Increasing congestion and environmental concerns have evoked an interest in promoting urban Public Transport (PT) the last decades. In 2012 the City of Stockholm adopted an “Urban mobility strategy” stating that public transport, cycling and walking should be prioritised over cars in central Stockholm. One of the most important factors influencing the modal choice is the travel time ratio between car and PT travel. According to earlier studies Public Transport Traffic Signal Priority (PTSP) can reduce travel times for public transport with only small negative impacts on other traffic. Conditional PTSP can also help to regulate the PT service. Thus PTSP may support drivers’ decision to change travel mode from car to PT, thus supporting adopted policy goals. Conventional control strategies for coordinated traffic signals have pre-set timings based on traffic surveys. Some traffic adaptation based on real time detector actuations can also take place within the frames of the pre-set cycle time. PTSP changes the signal timings, within pre-set limits, when a PT vehicle is detected. Self-optimising control strategies use a traffic model to predict the traffic flows from traffic counts, and determine the signal changes in real-time by minimising a cost function including delay, number of stops etc. PTSP is included directly in the optimisation by giving PT vehicles a higher weight compared to cars. In this thesis the fundamentals of signal control theory are reviewed as well as unconditional and conditional PTSP criteria and strategies. A simulation based method for evaluation of impacts of different PTSP strategies in coordinated controlled traffic signals is implemented. The simulation setup includes Software-In-the-Loop (SIL) signal controller simulators running the same control logic as used in field. Such simulation models can be useful to test and fine tune PTSP before being implemented in field. Simulations with a SIL setup also enable comparisons of signal control strategies or systems on equal terms, not practically or economically possible in field studies. The implemented SIL simulation model was used to evaluate the impacts on buses and other traffic from the different PTSP functions used in the “PRIBUSS” PTSP method. Short green time extensions showed travel time reductions for buses, with almost no travel time increase for other traffic. Long green time extensions gave somewhat larger benefits for the buses, but more delay to other traffic. Red truncation gave less travel time savings to the prioritised buses and more extra delay for cross street traffic, compared to green extensions. Double red truncation and Extra phase showed some additional travel time savings to the buses, but had the largest negative impact on other traffic. A combination of PRIBUSS functions showed the best results. Depending on the structure of the signal coordination and the location of the bus stops different PTSP functions may be needed. Based on the conclusions from the evaluation of the different PRIBUSS functions a conditional “differential on-time-status” based PTSP strategy was proposed and tested in the SIL simulation environment. The proposed method is focusing on direct travel time savings as well as on reduced bus bunching. The two self-optimising signal control systems Utopia/Spot and ImFlow were tested, and their impacts were compared to conventional control including PTSP with the PRIBUSS method in a SIL simulation environment. The aim was to test if commercially available self-optimising control systems can reduce the overall delay per person by applying more sophisticated PTSP. Both systems reduced the delay for buses, cyclists and pedestrians at a cost of increased delay and increased number of stops compared to the existing conventional control used in field. The total delay for all road users was reduced substantially. / Intresset för att påverka resvanorna i våra städer så att kollektivtrafikandelen ökar har växt de senaste decennierna på grund av en ökad trängsel i gatunätet samt ökad miljömedvetenhet. Stockholms stad har antagit ”Framkomlighetsstrategin” som innebär att kollektivtrafik, gång och cykel ska prioriteras framför biltrafik i centrala Stockholm. En av de faktorer som påverkar färdmedelsvalet mest är restidskvoten mellan bil och kollektivtrafik. Tidigare studier har visat att kollektivtrafikprioritering i trafiksignaler kan minska körtiden för kollektivtrafiken väsentligt, med små eller inga negativa konsekvenser för övrig trafik. Villkorlig prioritering kan dessutom förbättra kollektivtrafikens regularitet. Kollektivtrafikprioritering i trafiksignaler kan på så sätt hjälpa till att förbättra kollektivtrafikens attraktivitet och därigenom öka kollektivtrafikandelen. Samordnade trafiksignaler styrda med konventionell teknik har en fast tidsättning framtagen med insamlade historiska trafikdata som grund. Viss trafikstyrning kan åstadkommas inom ramen för den fasta omloppstiden. Om bussprioritering finns ändras signalväxlingen av prioriteringsfunktionerna när en buss detekteras, inom vissa begränsningar för att hålla ihop det samordnade systemet. Självoptimerande signalstyrning bygger på att fordonsrörelserna genom systemet predikteras med en trafikmodell utifrån trafikräkningar med detektorer. Signaltidsättningen bestäms sedan i realtid genom att minimera en kostnadsfunktion som innehåller fördröjning, antal stopp mm. för de modellerade fordonsrörelserna. Kollektivtrafiken prioriteras genom att dess fordon detekteras separat från övrig trafik, och ges en högre vikt i optimeringen av signaltidssättningen. I denna avhandling beskrivs de teoretiska grunderna för trafiksignalstyrning, liksom metoder och kriterier för villkorlig och ovillkorlig signalprioritering av kollektivtrafik. En simuleringsbaserad metod för att utvärdera effekterna av olika signalprioritering har implementerats. Denna använder styrapparatsimulatorer med samma programmering som styrapparaterna på gatan, inklusive prioriteringsfunktioner. Sådana simuleringar kan vara ett användbart verktyg för att justera in prioriteringsfunktionerna innan dessa implementeras i signalstyrningen på gatan. Simuleringar med styrapparatsimulatorer möjliggör också jämförelser av olika styrstrategier under kontrollerade förhållanden som inte vore praktiskt, eller ekonomiskt möjliga att genomföra i fält. I den framtagna simuleringsmiljön har effekterna av de olika prioriteringsfunktionerna i PRIBUSS utvärderats. Korta (maxtids-)förlängningar gav körtidsvinster för bussar och knappast några restidsförsämringar för övrig trafik. Långa förlängningar (fråntidsförlängning och återtagen start) gav ytterligare restidsvinster för busstrafiken, men ökad fördröjning för övrig trafik. Avkortning gav, jämfört med förlängningar, mindre restidsvinster för busstrafiken och mer störning för övrig trafik. De mer komplicerade funktionerna Dubbel avkortning och extrafas gav viss ytterligare restidsvinst för bussarna, men hade den största inverkan på övrig trafik. Bäst resultat uppkom dock med en kombination av PRIBUSS funktioner. Beroende på samordningens struktur och busshållplatsernas placering i förhållande till trafiksignalerna kan olika prioriteringsfunktioner ge större eller mindre nytta. Baserat på utvärderingen av de olika PRIBUSS funktionernas effekter på bussar och övrig trafik har en tidhållningsbaserad differentierad prioriteringsstrategi föreslagits, som förutom att skapa direkta restidvinster också försöker motverka ihopklumpning av bussar. Denna strategi har implementerats och testats i den framtagna simuleringsmiljön. Med hjälp av simuleringar har de självoptimerande signalstyrsystemen Utopia/Spot och ImFlow testats och jämförts med konventionell styrning, inklusive bussprioritering med PRIBUSS. Syftet med denna studie var att undersöka om fördröjningen per person i trafiknätet kan minskas genom bättre kollektivtrafikprioritering med hjälp av ett kommersiellt tillgängligt självoptimerande signalstyrsystem. De båda testade systemen gav minskad fördröjning för kollektivtrafik, gående och cyklister, men ökad fördröjning och antal stopp för biltrafik. Den totala fördröjningen minskades betydligt med de båda testade självoptimerande signalstyrsystemen. / <p>QC 20140513</p>

Um melhor uso da infraestrutura da rede de transporte é um ponto fundamental para atenuar os efeitos dos congestionamentos no trânsito. Este trabalho utiliza aprendizado por reforço multiagente (MARL) para melhorar o uso da infraestrutura e, consequentemente, mitigar tais congestionamentos. A partir disso, diversos desafios surgem. Primeiro, a maioria da literatura assume que os motoristas aprendem (semáforos não possuem nenhum tipo de aprendizado) ou os semáforos aprendem (motoristas não alteram seus comportamentos). Em segundo lugar, independentemente do tipo de classe de agentes e do tipo de aprendizado, as ações são altamente acopladas, tornando a tarefa de aprendizado mais difícil. Terceiro, quando duas classes de agentes co-aprendem, as tarefas de aprendizado de cada agente são de natureza diferente (do ponto de vista do aprendizado por reforço multiagente). Finalmente, é utilizada uma modelagem microscópica, que modela os agentes com um alto nível de detalhes, o que não é trivial, pois cada agente tem seu próprio ritmo de aprendizado. Portanto, este trabalho não propõe somente a abordagem de co-aprendizado em agentes que atuam em ambiente compartilhado, mas também argumenta que essa tarefa precisa ser formulada de forma assíncrona. Além disso, os agentes motoristas podem atualizar os valores das ações disponíveis ao receber informações de outros motoristas. Os resultados mostram que a abordagem proposta, baseada no coaprendizado, supera outras políticas em termos de tempo médio de viagem. Além disso, quando o co-aprendizado é utilizado, as filas de veículos parados nos semáforos são menores. / A better use of transport network infrastructure is a key point in mitigating the effects of traffic congestion. This work uses multiagent reinforcement learning (MARL) to improve the use of infrastructure and, consequently, to reduce such congestion. From this, several challenges arise. First, most literature assumes that drivers learn (traffic lights do not have any type of learning) or the traffic lights learn (drivers do not change their behaviors). Second, regardless of the type of agent class and the type of learning, the actions are highly coupled, making the learning task more difficult. Third, when two classes of agents co-learn, the learning tasks of each agent are of a different nature (from the point of view of multiagent reinforcement learning). Finally, a microscopic modeling is used, which models the agents with a high level of detail, which is not trivial, since each agent has its own learning pace. Therefore, this work does not only propose the co-learnig approach in agents that act in a shared environment, but also argues that this taks needs to be formulated asynchronously. In addtion, driver agents can update the value of the available actions by receiving information from other drivers. The results show that the proposed approach, based on co-learning, outperforms other policies regarding average travel time. Also, when co-learning is use, queues of stopped vehicles at traffic lights are lower.

Adaptive traffic signal control is the process by which the timing of a traffic signal is continuously adjusted based on the changing arrival patterns of vehicles at an intersection, usually with the goal of optimizing a given measure of effectiveness. Herein, a methodology is developed in which the characteristics of a traffic signal cycle are optimized at the conclusion of every phase based on the arrival times of vehicles to an intersection, using stopped delay as the measure of effectiveness. This optimization is solved using metaheuristic search procedures, namely tabu search, and embedded in an algorithm in which current vehicle arrival times are detected, arrival patterns over a specified horizon are predicted, the traffic signal timing is optimized, and the timings are sent to a traffic signal controller. The methodology is shown to provide improvement in performance for a number of intersection configurations and traffic regimes over traditional forms of traffic signal control, and the metaheuristic search is demonstrated to significantly reduce the computation time for a solution as compared with other search procedures. / text

Signal control systems have experienced tremendous development both in hardware and in control strategies in the past 50 years since the advent of the first electronic traffic signal control device. The state-of-art real-time signal control strategies rely heavily on infrastructure-based sensors, including in-pavement or video based loop detectors for data collection. With the emergence of connected vehicle technology, mobility applications utilizing vehicle to infrastructure (V2I) communications enable the intersection to acquire a much more complete picture of the nearby vehicle states. Based on this new source of data, traffic controllers should be able to make "smarter" decisions. This dissertation investigates the traffic signal control strategies in a connected vehicle environment considering mobility as well as safety. A system architecture for connected vehicle based signal control applications under both a simulation environment and in the real world has been developed. The proposed architecture can be applied to applications such as adaptive signal control, signal priority including transit signal priority (TSP), freight signal priority (FSP), emergency vehicle preemption, and integration of adaptive signal control and signal priority. Within the framework, the trajectory awareness of connected vehicles component processes and stores the connected vehicle data from Basic Safety Message (BSM). A lane level intersection map that represents the geometric structure was developed. Combined with the map and vehicle information from BSMs, the connected vehicles can be located on the map. Some important questions specific to connected vehicle are addressed in this component. A geo-fencing area makes sure the roadside equipment (RSE) receives the BSM from only vehicles on the roadway and within the Dedicated Short-range Communications (DSRC) range. A mechanism to maintain anonymity of vehicle trajectories to ensure privacy is also developed. Vehicle data from the trajectory awareness of connected vehicles component can be used as the input to a real-time phase allocation algorithm that considers the mobility aspect of the intersection operations. The phase allocation algorithm applies a two-level optimization scheme based on the dual ring controller in which phase sequence and duration are optimized simultaneously. Two objective functions are considered: minimization of total vehicle delay and minimization of queue length. Due to the low penetration rate of the connected vehicles, an algorithm that estimates the states of unequipped vehicles based on connected vehicle data is developed to construct a complete arrival table for the phase allocation algorithm. A real-world intersection is modeled in VISSIM to validate the algorithms. Dangerous driving behaviors may occur if a vehicle is trapped in the dilemma zone which represents one safety aspect of signalized intersection operation. An analytical model for estimating the number of vehicles in dilemma zone (NVDZ) is developed on the basis of signal timing, arterial geometry, traffic demand, and driving characteristics. The analytical model of NVDZ calculation is integrated into the signal optimization to perform dilemma zone protection. Delay and NVDZ are formulated as a multi-objective optimization problem addressing efficiency and safety together. Examples show that delay and NVDZ are competing objectives and cannot be optimized simultaneously. An economic model is applied to find the minimum combined cost of the two objectives using a monetized objective function. In the connected vehicle environment, the NVDZ can be calculated from connected vehicle data and dilemma zone protection is integrated into the phase allocation algorithm. Due to the complex nature of traffic control systems, it is desirable to utilize traffic simulation in order to test and evaluate the effectiveness and safety of new models before implementing them in the field. Therefore, developing such a simulation platform is very important. This dissertation proposes a simulation environment that can be applied to different connected vehicle related signal control applications in VISSIM. Both hardware-in-the-loop (HIL) and software-in-the-loop (SIL) simulation are used. The simulation environment tries to mimic the real world complexity and follows the Society of Automotive Engineers (SAE) J2735 standard DSRC messaging so that models and algorithms tested in the simulation can be directly applied in the field with minimal modification. Comprehensive testing and evaluation of the proposed models are conducted in two simulation networks and one field intersection. Traffic signal priority is an operational strategy to apply special signal timings to reduce the delay of certain types of vehicles. The common way of serving signal priority is based on the "first come first serve" rule which may not be optimal in terms of total priority delay. A priority system that can serve multiple requests with different priority levels should perform better than the current method. Traditionally, coordination is treated in a different framework from signal priority. However, the objectives of coordination and signal priority are similar. In this dissertation, adaptive signal control, signal priority and coordination are integrated into a unified framework. The signal priority algorithm generates a feasible set of optimal signal schedules that minimize the priority delay. The phase allocation algorithm considers the set as additional constraints and tries to minimize the total regular vehicle delay within the set. Different test scenarios including coordination request, priority vehicle request and combination of coordination and priority requests are developed and tested.

Dissertation for the degree of Doctor of Science in Technology--Helsinki University of Technology, Espoo, 2002. / "ISSN 0781-5816." Includes bibliographical references (p. 65-71). Available online as a PDF file via the World Wide Web.

Um melhor uso da infraestrutura da rede de transporte é um ponto fundamental para atenuar os efeitos dos congestionamentos no trânsito. Este trabalho utiliza aprendizado por reforço multiagente (MARL) para melhorar o uso da infraestrutura e, consequentemente, mitigar tais congestionamentos. A partir disso, diversos desafios surgem. Primeiro, a maioria da literatura assume que os motoristas aprendem (semáforos não possuem nenhum tipo de aprendizado) ou os semáforos aprendem (motoristas não alteram seus comportamentos). Em segundo lugar, independentemente do tipo de classe de agentes e do tipo de aprendizado, as ações são altamente acopladas, tornando a tarefa de aprendizado mais difícil. Terceiro, quando duas classes de agentes co-aprendem, as tarefas de aprendizado de cada agente são de natureza diferente (do ponto de vista do aprendizado por reforço multiagente). Finalmente, é utilizada uma modelagem microscópica, que modela os agentes com um alto nível de detalhes, o que não é trivial, pois cada agente tem seu próprio ritmo de aprendizado. Portanto, este trabalho não propõe somente a abordagem de co-aprendizado em agentes que atuam em ambiente compartilhado, mas também argumenta que essa tarefa precisa ser formulada de forma assíncrona. Além disso, os agentes motoristas podem atualizar os valores das ações disponíveis ao receber informações de outros motoristas. Os resultados mostram que a abordagem proposta, baseada no coaprendizado, supera outras políticas em termos de tempo médio de viagem. Além disso, quando o co-aprendizado é utilizado, as filas de veículos parados nos semáforos são menores. / A better use of transport network infrastructure is a key point in mitigating the effects of traffic congestion. This work uses multiagent reinforcement learning (MARL) to improve the use of infrastructure and, consequently, to reduce such congestion. From this, several challenges arise. First, most literature assumes that drivers learn (traffic lights do not have any type of learning) or the traffic lights learn (drivers do not change their behaviors). Second, regardless of the type of agent class and the type of learning, the actions are highly coupled, making the learning task more difficult. Third, when two classes of agents co-learn, the learning tasks of each agent are of a different nature (from the point of view of multiagent reinforcement learning). Finally, a microscopic modeling is used, which models the agents with a high level of detail, which is not trivial, since each agent has its own learning pace. Therefore, this work does not only propose the co-learnig approach in agents that act in a shared environment, but also argues that this taks needs to be formulated asynchronously. In addtion, driver agents can update the value of the available actions by receiving information from other drivers. The results show that the proposed approach, based on co-learning, outperforms other policies regarding average travel time. Also, when co-learning is use, queues of stopped vehicles at traffic lights are lower.

Um melhor uso da infraestrutura da rede de transporte é um ponto fundamental para atenuar os efeitos dos congestionamentos no trânsito. Este trabalho utiliza aprendizado por reforço multiagente (MARL) para melhorar o uso da infraestrutura e, consequentemente, mitigar tais congestionamentos. A partir disso, diversos desafios surgem. Primeiro, a maioria da literatura assume que os motoristas aprendem (semáforos não possuem nenhum tipo de aprendizado) ou os semáforos aprendem (motoristas não alteram seus comportamentos). Em segundo lugar, independentemente do tipo de classe de agentes e do tipo de aprendizado, as ações são altamente acopladas, tornando a tarefa de aprendizado mais difícil. Terceiro, quando duas classes de agentes co-aprendem, as tarefas de aprendizado de cada agente são de natureza diferente (do ponto de vista do aprendizado por reforço multiagente). Finalmente, é utilizada uma modelagem microscópica, que modela os agentes com um alto nível de detalhes, o que não é trivial, pois cada agente tem seu próprio ritmo de aprendizado. Portanto, este trabalho não propõe somente a abordagem de co-aprendizado em agentes que atuam em ambiente compartilhado, mas também argumenta que essa tarefa precisa ser formulada de forma assíncrona. Além disso, os agentes motoristas podem atualizar os valores das ações disponíveis ao receber informações de outros motoristas. Os resultados mostram que a abordagem proposta, baseada no coaprendizado, supera outras políticas em termos de tempo médio de viagem. Além disso, quando o co-aprendizado é utilizado, as filas de veículos parados nos semáforos são menores. / A better use of transport network infrastructure is a key point in mitigating the effects of traffic congestion. This work uses multiagent reinforcement learning (MARL) to improve the use of infrastructure and, consequently, to reduce such congestion. From this, several challenges arise. First, most literature assumes that drivers learn (traffic lights do not have any type of learning) or the traffic lights learn (drivers do not change their behaviors). Second, regardless of the type of agent class and the type of learning, the actions are highly coupled, making the learning task more difficult. Third, when two classes of agents co-learn, the learning tasks of each agent are of a different nature (from the point of view of multiagent reinforcement learning). Finally, a microscopic modeling is used, which models the agents with a high level of detail, which is not trivial, since each agent has its own learning pace. Therefore, this work does not only propose the co-learnig approach in agents that act in a shared environment, but also argues that this taks needs to be formulated asynchronously. In addtion, driver agents can update the value of the available actions by receiving information from other drivers. The results show that the proposed approach, based on co-learning, outperforms other policies regarding average travel time. Also, when co-learning is use, queues of stopped vehicles at traffic lights are lower.

One of the main difficulties in the development of urban traffic microsimulation models is the collection of traffic data for calibration and validation. However, the city of Fortaleza has an important mesosimulation tool that, in addition to controlling urban traffic in real time, estimates traffic variables: the well-known SCOOT system. This system, implemented in cities around the world, controls and estimates traffic in the densest urban area of Fortaleza, based on the continuous detection of vehicle occupation on its more than 900 detectors spread throughout the city. However, because these data are simulated, they require validation before being used. The main aim of this work was to develop and implement a methodology to validate the mesoscopic simulation model of SCOOT, so its data can be used in the development of traffic microsimulation models, having as a case-study the system operating in Fortaleza. Based on experiments, the effects of two factors in the estimation error were investigated: the calibration of the parameter SATO, and the average travel time between the loop detector and the stop-bar. The results show that these two factors affect the quality of the prediction of volume, delay and number of vehicle-stops. These results contribute with a validation methodology that allows a better use of the data provided by SCOOT. / Um das maiores dificuldades na construÃÃo de redes viÃrias urbanas microssimuladas reside na coleta dos dados de trÃfego para as fases de calibraÃÃo e validaÃÃo. PorÃm, a cidade de Fortaleza dispÃe de uma importante ferramenta de mesossimulaÃÃo que, alÃm de controlar o trÃfego urbano em tempo real, estima indicadores de trÃfego: sistema SCOOT â Split Cycle Offset Optmisation Technique. Este sistema, implantado em vÃrias cidades do mundo, controla e modela o trÃfego na regiÃo mais adensada da Ãrea urbana de Fortaleza, baseando-se na coleta contÃnua de ocupaÃÃo veicular sobre os seus mais de 900 laÃos detectores espalhados pela cidade. No entanto, por se tratar de valores simulados, carecem de verificaÃÃo antes de serem utilizados. O objetivo geral deste trabalho à desenvolver e implementar uma metodologia para validaÃÃo do modelo de simulaÃÃo mesoscÃpica do SCOOT, tendo em vista o uso de seus dados no desenvolvimento de modelos de microssimulaÃÃo do trÃfego, tendo como estudo de caso o sistema em operaÃÃo em Fortaleza. Por meio de experimentos, foram investigados os efeitos de dois fatores no erro de estimaÃÃo: a calibraÃÃo do parÃmetro SATO e o tempo de percurso mÃdio entre o laÃo detector e a faixa de retenÃÃo. Os resultados mostram que estes dois fatores afetam a qualidade da modelagem das variÃveis volume, atraso veicular e nÃmero de paradas. Os resultados desta pesquisa contribuem no sentido de oferecer uma metodologia de validaÃÃo que permita um melhor uso dos dados fornecidos pelo SCOOT.

CoordenaÃÃo de AperfeiÃoamento de NÃvel Superior / A utilizaÃÃo de mecanismos que auxiliem a tomada de decisÃo à cada vem mais difundida no meio tÃcnico, o uso destes ferramentais por Engenheiros de TrÃfego auxiliam na anÃlise do desempenho de novos cenÃrios urbanos, definiÃÃo de novas estratÃgias de coordenaÃÃo, isolamento de semÃforos, dentre outros, de forma a contribuir para a eficÃcia do sistema de trÃnsito. Com o aumento da problemÃtica relacionada ao trÃnsito nas grandes cidades, a implantaÃÃo de semÃforos surge como uma das medidas mitigadoras. Devido à existÃncia de vÃrios mÃtodos de implantaÃÃo de semÃforos, nacionais e estrangeiras, conforme as caracterÃsticas das cidades que os originaram, à necessÃrio alÃm da identificaÃÃo dos locais onde se devem implantar os semÃforos, utilizar outros mÃtodos, a exemplo de um otimizador e um simulador de trÃfego, que atravÃs de medidas de desempenho, auxiliem à anÃlise dos impactos operacionais no trÃnsito, de forma a verificar quais as reais melhorias resultantes das implantaÃÃes, considerando outras realidades. Este trabalho terà como estudo de caso um trecho viÃrio da Ãrea central da cidade de Fortaleza-Ce. Na concepÃÃo deste estudo, foram definidas as seguintes etapas: aplicaÃÃo das metodologias de implantaÃÃo de semÃforos estudadas, definiÃÃo do modo de operaÃÃo e coordenaÃÃo e avaliaÃÃo operacional. Optou-se por utilizar o simulador de trÃfego Integration que atravÃs de suas medidas de desempenho auxiliou na definiÃÃo do cenÃrio que apresentou os maiores ganhos operacionais em relaÃÃo ao cenÃrio atual. / Decision making techniques have become increasingly widespread in the technical field. The use of such tools by Traffic Engineers assists in analyzing the performance of new urban settings, the definition of new coordination strategies, traffic signal isolation, among others, as to contribute to the effectiveness of the traffic control system. With increasing problems related to traffic in large cities, the implementation of traffic control signals emerges as one of the mitigating measures. As there are several national and international methods used in the implementation of traffic signals, depending on the characteristics of the cities in which they originate, it is necessary not only to identify the sites where traffic signals should be installed, but also to utilize different methods. Such methods include traffic signal optimization and simulation tools, which assist in the analysis of operational impacts through performance measures, indentifying which are the real improvements resulting from the implementations, considering other realities. The present work will study a road section from the central area of the city of Fortaleza, in the state of CearÃ, Brazil. The following stages were defined: application of the studied methodologies of traffic control signal implementation, mode of operation, and operational coordination and evaluation. The traffic simulator Integration was used, and through its performance measurements it was possible to define the setting that exhibited the highest operational gains in relation to the current setting.

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).