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.

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.

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Sub-optimal automated transportation control systems incur high mobility, human health and environmental costs. With society reliant on its transportation systems for the movement of individuals, goods and services, minimizing these costs benefits many. Intersection traffic signal controllers are an important element of modern transportation systems that govern how vehicles traverse road infrastructure. Many types of traffic signal controllers exist; fixed time, actuated and adaptive. Adaptive traffic signal controllers seek to minimize transportation costs through dynamic control of the intersection. However, many existing adaptive traffic signal controllers rely on heuristic or expert knowledge and were not originally designed for scalability or for transportation’s big data future. This research addresses the aforementioned challenges by developing a scalable system for adaptive traffic signal control model development using deep reinforcement learning in traffic simulation. Traffic signal control can be modelled as a sequential decision-making problem; reinforcement learning can solve sequential decision-making problems by learning an optimal policy. Deep reinforcement learning makes use of deep neural networks, powerful function approximators which benefit from large amounts of data. Distributed, parallel computing techniques are used to provide scalability, with the proposed methods validated on a simulation of the City of Luxembourg, Luxembourg, consisting of 196 intersections. This research contributes to the body of knowledge by successfully developing a scalable system for adaptive traffic signal control model development and validating it on the largest traffic microsimulator in the literature. The proposed system reduces delay, queues, vehicle stopped time and travel time compared to conventional traffic signal controllers. Findings from this research include that using reinforcement learning methods which explicitly develop the policy offers improved performance over purely value-based methods. The developed methods are expected to mitigate the problems caused by sub-optimal automated transportation signal controls systems, improving mobility and human health and reducing environmental costs. / Thesis / Doctor of Philosophy (PhD) / Inefficient transportation systems negatively impact mobility, human health and the environment. The goal of this research is to mitigate these negative impacts by improving automated transportation control systems, specifically intersection traffic signal controllers. This research presents a system for developing adaptive traffic signal controllers that can efficiently scale to the size of cities by using machine learning and parallel computation techniques. The proposed system is validated by developing adaptive traffic signal controllers for 196 intersections in a simulation of the City of Luxembourg, Luxembourg, successfully reducing delay, queues, vehicle stopped time and travel time.

Dilemma zone (DZ), also called decision zone in other literature, is an area where drivers face an indecisiveness of stopping or crossing at the yellow onset. The DZ issue is a major reason for the crashes at high-speed signalized intersections. As a result, how to prevent approaching vehicles from being caught in the DZ is a widely concerning issue. In this dissertation, the author addressed several DZ-associated issues, including the new stochastic safety measure, namely dilemma hazard, that indicates the vehicles' changing unsafe levels when they are approaching intersections, the optimal advance detector configurations for the multi-detector green extension systems, the new dilemma zone protection algorithm based on the Markov process, and the simulation-based optimization of traffic signal systems with the retrospective approximation concept. The findings include: the dilemma hazard reaches the maximum when a vehicle moves in the dilemma zone and it can be calculated according the caught vehicles' time to the intersection; the new (optimized) GES design can significantly improve the safety, but slightly improve the efficiency; the Markov process can be used in the dilemma zone protection, and the Markov-process-based dilemma zone protection system can outperform the prevailing dilemma zone protection system, the detection-control system (D-CS). When the data collection has higher fidelity, the new system will have an even better performance. The retrospective approximation technique can identify the sufficient, but not excessive, simulation efforts to model the true system and the new optimization algorithm can converge fast, as well as accommodate the requirements by the RA technique. / Ph. D.

Traffic signal cycle lengths are traditionally optimized to minimize vehicle delay at intersections using the Webster formulation. This thesis includes two studies that develop new formulations to compute the optimum cycle length of isolated intersections, considering measures of effectiveness such as vehicle delay, fuel consumption and tailpipe emissions. Additionally, both studies validate the Webster model against simulated data. The microscopic simulation software, INTEGRATION, was used to simulate two-phase and four-phase isolated intersections over a range of cycle lengths, traffic demand levels, and signal timing lost times. Intersection delay, fuel consumption levels, and emissions of hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NOx), and carbon dioxide (CO2) were derived from the simulation software. The cycle lengths that minimized the various measures of effectiveness were then used to develop the proposed formulations. The first research effort entailed recalibrating the Webster model to the simulated data to develop a new delay, fuel consumption, and emissions formulation. However, an additional intercept was incorporated to the new formulations to enhance the Webster model. The second research effort entailed updating the proposed model against four study intersections. To account for the stochastic and random nature of traffic, the simulations were then run with twenty random seeds per scenario. Both efforts noted its estimated cycle lengths to minimize fuel consumption and emissions were longer than cycle lengths optimized for vehicle delay only. Secondly, the simulation results manifested an overestimation in optimum cycle lengths derived from the Webster model for high vehicle demands. / Master of Science / Traffic signal timings are traditionally designed to reduce vehicle congestion at an intersection. This thesis is based on two studies that develop new formulations to compute the most efficient signal cycle lengths of intersections, considering vehicle fuel consumption and tailpipe emissions. Additionally, both studies validate the Webster model, a model that is traditionally used in traffic signal design. Simulations were run to determine the intersection delay, fuel consumption levels, and emissions of hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NO_x), and carbon dioxide (CO₂) of the study intersections. To account for the random nature of traffic, each simulation scenario was run twenty different times. The cycle lengths that minimized the noted simulation outputs were then used to develop the proposed formulations. The new formulations demonstrated its estimated cycle lengths to minimize fuel consumption and emissions were longer than cycle lengths designed to minimize vehicle congestion. Secondly, the simulation results manifested an overestimation in optimum cycle lengths derived from the Webster model for high vehicle traffic.

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>

New dynamic signal control methods such as fuzzy logic and artificial intelligence developed recently mainly focused on isolated intersection. In this study, a Fuzzy Logic Control for a Diamond Interchange incorporating with Fuzzy Ramp System (FLDI) has been developed. The signalization of two closely spaced intersections in a diamond interchange is a complicated problem that includes both increasing the diamond interchange capacity and reduce delays at the same time. The model comprises of three main modules. The Fuzzy Phase Timing module controls the current phase green time extension, the Phase Selection module select the next phase based on the pre-defined phase sequence or phase logics and the Fuzzy Ramp module determines the cycle time of the ramp meter bases on current traffic volumes and conditions of the interchanges and the motorways. The developed FLDI model has been compared with the traffic actuated simulation with respects to flow rates and the average delays of the vehicles. The model of an actual diamond interchange is described and simulated by using AIMSUN (Advanced Interactive Microscopic Simulator for Urban and Non-Urban Network) software. Simulation results show the FLDI model outperformed the traffic actuated models with lower system total travel time, average delay and improvements in downstream average speed and average delay.

Modern traffic signal control systems have not changed significantly in the past 40-50 years. The most widely applied traffic signal control systems are still time-of-day, coordinated-actuated system, since many existing advanced adaptive signal control systems are too complicated and fathomless for most of people. Recent advances in communications standards and technologies provide the basis for significant improvements in traffic signal control capabilities. In the United States, the IntelliDriveSM program (originally called Vehicle Infrastructure Integration - VII) has identified 5.9GHz Digital Short Range Communications (DSRC) as the primary communications mode for vehicle-to-vehicle (v2v) and vehicle-to-infrastructure (v2i) safety based applications, denoted as v2x. The ability for vehicles and the infrastructure to communication information is a significant advance over the current system capability of point presence and passage detection that is used in traffic control systems. Given enriched data from IntelliDriveSM, the problem of traffic control can be solved in an innovative data-driven and mathematical way to produce robust and optimal outputs.In this doctoral research, three different problems within a v2x environment- "enhanced pseudo-lane-level vehicle positioning", "robust coordinated-actuated multiple priority control", and "multimodal platoon-based arterial traffic signal control", are addressed with statistical techniques and mathematical programming.First, a pseudo-lane-level GPS positioning system is proposed based on an IntelliDriveSM v2x environment. GPS errors can be categorized into common-mode errors and noncommon-mode errors, where common-mode errors can be mitigated by differential GPS (DGPS) but noncommon-mode cannot. Common-mode GPS errors are cancelled using differential corrections broadcast from the road-side equipment (RSE). With v2i communication, a high fidelity roadway layout map (called MAP in the SAE J2735 standard) and satellite pseudo-range corrections are broadcast by the RSE. To enhance and correct lane level positioning of a vehicle, a statistical process control approach is used to detect significant vehicle driving events such as turning at an intersection or lane-changing. Whenever a turn event is detected, a mathematical program is solved to estimate and update the GPS noncommon-mode errors. Overall the GPS errors are reduced by corrections to both common-mode and noncommon-mode errors.Second, an analytical mathematical model, a mixed-integer linear program (MILP), is developed to provide robust real-time multiple priority control, assuming penetration of IntelliDriveSM is limited to emergency vehicles and transit vehicles. This is believed to be the first mathematical formulation which accommodates advanced features of modern traffic controllers, such as green extension and vehicle actuations, to provide flexibility in implementation of optimal signal plans. Signal coordination between adjacent signals is addressed by virtual coordination requests which behave significantly different than the current coordination control in a coordinated-actuated controller. The proposed new coordination method can handle both priority and coordination together to reduce and balance delays for buses and automobiles with real-time optimized solutions.The robust multiple priority control problem was simplified as a polynomial cut problem with some reasonable assumptions and applied on a real-world intersection at Southern Ave. & 67 Ave. in Phoenix, AZ on February 22, 2010 and March 10, 2010. The roadside equipment (RSE) was installed in the traffic signal control cabinet and connected with a live traffic signal controller via Ethernet. With the support of Maricopa County's Regional Emergency Action Coordinating (REACT) team, three REACT vehicles were equipped with onboard equipments (OBE). Different priority scenarios were tested including concurrent requests, conflicting requests, and mixed requests. The experiments showed that the traffic controller was able to perform desirably under each scenario.Finally, a unified platoon-based mathematical formulation called PAMSCOD is presented to perform online arterial (network) traffic signal control while considering multiple travel modes in the IntelliDriveSM environment with high market penetration, including passenger vehicles. First, a hierarchical platoon recognition algorithm is proposed to identify platoons in real-time. This algorithm can output the number of platoons approaching each intersection. Second, a mixed-integer linear program (MILP) is solved to determine the future optimal signal plans based on the real-time platoon data (and the platoon request for service) and current traffic controller status. Deviating from the traditional common network cycle length, PAMSCOD aims to provide multi-modal dynamical progression (MDP) on the arterial based on the real-time platoon information. The integer feasible solution region is enhanced in order to reduce the solution times by assuming a first-come, first-serve discipline for the platoon requests on the same approach. Microscopic online simulation in VISSIM shows that PAMSCOD can easily handle two traffic modes including buses and automobiles jointly and significantly reduce delays for both modes, compared with SYNCHRO optimized plans.