arrival and service rate of vehicles and equating the benefits and delay due to priority. This strategy can be improved as vehicle arrival is not deterministic. As technology evolves, the use of connected vehicle technology and TSP together can be more beneficial. The algorithms that consider both could be developed. 92

101 8. References [1] K. Ova, A. Traffic, and A. Smadi, Evaluation of Transit Signal Priority Strategies for Small-, no. 701, pp. 1 14, [2] V. Ngan, T. Sayed, and A. Abdelfatah, Impacts of Various Parameters on Transit Signal Priority Effectiveness, J. Public Transp., vol. 7, no. 3, pp , [3] J. Chang, J. Collura, F. Dion, and H. Rakha, Evaluation of service reliability impacts of traffic signal priority strategies for bus transit, Transit Bus, Paratransit, Rural Public Intercity Bus, New Transp. Syst. Technol. Capacit. Qual. Serv., no. 1841, pp , [4] T. Hall and K. Kim, Simulation Testing of Adaptive Control, Bus Priority and, Transp. Res., pp. 1 22, [5] C. Members, Evaluating the Transit Signal Priority Impacts along the U. S. 1 Corridor in Northern Virginia Vaibhavi Kamdar MASTER OF SCIENCE In Civil Engineering Evaluating the Transit Signal Priority Impacts along the U. S. 1 Corridor in Northern Virginia Vaibha, Transit, [6] A. Skabardonis, Control strategies for transit priority, Transp. Res. Rec., no. 1727, pp , [7] M. Menendez and C. F. Daganzo, Effects of HOV lanes on freeway bottlenecks, Transp. Res. Part B Methodol., vol. 41, no. 8, pp , [8] J. Dahlgren, High occupancy vehicle lanes: Not always more effective than general purpose lanes, Transp. Res. Part A Policy Pract., vol. 32, no. 2, pp , [9] V. T. Arasan and P. Vedagiri, Study of the impact of exclusive bus lane under highly heterogeneous traffic condition, Public Transp., vol. 2, no. 1, pp ,

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107 Appendix A Sample of traffic count report 99

108 Sample of signal timing sheet 100

109 Sample of APC data 101

110 Appendix B Sample of PUA files for VAP 102

111 Sample of VAP logic files 103

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