The dissertation presents a connected vehicle based traffic signal control model (CVTSCM) for signalized arterials. The model addresses different levels of traffic congestion starting with the initial deployment of connected vehicle technologies focusing on two modules created in CVTSCM. For near/under-saturated intersections, an arterial-level traffic progression optimization model (ALTPOM) is being proposed. ALTPOM improves traffic progression by optimizing offsets for an entire signalized arterial simultaneously. To optimize these offsets, splits of coordinated intersections are first adjusted to balance predicted upcoming demands of all approaches at individual intersections. An open source traffic simulator was selected to implement and evaluate the performance of ALTPOM. The case studies’ field signal timing plans were coordinated and optimized using TRANSYT-7F as the benchmark. ALTPOM was implemented with connected vehicles penetration rates at 25% and 50%, ALTPOM significantly outperforms TRANSYT-7F with at least 26.0% reduction of control delay (sec/vehicle) and a 4.4% increase of throughput for both directions of major and minor streets. This technique differs from traditional traffic coordination which prioritizes major street traffic, and thereby generally results in degrading performance on minor streets. ALTPOM also provides smooth traffic progression for the coordinated direction with little impact on the opposite direction. The performance of ALTPOM improves as the penetration rate of connected vehicles increases. For saturated/oversaturated conditions, two queue length management based Active Traffic Management (ATM) strategies are proposed, analytically investigated, and experimentally validated. The first strategy distributes as much green time as possible for approaches with higher saturation discharge rate in order to reduce delay. For the second approach, green times are allocated to balance queue lengths of major and minor streets preventing queue spillback or gridlock. Both strategies were formulated initially using uniform arrival and departure, and then validated using field vehicle trajectory data. After validation of the modules, the effectiveness of CVTSCM is proven. Then, conclusions and recommendations for future researches are presented at the end.

An average of 6.5 million crashes are reported to the police every year in the United States. Safety is significantly important considering the rapid increase on traffic volume on American roads. This thesis describes the development of a safety model whose primary objective is to capture the benefits of Intelligent Transportation Systems (ITS) on safety. The specific ITS component that is examined in more detail is traffic signal coordination. The model was tested in a micro-simulation environment using INTEGRATION traffic simulation model as well as in a field data evaluation. The General Estimates System (GES) database was chosen as the primary national database to extract accident data. These data were used for the development of the statistical foundation for the safety model. Crash rates were produced using extracted crash frequencies and annual vehicle miles traveled figures from the Highway Statistics (FHWA, 1997). Regression analysis was performed to predict the behavior of several crash types, as they were associated with a variety of variables, for example the facility speed limit and time the crash occurred. The model was developed in FORTRAN code that estimates the accident risk of a facility based on its free-speed. Two methods were used to test the model: 1. field data from the city of Phoenix, Arizona were used in a GPS (Global Positioning Systems) floating car that tracked the accident risk on a second by second basis. Before and after signal coordination scenarios were tested thus yielding a result that the accident risk is less in the after scenario. 2. the model was then tested in a micro-simulation environment using the INTEGRATION traffic model. A hypothetical network, as well as the Scottsdale/Rural road corridor in Phoenix were used. The sensitivity analysis of before and after signal coordination scenarios indicated that after the signals were coordinated, the crash risk was lower, thus proving that the model could capture the benefits of this ITS component. Reducing the number of crashes is an important aspect of improving safety. Traffic signal coordination smoothens traffic on a facility and reduces its potential accident risk by producing less vehicle-to vehicle interactions. Also, traffic signal control increases the free-speed of a facility. The advantage of this safety model is the fact that it can be used to capture a variety of ITS technologies and not only signal coordination that is examined in more detail in this thesis. / Master of Science

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).

This thesis presents the use of microscopic traffic simulation models to evaluate the effects of operational-level transportation projects such as ITS. A detailed framework outlining the construction and calibration of microscopic simulation models is provided, as well as the considerations that must be made when analyzing the outputs from these models. Two case studies are used to reinforce the concepts presented. In addition, these case studies give valuable insight for using the outlined approach under real-world conditions. The study indicates a promising future for the use of microsimulation models for the purpose of evaluating operational-level projects, as the theoretical framework of the models is sound, and the computational strategies used are feasible. There are, however, instances where simulation models do not presently model certain phenomena, or where simulation models are too computationally intensive. Comprehensive models that integrate microscopic simulation with land use planning and realistic predictions of human behavior, for instance, cannot practically be modeled in contemporary simulation packages. Other than these instances, the largest obstacles to using simulation packages were found to be the manpower required and the complexity of constructing a model. Continuing research efforts and increasing computer speeds are expected to resolve the former issues. Both of the latter concerns are alleviated by the approach presented herein. Within the approach framework detailed in this thesis, particular emphasis is given to the calibration aspects of constructing a microscopic simulation model. Like the simulation process as a whole, calibration is both an art and a science, and relies on sound engineering judgement rather than indiscriminate, formulaic processes. / Master of Science

This dissertation discusses network wide signal control strategies base on connected vehicle technology. Traffic congestion on arterials has become one of the largest threats to economic competitiveness, livability, safety, and long-term environmental sustainability in the United States. In addition, arterials usually experience more blockage than freeways, specifically in terms of intersection congestion. There is no doubt that emerging technologies provide unequaled opportunities to revolutionize “retiming” and mitigate traffic congestion. Connected vehicle technology provides unparalleled safety benefits and holds promise in terms of alleviating both traffic congestion and the environmental impacts of future transportation systems. The objective of this research is to improve the mobility, safety and environmental effects at signalized arterials with connected vehicles. The proposed solution of this dissertation is to formulate traffic signal control models for signalized arterials based on connected vehicle technology. The models optimize offset, split, and cycle length to minimize total queue delay in all directions of coordinated intersections. Then, the models are implemented in a centralized system—including closed-loop systems—first, before expanding the results to distributed systems. The benefits of the models are realized at the infant stage of connected vehicle deployment when the penetration rate of connected vehicles is around 10%. Furthermore, the benefits incentivize the growth of the penetration rate for drivers. In addition, this dissertation contains a performance evaluation in traffic delay, volume throughput, fuel consumption, emission, and safety by providing a case study of coordinated signalized intersections. The case study results show the solution of this dissertation could adapt early deployment of connected vehicle technology and apply to future connected vehicle technology development.

No description available.

Nas grandes cidades ao redor do mundo os congestionamentos são problemas bem conhecidos e compartilhados por todos os setores da sociedade. Não é surpreendente, então, que grande parte dos investimentos públicos caminhem na direção de reduzir paulatinamente o esforço rotineiro que a população itinerante faz para chegar ao trabalho ou retornar ao lar, melhorando sua qualidade de vida. É partilhando desse intuito que se encontrou a motivação inicial para o desenvolvimento deste trabalho. Em outro pólo encontra-se a vertente dos procedimentos evolutivos que ao longo da história da vida na Terra foram responsáveis pela emergência espontânea de uma enorme gama de soluções para os mais variados ecossistemas. A tradução disso para ambientes computacionais criou a classe dos algoritmos evolucionários, dentre os quais os Algoritmos Genéticos (AGs), que se destacaram por serem boas heurísticas de busca por conjuntos de parâmetros que resultem em ótimos globais para problemas de engenharia. A aplicação de AGs para otimizações em engenharia de tráfego possui boa base bibliográfica, mas são raras as aplicações reais em meios onde há escassez de dados e de Sistemas Inteligentes de Transporte (do inglês, Intelligent Transportation Systems ITS). Neste trabalho então foi desenvolvido um novo modelo de simulação mesoscópica sobre o qual um AG é aplicado para encontrar planos semafóricos que reduzam atrasos e paradas em sub-redes congestionadas. A ferramenta é simplificada para execução rápida, usando parâmetros normalmente colhidos pela Companhia de Engenharia de Tráfego de São Paulo (CET-SP) em estudos de revisão de temporização semafórica. Ao fim do trabalho, o estudo de caso em uma sub-rede paulistana resultou em reduções da ordem de 30% no nível de atraso e paradas em relação aos valores obtidos com a simulação dos planos anteriores. Vale ressaltar que o espaço de busca foi reduzido ao sub espaço de planos aceitos pela experiência dos especialistas da CET-SP, e mesmo dentro deste escopo, o algoritmo foi bem sucedido ao descartar soluções ruins e fazer emergirem soluções ótimas coerentes. / In large cities throughout the world, high traffic congestion is a well known problem endured by all parts of society. Not surprisingly, a great deal of public investments is made in the direction of reducing the citizens daily efforts for going from home to work and backwards. It is by sharing this intent that the motivation for this thesis was found. Meanwhile, in another section of human knowledge there are concepts revolving the evolutionary procedures that were responsible for the spontaneous emergence of an immeasurable quantity of solutions for adaptation to an almost as greater quantity of ecossystems. When science brought these concepts to computational environments the class of evolutionary algorithms was born, a class embodied by the Genetics Algorithms (in this text referred as AGs, for Algoritmos Genéticos, in portuguese) which are heuristics that stand out as good alternatives for searching parameter settings that result in global optima for engineering problems. There is a good knowledge base regarding the use of AGs for traffic engineering optimizations, but rare are the real implementations in which specialists have to deal with lack of data and the absence of Intelligent Transportation Systems (ITS). Therefore, in this thesis a new mesoscopic simulation model was developed over which an AG is applied for finding signal timing plans that reduce stops and delays in congested subnetworks. The prototyped tool is simplified for quicker execution, using data that is normally collected by the Traffic Engineering Company of São Paulo (CET-SP) in signal timing revision activities. After the development, one of the numerous citys subnetworks was adopted as the case study, for which the prototype found plans that reduced the stops and delays in approximately 30% when compared to the values measured with the simulation of the old plans. It is worth notice that the search space was reduced to the subspace that only contains solutions accepted by the experience of CET-SPs traffic signal specialists, and within this subgroup, the algorithm succeeded in discarding the bad solutions and providing means for the emergence of coherent global optima.

Nas grandes cidades ao redor do mundo os congestionamentos são problemas bem conhecidos e compartilhados por todos os setores da sociedade. Não é surpreendente, então, que grande parte dos investimentos públicos caminhem na direção de reduzir paulatinamente o esforço rotineiro que a população itinerante faz para chegar ao trabalho ou retornar ao lar, melhorando sua qualidade de vida. É partilhando desse intuito que se encontrou a motivação inicial para o desenvolvimento deste trabalho. Em outro pólo encontra-se a vertente dos procedimentos evolutivos que ao longo da história da vida na Terra foram responsáveis pela emergência espontânea de uma enorme gama de soluções para os mais variados ecossistemas. A tradução disso para ambientes computacionais criou a classe dos algoritmos evolucionários, dentre os quais os Algoritmos Genéticos (AGs), que se destacaram por serem boas heurísticas de busca por conjuntos de parâmetros que resultem em ótimos globais para problemas de engenharia. A aplicação de AGs para otimizações em engenharia de tráfego possui boa base bibliográfica, mas são raras as aplicações reais em meios onde há escassez de dados e de Sistemas Inteligentes de Transporte (do inglês, Intelligent Transportation Systems ITS). Neste trabalho então foi desenvolvido um novo modelo de simulação mesoscópica sobre o qual um AG é aplicado para encontrar planos semafóricos que reduzam atrasos e paradas em sub-redes congestionadas. A ferramenta é simplificada para execução rápida, usando parâmetros normalmente colhidos pela Companhia de Engenharia de Tráfego de São Paulo (CET-SP) em estudos de revisão de temporização semafórica. Ao fim do trabalho, o estudo de caso em uma sub-rede paulistana resultou em reduções da ordem de 30% no nível de atraso e paradas em relação aos valores obtidos com a simulação dos planos anteriores. Vale ressaltar que o espaço de busca foi reduzido ao sub espaço de planos aceitos pela experiência dos especialistas da CET-SP, e mesmo dentro deste escopo, o algoritmo foi bem sucedido ao descartar soluções ruins e fazer emergirem soluções ótimas coerentes. / In large cities throughout the world, high traffic congestion is a well known problem endured by all parts of society. Not surprisingly, a great deal of public investments is made in the direction of reducing the citizens daily efforts for going from home to work and backwards. It is by sharing this intent that the motivation for this thesis was found. Meanwhile, in another section of human knowledge there are concepts revolving the evolutionary procedures that were responsible for the spontaneous emergence of an immeasurable quantity of solutions for adaptation to an almost as greater quantity of ecossystems. When science brought these concepts to computational environments the class of evolutionary algorithms was born, a class embodied by the Genetics Algorithms (in this text referred as AGs, for Algoritmos Genéticos, in portuguese) which are heuristics that stand out as good alternatives for searching parameter settings that result in global optima for engineering problems. There is a good knowledge base regarding the use of AGs for traffic engineering optimizations, but rare are the real implementations in which specialists have to deal with lack of data and the absence of Intelligent Transportation Systems (ITS). Therefore, in this thesis a new mesoscopic simulation model was developed over which an AG is applied for finding signal timing plans that reduce stops and delays in congested subnetworks. The prototyped tool is simplified for quicker execution, using data that is normally collected by the Traffic Engineering Company of São Paulo (CET-SP) in signal timing revision activities. After the development, one of the numerous citys subnetworks was adopted as the case study, for which the prototype found plans that reduced the stops and delays in approximately 30% when compared to the values measured with the simulation of the old plans. It is worth notice that the search space was reduced to the subspace that only contains solutions accepted by the experience of CET-SPs traffic signal specialists, and within this subgroup, the algorithm succeeded in discarding the bad solutions and providing means for the emergence of coherent global optima.