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At most of the US signal, pedestrian walk timings run in concurrence with relevant vehicular traffic signal phases which means that major-street coordinated operations can be interrupted by a pedestrian call. Such interruption may increase delays and stops for major traffic flows. An alternative to this design is to increase the cycle length and embed pedestrian timings within the ring-barrier structure of the prevailing coordination plan. Both approaches have advantages and disadvantages. This study attempts a novel approach to address this situation by a comprehensive experimental evaluation of traffic performance under various pedestrian signal timing strategies. Findings show that either abovementioned approach works well for very low traffic demands. When the traffic demand increases findings cannot be generalized as they differ for major coordinated movements versus overall network performance. While coordinated movements prefer no interruption of the coordinated operations, the overall network performance is better in the other case. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Borrowing ideas from natural immunity, Artificial Immune Systems (AIS) offer a novel approach to solving many diagnosis, optimization and control problems. In the course of this research this paradigm was applied to the problem of optimizing urban traffic. The traffic was micro-simulated with each car on a two junction road system modeled individually. The cars themselves were programmed with 'personalities' to better simulate real traffic. A novel AIS was developed to detect, predict, and control anomalous traffic conditions. It was also used to optimize the flow of traffic through the road network. Benchmarking was performed against the well accepted TRANSYT traffic control system. Though the TRANSYT system performed better initially, the AIS control showed marked improvement over time as it adapted better to changing traffic conditions. This change was expected as TRANSYT is optimized for specific initial conditions unlike the AIS system which adapts to changes.

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.

Depuis plusieurs décennies, la congestion urbaine est de plus en plus répandue et dégrade la qualité de vie des habitants des villes. Plusieurs méthodes sont utilisées pour diminuer la congestion urbaine, notamment la régulation du trafic et la valorisation des transports en commun. Depuis les années 1990 l'utilisation d‘outils issus de l'intelligence artificielle, et en particulier des méthodes distribuées et les systèmes multi-agents, a permis de concevoir de nouvelles méthodes de régulation du trafic. Parallèlement, l'amélioration des capacités de communication des véhicules et des conducteurs et l'arrivée de voitures autonomes permettent d'envisager de nouvelles approches en matière de régulation. Le travail de recherche proposé dans le cadre de cette thèse est structuré en deux volets. Nous proposons d'abord une méthode de régulation du trafic à une intersection s'appuyant sur la négociation automatique. Notre méthode se fonde sur un système d'argumentation décrivant l'état du trafic et les préférences de chacun, appuyé par des méthodes de raisonnement pour les véhicules et les infrastructures. Dans le deuxième volet de cette thèse, nous proposons une méthode de coordination des bus avec le reste du trafic. Celle-ci permet à un bus de se coordonner de manière anticipative avec les prochaines intersections qu'il prévoit de traverser, afin de mettre en place une politique commune de régulation qui permet au bus d'atteindre son prochain arrêt en subissant le minimum de congestions potentielles / Since several decades, urban congestion is more and more widespread and deteriorate the quality of life of citizens who live in cities. Several methods are used to reduce urban congestion, notably traffic regulation and promotion of public transportation. Since the 1990's, the usage of tools from artificial intelligence, particularly distributed systems and multi-agent systems, allowed to design new methods for traffic regulation. Indeed, these methods ease to take into account the complexity of traffic-related problems with distribution. Moreover, the improvement of the communication abilities of the vehicles and the coming of autonomous vehicles allow to consider new approaches for regulation.The research work presented in this work is twofold. First we propose a method for traffic regulation at an intersection based on automatic negotiation. Our method is based on an argumentation system describing the state of the traffic and the preferences of each vehicle, relying on reasonning methods for vehicles and infrastructures. In the second part of this thesis, we propose a coordination method for buses for the rest of the traffic. This method allows a bus to coordinate in an anticipatory way with the next intersections on its trajectory, in order to define a common regulation policy allowing the bus to reach its next stop without suffering from potential congestions

Thesis (M.App.Sc.) - Carleton University, 2002. / Includes bibliographical references (p. 107-113). Also available in electronic format on the Internet.

Submitted by Edileide Reis (leyde-landy@hotmail.com) on 2013-04-09T18:13:53Z No. of bitstreams: 1 Valdizio 1.pdf: 7942978 bytes, checksum: 4725d13390e14918f79ef66ee5f97cc4 (MD5) / Approved for entry into archive by Rodrigo Meirelles(rodrigomei@ufba.br) on 2013-04-13T19:59:09Z (GMT) No. of bitstreams: 1 Valdizio 1.pdf: 7942978 bytes, checksum: 4725d13390e14918f79ef66ee5f97cc4 (MD5) / Made available in DSpace on 2013-04-13T19:59:09Z (GMT). No. of bitstreams: 1 Valdizio 1.pdf: 7942978 bytes, checksum: 4725d13390e14918f79ef66ee5f97cc4 (MD5) Previous issue date: 2004 / O presente trabalho pretende avaliar o impacto da situação da poluição sonora provocada pelo tráfego em um parque urbano, através da realização de medidas em campo, mapeamento dos níveis de poluição sonora na área, através de simulações em computador de medidas atenuadoras desta poluição, que considera fontes pontuais e contínuas de geração de ruído, e também a simulação de colocação de barreiras físicas em distâncias variáveis como recurso para atenuação do ruído; além de outras medidas atenuadoras existentes na literatura científica especializada sobre o assunto. O estudo utiliza o software Community Noise Model - CNM 6.0, que faz a simulação dos diferentes níveis de poluição sonora. É feita uma validação deste software no que se refere à frota de veículos utilizados no input. A construção de um sistema de informação geográfica facilitará a visualização da distribuição espacial da poluição sonora, além de outros dados espaciais relevantes na área, e compreensão do seu comportamento, de forma a habilitar a comparação com padrões máximos fixados para as atividades existentes na área. Isto permitirá a identificação dos impactos da poluição sonora e a proposta de medidas atenuadoras deste impacto nos locais de atividades sensíveis a esta poluição. / Salvador

nÃo hà / A modelagem do trÃfego veicular em grandes Ãreas urbanas à uma ferramenta fundamental na anÃlise do desempenho de novas estratÃgias e polÃticas de gerÃncia e controle de trÃfego, idealizadas para potencializar a eficiÃncia do sistema de transportes. Com o uso de simuladores de trÃfego à possÃvel realizar estudos sobre os impactos de diferentes alternativas de intervenÃÃo, tais como a implementaÃÃo ou duplicaÃÃo de vias, alteraÃÃes na circulaÃÃo viÃria, entre outras aplicaÃÃes. Entretanto, os simuladores mais utilizados foram desenvolvidos em outros paÃses e geralmente nÃo representam o comportamento do trÃfego das redes viÃrias urbanas brasileiras, sendo, portanto necessÃria a calibraÃÃo dos seus parÃmetros. Com a evoluÃÃo tecnolÃgica dos modelos de microssimulaÃÃo de trÃfego, o processo de calibraÃÃo tornou-se ainda mais difÃcil devido ao maior nÃvel de complexidade dos modelos, o que implica no aumento do nÃmero de parÃmetros a serem calibrados. Diante do exposto e a fim de permitir a utilizaÃÃo de microssimuladores de trÃfego na modelagem de malhas viÃrias urbanas brasileiras, torna-se indispensÃvel o uso de mÃtodos computacionais de otimizaÃÃo que garantam, de forma eficaz, a calibraÃÃo simultÃnea de uma grande quantidade de parÃmetros. Essa dissertaÃÃo de mestrado busca verificar a aplicabilidade de algoritmos genÃticos para calibraÃÃo de modelos de microssimulaÃÃo do trÃfego veicular em malhas viÃrias urbanas como as encontradas no cenÃrio nacional. Ao final dessa pesquisa, concluiu-se que o mÃtodo de calibraÃÃo utilizado revelou-se eficiente considerando os resultados atingidos no estudo de caso realizado. AlÃm disto, acredita-se que a metodologia aqui proposta juntamente com os valores encontrados para os parÃmetros do microssimulador e do algoritmo genÃtico possam ser utilizados em outras malhas viÃrias urbanas, desde que estas apresentem caracterÃsticas semelhantes as da malha viÃria aqui simulada. / Traffic simulation modeling of mid-sized and big cities is a fundamental tool to analyze the performance of strategies idealized to maximize efficiency of urban transport systems. Traffic microsimulation models allow for foreseeing the impacts of different alternatives of solution, such as creation or duplication of lanes, reversion of flow direction, and implementation of priority lanes for public transport. However, the main microsimulation software packages available were developed in other countries, what means that they usually do not suit traffic behavior found in Brazilian urban centers, so parameter calibration is necessary to better adjust the models to local reality. Advances in computational capabilities have allowed traffic microsimulation models incorporate greater amount of parameters to be calibrated, so the process of calibration has become relatively more complex. Therefore, the use of computational methods to optimize the parametersâ values simultaneously is indispensable. This Masterâs thesis aims to verify the applicability of genetic algorithms to calibrate microsimulation traffic models in urban Brazilian networks. At the end of this study, it was concluded that the calibration method used proved to be efficient. It is believed that the proposed methodology to calibrate the microsimulation software and the genetic algorithm, as well as the final configuration of the genetic algorithm selected, may be applied to other urban networks, provided that they present similar characteristics to the studied network.

There are a lot more roads being built periodically in most of the countries with the advancement of modern society. In order to promote the overall traffic flow quality within different cities, city traffic management has been playing a more and more essential role during the last few decades. In recent years, a significantly increasing attention has been paid to the management of traffic flow in major cities all over the world. In this thesis, we develop a stochastic dynamic model for urban traffic along with physical constraints characteristic of intersections equipped with traffic light. We assume that the incoming traffic to each stream in an intersection is amenable to the Poisson random process with variable intensity (mean). We introduce expressions for traffic throughput, congestion as well as operator's waiting time for the typical intersection in a city and hereafter define an appropriate objective functional. Afterwards, we formulate an optimization problem and propose the sequential (or recursive) algorithm based on the principle of optimality (dynamic programming) due to Bellman. The solution if implemented is expected to improve throughput, reduce congestion, and promote driver's satisfaction. Because the dynamic programming method is computationally quite intensive, we consider the scenario that one unit traffic stream stands for a specific number of vehicles which actually depends on the volume of traffic flow through the intersection. The system is simulated with inputs described by several distinct nonhomogeneous Poisson processes. For example, we apply the typical traffic arrival rate in Canada with morning peak hour at around 7:30 AM and afternoon peak hour at around 4:30 PM whilst it is also applied with morning rush hour at about 8:00 AM and afternoon rush hour at about 6:00 PM like in China. In the meanwhile, we also present a group of numerical results for the traffic arrival rates that have shorter morning peak-hour period but longer afternoon rush hour period. This may occasionally happen when there are some social activities or big events in the afternoon. In addition, another series of experiments are carried out to illustrate the feasibility of the proposed dynamic model based on the traffic arrival rates with only one peak-hour throughout the whole day. The system is simulated with a series of experiments and the optimization problem is solved by dynamic programming based on the proposed algorithm which gives us the optimal feedback control law. More specifically, the results show that both the optimal traffic light timing allocated for each stream and the congestion broadcast level (CBL) of each road segment during each time segment are found. Accordingly, the corresponding optimal cost can be found for any given initial condition. It is reasonably believed that this stochastic dynamic model would be potentially applicable for real time adaptive traffic control system.

Congestionamentos no trânsito urbano são uma preocupação principal em grandes cidades pelo mundo, devido a seus impactos negativos multifacetados na saúde humana, no meio ambiente e na economia. A urbanização crescente, e seu consequente aumento no volume do trânsito, causam ainda mais congestionamentos por causa do ritmo lento - e, em alguns casos, inexistente - das melhoras na infraestrutura urbana. Uma solução com bom custo-benefício para reduzir o tempo médio de viagem dos veículos e prevenir os congestionamentos é o controle do trânsito urbano. No entanto, a maior parte das abordagens de controle do trânsito urbano adota um ciclo de controle fixo, o qual limita o desempenho de controle devido à consequente inabilidade de agir quando necessário. Ao contrário dessas abordagens, esse trabalho propõe uma rede neural bioinspirada que monitora o estado do sistema de forma contínua e é capaz de agir em qualquer momento. A rede neural bioinspirada proposta adota plasticidade intrínseca e inibição lateral para gerar uma competição natural entre os neurônios, a qual determina quais semáforos devem ser ativados em cada momento. Além disso, interneurônios inibitórios são adotados para coordenar intersecções vizinhas e melhorar os fluxos de veículos. Devido à grande quantidade de possíveis combinações dos parâmetros, um método para determinar o comportamento do modelo de acordo com as características intrínsecas da rede neural bioinspirada também é proposto. A convergência e a estabilidade do modelo proposto são avaliadas por seus pontos-fixos e autovalores, respectivamente. Ademais, o tempo de processamento e a complexidade computacional da rede neural bioinspirada também são avaliados. Por fim, o desempenho do modelo para diferentes demandas de veículos e situações do trânsito é avaliado com um simulador de mobilidade urbana e comparado a um método de controle adaptativo. / Traffic congestions are a major concern for big cities around the world due to its multifaceted negative impacts on human health, the environment and the economy. Growing urbanization, and the consequent increase in traffic volume, causes even more traffic congestions due to the slow-paced - and, in some cases, non-existing - improvements in the urban traffic infrastructure. A cost-effective solution to reduce vehicle travel times and prevent traffic congestions is traffic signal control. However, most approaches to traffic signal control adopt a fixed control cycle, which limits control performance due to the consequent inability to act when necessary. Contrary to these approaches, this work proposes a biologically-inspired neural network that monitors the system state continuously and can act upon it at any moment. The biologically-inspired neural network proposed adopts intrinsic plasticity and lateral inhibition to generate natural competition among neurons, determining which semaphores should be active at each moment. Furthermore, inhibitory interneurons are also adopted to coordinate neighboring intersections and to improve vehicle flows. Due to the large number of parameter combinations, a method to determine the model behavior according to the intrinsic characteristics of the biologically-inspired neural network is also proposed. Model convergence and stability are evaluated by its fixed-points and eigenvalues, respectively. Moreover, the computation time and computational complexity of the biologically-inspired neural network are also evaluated. Finally, the model performance for different vehicle demands and traffic situations is evaluated with a simulator of urban mobility and compared to an adaptive control method.