With the rapid growth in modern cities and congestion on major freeways and local streets, public transit services have become more and more important for urban transportation. As an important component of Intelligent Transportation Systems (ITS), Transit Signal Priority (TSP) systems have been extensively studied and widely implemented to improve the quality of transit service by reducing transit delay. The focus of this research is on the development of a platform with the physical queue representation that can be employed to evaluate and/or improve TSP strategies with the consideration of the interaction between transit vehicles and queues at the intersection.This dissertation starts with deterministic analyses of TSP systems based on a physical queue model. A request oriented TSP decision process is then developed which incorporates a set of TSP decision regions defined on a time-space diagram with the physical queue representation. These regions help identify the optimal detector location, select the appropriate priority control strategy, and handle the situations with multiple priority requests. In order to handle uncertainties in TSP systems arising in bus travel time and dwell time estimation, a type-2 fuzzy logic forecasting system is presented and tested with field data. Type-2 fuzzy logic is very powerful in dealing with uncertainty. The use of Type-2 fuzzy logic helps improve the performance of TSP systems. The last component of the dissertation is the development of a Colored Petri Net (CPN) model for TSP systems. With CPN tools, computer simulation can be performed to evaluate various TSP control strategies and the decision process. Examples for demonstrating the process of implementing the green extension strategy and the proposed TSP decision process are presented in the dissertation. The CPN model can also serve as an interface between the platform developed in this dissertation and the implementation of the control strategies at the controller level.

Transit agencies seeking to improve transit service delivery are increasingly considering the deployment of transit signal priority (TSP). However, the impact of TSP on transit service and on the general traffic stream is a function of many factors, including intersection geometry, signal timings, traffic demands, TSP strategies and parameters, transit vehicle headways, timing when transit vehicles arrive at the intersection, etc. Previous studies have shown that depending on these factors, the net impact of TSP in terms of vehicle or person delay can be positive or negative. Furthermore, due to financial constraints, transit agencies are often able to deploy TSP at only a portion of all of the candidate intersections. Consequently, there is a need to estimate the impact of TSP prior to implementation in order to assist in determining at which intersections TSP should be deployed. Currently, the impacts of TSP are often estimated using microscopic simulation models. However, the application of these models is resource intensive and requires specialized expertise that is often not available in-house to transit agencies. In this thesis, an analytical model was proposed for estimating the delay impacts of green extension and early green (red truncation) TSP strategies. The proposed model is validated with analytical model reported in the literature and microscopic simulation model. This is followed by model sensitivity analysis. A software module is developed using the proposed model. The usefulness of the model is illustrated through its application to estimate the TSP performance. Finally, a prioritization is conducted on sixteen intersections with different geometric and operational traffic strategies. The overall results indicate that the proposed model is suitable for both estimating the pre-deployment and post-deployment TSP performance. The proposed model is suitable for implementation within a spreadsheet and requires considerably less effort, and less technical expertise, to apply than a typical micro-simulation model and therefore is a more suitable tool for transit agencies to use for prioritising TSP deployment.

Transit agencies seeking to improve transit service delivery are increasingly considering the deployment of transit signal priority (TSP). However, the impact of TSP on transit service and on the general traffic stream is a function of many factors, including intersection geometry, signal timings, traffic demands, TSP strategies and parameters, transit vehicle headways, timing when transit vehicles arrive at the intersection, etc. Previous studies have shown that depending on these factors, the net impact of TSP in terms of vehicle or person delay can be positive or negative. Furthermore, due to financial constraints, transit agencies are often able to deploy TSP at only a portion of all of the candidate intersections. Consequently, there is a need to estimate the impact of TSP prior to implementation in order to assist in determining at which intersections TSP should be deployed. Currently, the impacts of TSP are often estimated using microscopic simulation models. However, the application of these models is resource intensive and requires specialized expertise that is often not available in-house to transit agencies. In this thesis, an analytical model was proposed for estimating the delay impacts of green extension and early green (red truncation) TSP strategies. The proposed model is validated with analytical model reported in the literature and microscopic simulation model. This is followed by model sensitivity analysis. A software module is developed using the proposed model. The usefulness of the model is illustrated through its application to estimate the TSP performance. Finally, a prioritization is conducted on sixteen intersections with different geometric and operational traffic strategies. The overall results indicate that the proposed model is suitable for both estimating the pre-deployment and post-deployment TSP performance. The proposed model is suitable for implementation within a spreadsheet and requires considerably less effort, and less technical expertise, to apply than a typical micro-simulation model and therefore is a more suitable tool for transit agencies to use for prioritising TSP deployment.

Traffic signal control systems have been tremendously improved since the first colored traffic signal light was installed in London in December 1868. There are many different types of traffic signal control systems that can be categorized into three major control types: fixed-time, actuated, and adaptive. Choosing a proper traffic signal system is very important since there exists no perfect signal control strategy that fits every traffic network. One example is traffic signal coordination, which is the most widely used traffic signal control system. It is believed that performance measures, such as travel times, vehicle delay, and number of stops, can be enhanced by synchronizing traffic signals over a corridor. However, it is not always true that the coordination will have the same benefits for all the traffic in the network. Most of the research on coordination has focused only on strengthening the major movement along the coordinated routes without considering system-wide impacts on other traffic. Therefore, before implementing a signal control system to a specific traffic network, a thorough investigation should be conducted to see how the control strategy may impact the entire network in terms of the objectives of each type of traffic control system. This dissertation first considers two different kinds of systematic performance analyses for traffic signal control systems. Then, it presents two types of signal control strategies that account for current issues in coordination and priority control systems, respectively. First, quantitative analysis of smooth progression for traffic flow is investigated using connected vehicle technology. Many studies have been conducted to measure the quality of progression, but none has directly considered smooth progression as the significant factor of coordination, despite the fact that the definition of coordination states that the goal is to have smooth traffic flow. None of the existing studies concentrated on measuring a continuous smooth driving pattern for each vehicle in terms of speed. In order to quantify the smoothness, this dissertation conducts an analysis of the speed variation of vehicles traveling along a corridor. A new measure is introduced and evaluated for different kinds of traffic control systems. The measure can be used to evaluate how smoothly vehicles flow along a corridor based on the frequency content of vehicle speed. To better understand the impact of vehicle mode, a multi-modal analysis is conducted using the new measure. Second, a multi-modal system-wide evaluation of traffic signal systems is conducted. This analysis is performed for traffic signal coordination, which is compared with fully actuated control in terms of a systematic assessment. Many optimization models for coordination focus mainly on the objective of the coordinated route and do not account for the impacts on side street movements or other system-wide impacts. In addition, multi-modality is not considered in most optimized coordination plans. Thus, a systematic investigation of traffic signal coordination is conducted to analyze the benefits and impacts on the entire system. The vehicle time spent in the system is measured as the basis of the analysis. The first analysis evaluates the effect of coordination on each route based on a single vehicle mode (regular passenger vehicles). The second analysis reveals that how multi-modality affects the performance of the entire system. Third, in order to address traffic demand fluctuation and traffic pattern changes during coordination periods, this dissertation presents an adaptive optimization algorithm that integrates coordination with adaptive signal control using data from connected vehicles. Through the algorithm, the coordination plan can be updated to accommodate the traffic demand variation and remain optimal over the coordination period. The optimization framework consists of two levels: intersection and corridor. The intersection level handles phase allocation in real time based on connected vehicle trajectory data, while the corridor level deals with the offsets optimization. The corridor level optimization focuses on the performance of the vehicle movement along the coordinated phase, while at the intersection level, all movements are considered to create the optimal signal plan. The two levels of optimizations apply different objective functions and modeling methodologies. The objective function at the intersection level is to minimize individual vehicle delay for both coordinated and non-coordinated phases using dynamic programming (DP). At the corridor level, a mixed integer linear programming (MILP) is formulated to minimize platoon delay for the coordinated phase. Lastly, a peer priority control strategy, which is a methodology that enhances the multi modal intelligent traffic signal system (MMITSS) priority control model, is presented based on peer-to-peer (P2P) and dedicated short range communication (DSRC) in a connected vehicle environment. The peer priority control strategy makes it possible for a signal controller to have a flexible long-term plan for prioritized vehicles. They can benefit from the long-term plan within a secured flexible region and it can prevent the near-term priority actions from having a negative impact on other traffic by providing more flexibility for phase actuation. The strategy can be applied to all different modes of vehicles such as transit, freight, and emergency vehicles. Consideration for far side bus stops is included for transit vehicles. The research that is presented in this dissertation is constructed based on Standard DSRC messages from connected vehicles such as Basic Safety Messages (BSMs), Signal Phasing and Timing Messages (SPaTs), Signal Request Messages (SRMs), and MAP Messages, defined by Society of Automotive Engineers (SAE) (SAE International 2016).

With many advances in transportation technology, micro-simulation models have proven to be a useful tool in transportation engineering alternative analyses. Micro-simulation software packages can be used to quickly and efficiently design new transportation infrastructure and strategies, while helping transportation planners and traffic engineers identify possible problems that might arise in a particular design alternative. Over the years these simulation packages have become more advanced, and their capabilities in terms of modeling complex, intricate intersections and producing useful outputs for analysis have increased. Today’s simulations can reproduce many facets of transportation design alternatives while generating outputs that help increase efficiency, reduce cost, optimize financing, and improve safety. Recently micro-simulation models have been employed in the analysis and design of alternative transit signal priority (TSP) strategies. This research reviews the similarities, differences and functional capabilities of two micro-simulation software packages: 1) VISSIM, and 2) S-Paramics. A special effort is made to discuss the usefulness of each package when used to analyze TSP alternatives for small and medium sized urban areas, where data and staff availability are typically limited. The paper includes a case study of Burlington, Vermont in which each software package is employed to evaluate several alternative TSP strategies. Each package is evaluated in terms of ease of use, usefulness of outputs, and consistency of results. The results of the evaluation are intended to guide planners and traffic engineers in small and medium urban areas in the selection of an appropriate simulation package for TSP analysis and design.

Traffic congestion has continued to grow as a result of urbanization, which is associated with an increase in car ownership. As a way to improve the efficiency of the transportation system, emerging technologies including Connected Automated Vehicles (CAVs), loop detectors, Advanced Traveler Information Systems (ATISs), and Advanced Public Transportation Systems (APTSs) are being deployed. One of the successful techniques that has demonstrated benefits for system users, operators and agencies is Transit Signal Priority (TSP). TSP favors transit vehicles in the allocation of green times at traffic signals. A successful deployment of TSP depends on different factors including the prediction of various components of transit vehicle travel times to predict when a vehicle would arrive at a traffic signal. Current TSP state-of-the-art and state-of-practice disregards the impact of bus stops, transit vehicle characteristics, driver, and the prevailing traffic conditions on the predicted arrival time of transit vehicles at traffic signals. Considering these factors is important the success of TSP hinges on the ability to predict transit vehicle arrival times at traffic signals in order to provide these vehicles with priority service. The main contribution of this research effort relates to the modeling of the various components of transit vehicle travel times. This model explicitly captures the impact of passengers, drivers and vehicle characteristics on transit vehicle travel times thus providing better models for use in various transit applications, including TSP. Furthermore, the thesis presents a comprehensive understanding of the determinants of each travel time component. In essence, the determinants of each component, the stochasticity in these determinants and the correlation between them are explicitly modeled and captured. To achieve its contribution, the study starts by improving the current state-of-the-art and state-of-practice transit vehicle boarding/alighting (BA) models by explicitly accounting for the different factors that impact BA times while ensuring a relatively generalized formulation. Current formulations are specific for the localities and bus configurations that they were developed for. Alternatively, the proposed BA time model is independent of the transit vehicle capacity and transit vehicle configuration (except for the fact that it is only valid for two-door buses – a separate door for alighting and boarding the bus) and accounts for the number of on-board passengers, boarding and alighting passengers. The model also captures the stochasticity and the correlation between the model coefficients with minimum computational requirements. Next the model was extended to capture the bus driver and vehicle impacts on the transit vehicle delay in the vicinity of bus stops, using a vehicle kinematics model with maximum speed and acceleration constraints to model the acceleration/deceleration delay. The validation of the model was done using field data that cover different driving conditions. Results of this work found that the proposed formulation successfully integrated the human and vehicle characteristics component in the model and that the new formulation improves the estimation of the total delay that transit vehicles experience near bus stops. Finally, the model was extended to estimate the time required to merge into the adjacent lane and the time required to traverse a queue upstream of a traffic signal. The final part of this study models the bus arrival time at traffic signal using shockwave and prediction model in a connected environment. This section aims to model the transit vehicle arrival time at traffic signal considering the impact of signal timing and the prevailing traffic conditions. In summary, the proposed model overcomes the current state-of-the-art models in the following ways: 1) it accounts for the vehicle capacity and the number of on-board passengers on bus BA times, 2) it captures the stochasticity in the bus stop demand and the associated BA times, 3) it captures the impact of the traffic in modeling the delay at a bus stop , 4) it incorporates the driver and vehicle impact by modeling the acceleration and deceleration time, and 5) it uses shockwave analysis to estimate bus arrival times through the use of emerging technology data. Through statistical modeling and evaluation using field and simulated data, the model overcomes the current state-of practice and state-of art transit vehicle arrival time models. / Doctor of Philosophy / Traffic congestion grows rapidly causing increment in travel time, reducing travel time reliability, and reducing the number of public transportation riders. Using the Advanced Public Transportation Systems (APTS) technology with Advanced Traveler Information Systems (ATISs) helps in improving transportation network travel time by providing real-time travel information. One of the successful techniques that has demonstrated benefits for system users, operators and agencies is Transit Signal Priority (TSP). A successful deployment of TSP depends on different factors including the prediction of various components of transit vehicle travel times to predict when a vehicle would arrive at a traffic signal. Current TSP state-of-the-art and state-of-practice disregards the impact of bus stops, transit vehicle characteristics, driver, and the prevailing traffic conditions on the predicted arrival time of transit vehicles at traffic signals. The difficulty of modeling the various determinants of the transit vehicle travel time as explicit variables rather than include some of them are implicitly modeled due to two main reasons. First, there are various significant factors affecting estimating the transit vehicle arrival time including; the passenger demand at bus stop, driver characteristics, vehicle characteristics and the adjacent prevailing traffic conditions. Second, the stochasticity and the fluctuation nature of each variables as they differ spatiotemporally. The research presented in this thesis provides a comprehensive investigation of the determinants of different transit vehicle travel time components of the transit vehicle arrival time at traffic signal leading to a better implementing of TSP. This study was initiated due to the noticeable overlooking of the different factors including human and vehicle behavior in the current state-of-practice and state-of-art which, as a result, fails to capture and incorporate the impact of these components on the implementing of TSP.

Cities worldwide are faced with the challenge of improving transit service in urban areas using lower cost means. Transit signal priority is considered to be one of the most effective ways to improve the service of transit vehicles. Transit signal priority has become a very popular topic in transportation in the past 20 to 30 years and it has been implemented in many places around the world. In this thesis, transit signal priority strategies are categorized and an extensive literature review on past research on transit signal priority is conducted. Then a case study on Columbia Pike in Arlington (including 21 signalized intersections) is conducted to assess the impacts of integrating transit signal priority and SCOOT adaptive signal control. At the end of this thesis, an isolated intersection is designed to analyze the sensitivity of major parameters on performance of the network and transit vehicles. The results of this study indicate that the prioritized vehicles usually benefit from any priority scheme considered. During the peak period, the simulations clearly indicate that these benefits are typically obtained at the expense of the general traffic. While buses experience reductions in delay, stops, fuel consumption, and emissions, the opposite typically occurs for the general traffic. Furthermore, since usually there are significantly more cars than buses, the negative impacts experienced by the general traffic during this period outweigh in most cases the benefits to the transit vehicles, thus yielding overall negative impacts for the various priority schemes considered. For the off-peak period, there are no apparent negative impacts, as there is more spare capacity to accommodate approaching transit vehicles at signalized intersections without significantly disrupting traffic operations. It is also shown in this study that it is generally difficult to improve the system-wide performance by using transit priority when the signal is already optimized according to generally accepted traffic flow criteria. In this study it is also observed that the system-wide performance decreases rapidly when transit dwell time gets longer. / Master of Science

Esta pesquisa traz à discussão a implantação de Sistemas Inteligentes de Transportes (ITS), em particular a funcionalidade Transit Signal Priority (TSP), ou Prioridade Semafórica, como uma solução a ser considerada para melhorar o desempenho de um corredor de ônibus. Os dados do Sistema Integrado de Monitoramento (SIM) foram empregados para identificar os locais com maior retardamento no Corredor Campo Limpo - Rebouças- Centro em São Paulo e, selecionado um trecho crítico, foi elaborada uma rede de microssimulação no software PTV - Vissim. A aplicação da prioridade semafórica foi feita através do VISVAP, controlador de lógica externo, no qual foram escritas as condicionantes de prioridade. O TSP foi simulado em quatro cenários distintos e, os resultados obtidos permitiram concluir que as expectativas verificadas no referencial teórico (por exemplo: aumento da velocidade média dos ônibus e automóveis), puderam ser comprovadas e, além disso, a prioridade semafórica condicional foi capaz de reduzir os retardos inclusive nas vias transversais não priorizadas. / This research brings to discussion the implementation of Intelligent Transportation Systems (ITS), particularly the Transit Signal Priority (TSP) feature as a solution to be considered to improve the performance of a bus corridor. Data from a Monitoring Integrated System (Sistema Integrado de Monitoramento - SIM) were used to identify most significant delay points at Campo Limpo - Rebouças- Centro Corridor in São Paulo and, after selected a critical stretch, it was developed a microsimulation with PTV Vissim software. The transit signal priority was made by VISVAP, external logic controller, in which were described the priority conditions. TSP was simulated in four different scenarios and, the obtained results have concluded that expectations examined in academic referencial (for example: increase in the average speed of buses and cars), could be confirmed, and, in addition, the transit signal priority was able to decrease delays in cross ways too (not prioritized).

Transit Signal Priority (TSP) is an operational strategy that can speed the movement of in-service transit vehicles (typically bus, light rail, or streetcar) through traffic signals. By reducing control delay at signalized intersections, TSP can improve schedule adherence and travel time efficiency while minimizing impacts to normal traffic operations. These benefits improve the quality of service thereby making it more attractive to choice riders. A TSP system can also allow for fewer buses on the same due to travel time reductions and increased reliability, thus reducing transit operating costs. Much of the previous research on TSP has focused on signal control strategies and bus stop placement with little of it analyzing the effectiveness of the system using actual data. This study aims to evaluate the effectiveness of the system using a bus route corridor in Portland, Oregon through real-time Automatic Vehicle Locator data. Key measures that TSP is promoted to improve are evaluated, including travel time, schedule adherence and variability. The TSP system on data was collected for two weeks and is compared to an adjacent two weeks of bus data with the TSP system turned off such that there is no skewing of data due to changes in traffic volumes or transit ridership. This research has shown, that on certain corridors there may be little to no benefit towards TSP implementation and may possibly provide some disbenefit. The direct comparison for TSP on and off scenarios completed for this research yielded no significant differences in reduction in travel time or schedule adherence performance. An additional interesting result was that the standard deviation of the results did not have any specific tendencies with the TSP on or off. Based on these findings, recommendations are made to increase the effectiveness of the system.

Esta pesquisa traz à discussão a implantação de Sistemas Inteligentes de Transportes (ITS), em particular a funcionalidade Transit Signal Priority (TSP), ou Prioridade Semafórica, como uma solução a ser considerada para melhorar o desempenho de um corredor de ônibus. Os dados do Sistema Integrado de Monitoramento (SIM) foram empregados para identificar os locais com maior retardamento no Corredor Campo Limpo - Rebouças- Centro em São Paulo e, selecionado um trecho crítico, foi elaborada uma rede de microssimulação no software PTV - Vissim. A aplicação da prioridade semafórica foi feita através do VISVAP, controlador de lógica externo, no qual foram escritas as condicionantes de prioridade. O TSP foi simulado em quatro cenários distintos e, os resultados obtidos permitiram concluir que as expectativas verificadas no referencial teórico (por exemplo: aumento da velocidade média dos ônibus e automóveis), puderam ser comprovadas e, além disso, a prioridade semafórica condicional foi capaz de reduzir os retardos inclusive nas vias transversais não priorizadas. / This research brings to discussion the implementation of Intelligent Transportation Systems (ITS), particularly the Transit Signal Priority (TSP) feature as a solution to be considered to improve the performance of a bus corridor. Data from a Monitoring Integrated System (Sistema Integrado de Monitoramento - SIM) were used to identify most significant delay points at Campo Limpo - Rebouças- Centro Corridor in São Paulo and, after selected a critical stretch, it was developed a microsimulation with PTV Vissim software. The transit signal priority was made by VISVAP, external logic controller, in which were described the priority conditions. TSP was simulated in four different scenarios and, the obtained results have concluded that expectations examined in academic referencial (for example: increase in the average speed of buses and cars), could be confirmed, and, in addition, the transit signal priority was able to decrease delays in cross ways too (not prioritized).