Flow of traffic on freeways and limited access highways can be represented as a series of kinemetic waves. Solutions to these systems of equations become problematic under congested traffic flow conditions, and under complicated (real-world) networks. A simplified theory of kinematics waves was previously proposed. Simplifying elements includes translation of the problem to moving coordinate system, adoption of bi-linear speed-density relationships, and adoption of restrictive constraints at the on- and off-ramps. However, these simplifying assumptions preclude application of this technique to most practical situations. This research explores the limitations of the simplified theory of kinematic waves. First this research documents a relaxation of several key constraints. In the original theory, priority was given to on-ramp merging vehicles so that they can bypass any queue at the merge. This research proposes to relax this constraint using a capacity-based weighted fair queuing (CBWFQ) merge model. In the original theory, downstream queue affects upstream traffic as a whole and exiting traffic can always be able to leave as long as it gets to the diverge. This research proposes that this diverge constraint be replaced with a contribution-based weighted splitting (CBWS) diverge model. This research proposes a revised notation system, permitting the solution techniques to be extended to freeway networks with multiple freeways and their ramps. This research proposes a generalization to permit application of the revised theory to general transportation networks. A generalized CBWFQ merge model and a generalized CBWS diverge model are formulated to deal with merging and diverging traffic. Finally, this research presents computational procedure for solving the new system of equations. Comparisons of model predictions with field observations are conducted on GA 400 in Atlanta. Investigations into the performance of the proposed CBWFQ and CBWS models are conducted. Results are quite encouraging, quantitative measures suggest satisfactory accuracy with narrow confidence interval.

Le problème de la gestion éco-responsable du trafic urbain est adressé. Ce type de gestion du trafic vise à réduire les arrêts des véhicules, les accélérations, la consommation énergétique, ainsi que la congestion. L'éco-management du trafic dans les réseaux urbains peut être catégorisé dans deux classes principales : contrôle du véhicule et contrôle de l'infrastructure. Les deux domaines de contrôle peuvent présenter caractéristiques soit isolées soit coordonnées, en dépendant du type d'information utilisée dans l'optimisation.La gestion du trafic côté véhicule influe sur chaque véhicule en fonction de ses propres caractéristiques et position. Le contrôle isolé du véhicule vise principalement à optimiser la transmission et/ou le profil de conduite des véhicules, en utilisant éventuellement des informations sur les caractéristiques de la route, mais sans communiquer avec les autres agents du réseau. Le contrôle coordonné du véhicule, d'autre part, fait usage de la communication entre les véhicules et avec l'infrastructure pour obtenir des bénéfices plus importants en termes de consommation d'énergie et de fluidité de la circulation.En revanche, la gestion du côté infrastructure influe sur les feux et les panneaux de signalisation, afin d'améliorer les performances de l'ensemble du trafic. Le contrôle isolé de l'infrastructure régule essentiellement les feux de signalisation pour une seule intersection, ou bien les limites de vitesse dans un seul tronçon de route, sans prendre en compte les interactions avec les jonctions et/ou les sections voisines. Le contrôle coordonné de l'infrastructure surmonte cette limitation en utilisant des informations sur les conditions de circulation dans d'autres sections de la route, afin de réduire la congestion.Les contributions de ce travail peuvent être résumées comme suit.Tout d'abord, une solution pour le contrôle coordonné du véhicule a été proposée, dans laquelle la communication avec l'infrastructure est exploitée pour réduire la consommation d'énergie. En particulier, les plans des feux de signalisation sont supposés être communiqués au véhicule et connus, et une vitesse optimale est suggérée au véhicule afin de traverser une séquence de carrefours à feux sans s'arrêter, tout en suivant une trajectoire d'énergie minimale. La stratégie proposée, appliquée indépendamment à chaque véhicule, a été testée dans un simulateur de trafic microscopique afin d'évaluer l'impact sur les performances du trafic. L'analyse a montré que la consommation d'énergie et le nombre d'arrêts peuvent être considérablement réduits sans affecter le temps de parcours.Ensuite, une solution pour le contrôle isolé de l'infrastructure a été proposée. Un modèle macroscopique du trafic urbain a été introduit, et les limites de vitesse variables ont été utilisées pour améliorer les performances de la circulation. L'optimisation vise à trouver un compromis entre la réduction de consommation énergétique et le temps de parcours moyen des véhicules dans le tronçon de route considéré. Des expériences ont démontré qu'il existe une limite de vitesse optimale qui améliore les performances du trafic, et qui réduit la longueur de la file d'attente au feu de signalisation.Enfin, une solution pour le contrôle coordonné de l'infrastructure a été proposée. La synchronisation des feux de signalisation sur les grands axes de circulation a été prouvée efficace pour réduire le temps de parcours. Notre analyse a démontré qu'un problème d'optimisation peut être formalisé pour prendre en compte également les aspects énergétiques. Des expériences approfondies dans un simulateur de trafic microscopique ont montré qu'il existe une corrélation entre la progression du trafic et ses performances. La stratégie de contrôle proposée a montré qu'une réduction significative de la consommation d'énergie peut être atteinte, en éliminant presque complètement les arrêts et le temps d'arrêt, sans affecter le temps de parcours. / The problem of energy-aware traffic management in urban environment is addressed. Such traffic management aims at reducing vehicle stops, accelerations, energy consumption, and ultimately congestion. The eco-management in urban traffic networks may be divided in two broad categories: vehicle-side control and infrastructure-side control. Both control domains can feature isolated or coordinated characteristics, depending on the type of information used in the optimization.The vehicle-side traffic management influences each single vehicle according to its own characteristics and position. Isolated vehicle control aims primarily at optimizing the powertrain and/or the driving profile of the vehicles, possibly using information about the road characteristics, but without communicating with the other agents of the traffic network. Coordinated vehicle control makes use of communication among vehicles and with the infrastructure in order to achieve larger benefits in terms of energy consumption and traffic fluidity.The infrastructure-side management, on the other hand, influences traffic lights and road side panels in order to improve the performance of the traffic as a whole. Isolated infrastructure control regulates essentially the traffic lights at a single signalized intersection, or the speed limits in a single stretch of road, without taking into account the interactions with the neighboring junctions and/or road sections. Coordinated infrastructure control overcomes this limitation by using information about traffic conditions in other road sections to alleviate congestion.The contributions of this work to the energy-aware traffic management may be summarized as follows.Firstly, a solution for the coordinated vehicle control has been proposed, in which communication with the infrastructure is exploited to reduce energy consumption. In particular, the traffic lights timings are assumed to be communicated to the vehicle and known, and the vehicle is suggested an optimal speed to drive through a sequence of signalized intersections without stopping, while following a minimum-energy trajectory. The proposed strategy, independently applied to each vehicle, has been tested in a microscopic traffic simulator in order to assess the impact on the traffic performance. The analysis has demonstrated that the energy consumption and the number of stops can be drastically reduced without affecting the travel time.Then, a solution for the isolated infrastructure control has been proposed. A macroscopic urban traffic model has been introduced, and the variable speed limits have been used as actuation to improve traffic performance. In particular, the analysis has been carried out at saturated traffic conditions, with given and fixed traffic lights scheduling. The optimization aims at reducing the energy consumption in trade-off with the average travel time of the vehicles in the considered road section. Experiments have demonstrated that there exists an optimal speed limit that improves traffic performance and reduces the length of the queue at the traffic light.Lastly, a solution for the coordinated infrastructure control has been proposed. Traffic lights coordination on arterials has been proved to be effective in terms of traffic delay reduction. Our analysis has demonstrated that an optimization problem can be cast to take into account also energetic aspects. Extensive experiments in a microscopic traffic simulator have showed that a correlation exists between traffic progression and traffic performance indexes, such as energy consumption, travel time, idling time, and number of stops. The proposed control strategy has showed that a significant reduction of energy consumption can be achieved, almost completely eliminating number of stops and idling time, without affecting the travel time.

O rápido crescimento das regiões urbanas tem impacto significativo nos sistemas de tráfego e transportes. Políticas de gerenciamento e estratégias de planejamento alternativas são claramente necessárias para o tratamento da capacidade limitada, e cada vez mais deficitária, das redes viárias. O conceito de Sistemas Inteligentes de Transportes (ITS) surge neste cenário; mais do que procurar aumentar a capacidade por meio de modificações físicas na infraestrutura, sua premissa baseia-se na utilização de tecnologias avançadas de comunicação e computação para melhor gerir os recursos de tráfego e transportes atuais. Influenciar o padrão do comportamento dos usuários é um desafio que tem estimulado muita pesquisa na área de ITS, onde fatores humanos passam a ter grande importância na modelagem, simulação e avaliação dessa abordagem inovadora. Este trabalho tem como foco a utilização de Sistemas Multiagentes (MAS) na representação dos sistemas de tráfego e transporte, com base nas novasmedidas de desempenho impostas pelas tecnologias ITS. As características de agentes têm grande potencial para representar componentes geográfica e funcionalmente distribuídos, como a maioria dos elementos no domínio da aplicação. Uma arquitetura BDI (beliefs, desires, intentions) é apresentada como alternativa a modelos tradicionais, usados para representar o comportamento do motorista em simulação microscópica, considerando-se a representação explícita dos estados mentais dos usuários. Os conceitos básicos de ITS e MAS são apresentados, assim como exemplos de aplicações relacionados com o tema do trabalho. Esta foi a motivação para a extensão de um simulador microscópico existente, no sentido de incorporar as características dos MAS para melhorar a representação dos motoristas. Assim, a demanda é gerada a partir de uma população de agentes, resultando da decisão sobre a rota e o tempo de partida ao longo de vários dias. O modelo estendido, que passa a suportar a interação de motoristas BDI, foi efetivamente implementado e foram executados diferentes experimentos para testar a abordagem em cenários de tráfego urbano. MAS permite uma abordagem direcionada a processos que facilita a construção de representações modulares, robustas, e extensíveis, características pouco presentes em abordagens voltadas ao resultado. Suas premissas de abstração permitem uma associação direta entre modelo e implementação. Incerteza e variabilidade são assim tratadas de maneira mais intuitiva, uma vez que arquiteturas cognitivas permitem uma fácil representação do comportamento humano na estrutura do motorista. Desta forma, MAS estende a simulação microscópica de tráfego no sentido de melhor representar a complexidade inerente às tecnologias ITS. / The rapid growth of urban areas has a significant impact on traffic and transportation systems. New management policies and planning strategies are clearly necessary to cope with the more than ever limited capacity of existing road networks. The concept of Intelligent Transportation System (ITS) arises in this scenario; rather than attempting to increase road capacity by means of physical modifications to the infrastructure, the premise of ITS relies on the use of advanced communication and computer technologies to handle today’s traffic and transportation facilities. Influencing users’ behaviour patterns is a challenge that has stimulated much research in the ITS field, where human factors start gaining great importance to modelling, simulating, and assessing such an innovative approach. This work is aimed at using Multi-agent Systems (MAS) to represent the traffic and transportation systems in the light of the new performance measures brought about by ITS technologies. Agent features have good potentialities to represent those components of a system that are geographically and functionally distributed, such as most components in traffic and transportation. A BDI (beliefs, desires, and intentions) architecture is presented as an alternative to traditional models used to represent the driver behaviour within microscopic simulation allowing for an explicit representation of users’ mental states. Basic concepts of ITS and MAS are presented, as well as some application examples related to the subject. This has motivated the extension of an existing microscopic simulation framework to incorporate MAS features to enhance the representation of drivers. This way demand is generated from a population of agents as the result of their decisions on route and departure time, on a daily basis. The extended simulation model that now supports the interaction of BDI driver agents was effectively implemented, and different experiments were performed to test this approach in commuter scenarios. MAS provides a process-driven approach that fosters the easy construction of modular, robust, and scalable models, characteristics that lack in former result-driven approaches. Its abstraction premises allow for a closer association between the model and its practical implementation. Uncertainty and variability are addressed in a straightforward manner, as an easier representation of humanlike behaviours within the driver structure is provided by cognitive architectures, such as the BDI approach used in this work. This way MAS extends microscopic simulation of traffic to better address the complexity inherent in ITS technologies.

A crescente complexidade das redes de comunicação de dados tem como conseqüência direta tornar cada vez mais complexas as tarefas de projetar seu dimensionamento e evolução. Um passo preliminar vitalmente importante no projeto de uma rede é a coleta de dados relacionados ao uso esperado da rede. Os padrões de tráfego estimados são usados nos cálculos de dimensionamento dos recursos. Fortemente relacionada a estas atividades está a previsão da performance em termos de throughput da rede, tempos de resposta, probabilidade de congestionamento, entre outras. Uma análise de performance efetiva depende de se representar precisamente a configuração da rede e da carga de tráfego a ela submetida. Técnicas de modelagem analítica ou de simulação podem ser usadas para determinar a performance esperada do sistema. Neste trabalho a usada a técnica de simulação para modelar o tráfego da rede usando dados coletados em uma rede real por monitoração. Trabalhando-se com o modelo validado e possível ajustar a carga de tráfego para representar mudanças esperadas nos volumes de tráfego, e testar o efeito na performance de diferentes configurações de rede. Isso possibilita que os processos de planejamento e projeto sejam executados com confiança, alem dos custos da rede poderem ser otimizados. O sistema apresentado neste trabalho e orientado à gerência de performance de redes, apoiando as atividades de gerência e planejamento de capacidade. Defende-se a idéia de que, a partir do perfil dos usuários de uma rede, derivado do tráfego por eles gerado, pode-se construir um modelo de tráfego significativamente representativo para a análise e previsão do desempenho da rede. O modelo foi desenvolvido para redes de comutação de pacotes com serviço orientado a conexão, e validado sobre o tráfego de uma rede X.25 através de um modelo de simulação, implementado em GPSS. O protótipo do sistema é composto por três processos principais que incorporam as etapas da análise de performance: monitoração do tráfego, análise do tráfego e simulação do tráfego. Na monitoração, são contabilizadas uma série de variáveis para cada conexão gerada na rede. Para cada tipo de aplicação são calculados os respectivos fatores de carga de tráfego. As aplicações são classificadas em dois grandes grupos: interativas e não-interativas. O processo de análise do tráfego gera os fatores de carga de tráfego na forma de tabelas e gráficos. As conexões e as métricas de performance produzidas na simulação do tráfego podem ser analisadas e validadas graficamente. O sistema proposto foi especificado em SDL (Specification and Description Language). / The increasing complexity of communication networks turns each time more complex the task to plan its expansion and evolution. A vitally important preliminar step in computer network design is the gathering of data relating to the expected use of the network. The estimated traffic patterns are used in resource usage computations. Closely related to these activities is the estimation of performance in terms of network throughput, response times, congestion probability, among others. An efective performance analysis requires the accurate representation of computer network configuration and traffic load. Analytical or simulation techniques can be used to establish the expected system performance. This work makes use of simulation to model the network traffic, based on collected data of a real network. network. Working with the validated model is possible to adjust the traffic load to represent future demand, and to test the performance impact under distinct network configurations. This enable a trustful execution of network planning and design, besides of the cost optimization. The system presented at this work is network performance management oriented, giving support to capacity management and capacity planning. The central idea is that, through network users profile achieved from generated traffic, it's possible to build a representative traffic model to aim at the network performance analysis and forecast. The model was developed to connection oriented packet switched networks, and validated by means of a simulation model to X.25 network traffic, implementd in GPSS. The system prototype comprises three process: traffic monitoring, traffic analysis and traffic simulation. In monitoring, a set of variables are registered for each network connection generated. The traffic load factors are computed according the application type. The applications are classified in two groups: interactive and non-interactive. The traffic analysis process produces traffic load factors in form of tables and graphics. Connections and performance metrics generated at traffic simulation can be graphically analyzed and validated. The proposed system was specified in SDL (Specification and Description Language).

A crescente complexidade das redes de comunicação de dados tem como conseqüência direta tornar cada vez mais complexas as tarefas de projetar seu dimensionamento e evolução. Um passo preliminar vitalmente importante no projeto de uma rede é a coleta de dados relacionados ao uso esperado da rede. Os padrões de tráfego estimados são usados nos cálculos de dimensionamento dos recursos. Fortemente relacionada a estas atividades está a previsão da performance em termos de throughput da rede, tempos de resposta, probabilidade de congestionamento, entre outras. Uma análise de performance efetiva depende de se representar precisamente a configuração da rede e da carga de tráfego a ela submetida. Técnicas de modelagem analítica ou de simulação podem ser usadas para determinar a performance esperada do sistema. Neste trabalho a usada a técnica de simulação para modelar o tráfego da rede usando dados coletados em uma rede real por monitoração. Trabalhando-se com o modelo validado e possível ajustar a carga de tráfego para representar mudanças esperadas nos volumes de tráfego, e testar o efeito na performance de diferentes configurações de rede. Isso possibilita que os processos de planejamento e projeto sejam executados com confiança, alem dos custos da rede poderem ser otimizados. O sistema apresentado neste trabalho e orientado à gerência de performance de redes, apoiando as atividades de gerência e planejamento de capacidade. Defende-se a idéia de que, a partir do perfil dos usuários de uma rede, derivado do tráfego por eles gerado, pode-se construir um modelo de tráfego significativamente representativo para a análise e previsão do desempenho da rede. O modelo foi desenvolvido para redes de comutação de pacotes com serviço orientado a conexão, e validado sobre o tráfego de uma rede X.25 através de um modelo de simulação, implementado em GPSS. O protótipo do sistema é composto por três processos principais que incorporam as etapas da análise de performance: monitoração do tráfego, análise do tráfego e simulação do tráfego. Na monitoração, são contabilizadas uma série de variáveis para cada conexão gerada na rede. Para cada tipo de aplicação são calculados os respectivos fatores de carga de tráfego. As aplicações são classificadas em dois grandes grupos: interativas e não-interativas. O processo de análise do tráfego gera os fatores de carga de tráfego na forma de tabelas e gráficos. As conexões e as métricas de performance produzidas na simulação do tráfego podem ser analisadas e validadas graficamente. O sistema proposto foi especificado em SDL (Specification and Description Language). / The increasing complexity of communication networks turns each time more complex the task to plan its expansion and evolution. A vitally important preliminar step in computer network design is the gathering of data relating to the expected use of the network. The estimated traffic patterns are used in resource usage computations. Closely related to these activities is the estimation of performance in terms of network throughput, response times, congestion probability, among others. An efective performance analysis requires the accurate representation of computer network configuration and traffic load. Analytical or simulation techniques can be used to establish the expected system performance. This work makes use of simulation to model the network traffic, based on collected data of a real network. network. Working with the validated model is possible to adjust the traffic load to represent future demand, and to test the performance impact under distinct network configurations. This enable a trustful execution of network planning and design, besides of the cost optimization. The system presented at this work is network performance management oriented, giving support to capacity management and capacity planning. The central idea is that, through network users profile achieved from generated traffic, it's possible to build a representative traffic model to aim at the network performance analysis and forecast. The model was developed to connection oriented packet switched networks, and validated by means of a simulation model to X.25 network traffic, implementd in GPSS. The system prototype comprises three process: traffic monitoring, traffic analysis and traffic simulation. In monitoring, a set of variables are registered for each network connection generated. The traffic load factors are computed according the application type. The applications are classified in two groups: interactive and non-interactive. The traffic analysis process produces traffic load factors in form of tables and graphics. Connections and performance metrics generated at traffic simulation can be graphically analyzed and validated. The proposed system was specified in SDL (Specification and Description Language).

O rápido crescimento das regiões urbanas tem impacto significativo nos sistemas de tráfego e transportes. Políticas de gerenciamento e estratégias de planejamento alternativas são claramente necessárias para o tratamento da capacidade limitada, e cada vez mais deficitária, das redes viárias. O conceito de Sistemas Inteligentes de Transportes (ITS) surge neste cenário; mais do que procurar aumentar a capacidade por meio de modificações físicas na infraestrutura, sua premissa baseia-se na utilização de tecnologias avançadas de comunicação e computação para melhor gerir os recursos de tráfego e transportes atuais. Influenciar o padrão do comportamento dos usuários é um desafio que tem estimulado muita pesquisa na área de ITS, onde fatores humanos passam a ter grande importância na modelagem, simulação e avaliação dessa abordagem inovadora. Este trabalho tem como foco a utilização de Sistemas Multiagentes (MAS) na representação dos sistemas de tráfego e transporte, com base nas novasmedidas de desempenho impostas pelas tecnologias ITS. As características de agentes têm grande potencial para representar componentes geográfica e funcionalmente distribuídos, como a maioria dos elementos no domínio da aplicação. Uma arquitetura BDI (beliefs, desires, intentions) é apresentada como alternativa a modelos tradicionais, usados para representar o comportamento do motorista em simulação microscópica, considerando-se a representação explícita dos estados mentais dos usuários. Os conceitos básicos de ITS e MAS são apresentados, assim como exemplos de aplicações relacionados com o tema do trabalho. Esta foi a motivação para a extensão de um simulador microscópico existente, no sentido de incorporar as características dos MAS para melhorar a representação dos motoristas. Assim, a demanda é gerada a partir de uma população de agentes, resultando da decisão sobre a rota e o tempo de partida ao longo de vários dias. O modelo estendido, que passa a suportar a interação de motoristas BDI, foi efetivamente implementado e foram executados diferentes experimentos para testar a abordagem em cenários de tráfego urbano. MAS permite uma abordagem direcionada a processos que facilita a construção de representações modulares, robustas, e extensíveis, características pouco presentes em abordagens voltadas ao resultado. Suas premissas de abstração permitem uma associação direta entre modelo e implementação. Incerteza e variabilidade são assim tratadas de maneira mais intuitiva, uma vez que arquiteturas cognitivas permitem uma fácil representação do comportamento humano na estrutura do motorista. Desta forma, MAS estende a simulação microscópica de tráfego no sentido de melhor representar a complexidade inerente às tecnologias ITS. / The rapid growth of urban areas has a significant impact on traffic and transportation systems. New management policies and planning strategies are clearly necessary to cope with the more than ever limited capacity of existing road networks. The concept of Intelligent Transportation System (ITS) arises in this scenario; rather than attempting to increase road capacity by means of physical modifications to the infrastructure, the premise of ITS relies on the use of advanced communication and computer technologies to handle today’s traffic and transportation facilities. Influencing users’ behaviour patterns is a challenge that has stimulated much research in the ITS field, where human factors start gaining great importance to modelling, simulating, and assessing such an innovative approach. This work is aimed at using Multi-agent Systems (MAS) to represent the traffic and transportation systems in the light of the new performance measures brought about by ITS technologies. Agent features have good potentialities to represent those components of a system that are geographically and functionally distributed, such as most components in traffic and transportation. A BDI (beliefs, desires, and intentions) architecture is presented as an alternative to traditional models used to represent the driver behaviour within microscopic simulation allowing for an explicit representation of users’ mental states. Basic concepts of ITS and MAS are presented, as well as some application examples related to the subject. This has motivated the extension of an existing microscopic simulation framework to incorporate MAS features to enhance the representation of drivers. This way demand is generated from a population of agents as the result of their decisions on route and departure time, on a daily basis. The extended simulation model that now supports the interaction of BDI driver agents was effectively implemented, and different experiments were performed to test this approach in commuter scenarios. MAS provides a process-driven approach that fosters the easy construction of modular, robust, and scalable models, characteristics that lack in former result-driven approaches. Its abstraction premises allow for a closer association between the model and its practical implementation. Uncertainty and variability are addressed in a straightforward manner, as an easier representation of humanlike behaviours within the driver structure is provided by cognitive architectures, such as the BDI approach used in this work. This way MAS extends microscopic simulation of traffic to better address the complexity inherent in ITS technologies.

O rápido crescimento das regiões urbanas tem impacto significativo nos sistemas de tráfego e transportes. Políticas de gerenciamento e estratégias de planejamento alternativas são claramente necessárias para o tratamento da capacidade limitada, e cada vez mais deficitária, das redes viárias. O conceito de Sistemas Inteligentes de Transportes (ITS) surge neste cenário; mais do que procurar aumentar a capacidade por meio de modificações físicas na infraestrutura, sua premissa baseia-se na utilização de tecnologias avançadas de comunicação e computação para melhor gerir os recursos de tráfego e transportes atuais. Influenciar o padrão do comportamento dos usuários é um desafio que tem estimulado muita pesquisa na área de ITS, onde fatores humanos passam a ter grande importância na modelagem, simulação e avaliação dessa abordagem inovadora. Este trabalho tem como foco a utilização de Sistemas Multiagentes (MAS) na representação dos sistemas de tráfego e transporte, com base nas novasmedidas de desempenho impostas pelas tecnologias ITS. As características de agentes têm grande potencial para representar componentes geográfica e funcionalmente distribuídos, como a maioria dos elementos no domínio da aplicação. Uma arquitetura BDI (beliefs, desires, intentions) é apresentada como alternativa a modelos tradicionais, usados para representar o comportamento do motorista em simulação microscópica, considerando-se a representação explícita dos estados mentais dos usuários. Os conceitos básicos de ITS e MAS são apresentados, assim como exemplos de aplicações relacionados com o tema do trabalho. Esta foi a motivação para a extensão de um simulador microscópico existente, no sentido de incorporar as características dos MAS para melhorar a representação dos motoristas. Assim, a demanda é gerada a partir de uma população de agentes, resultando da decisão sobre a rota e o tempo de partida ao longo de vários dias. O modelo estendido, que passa a suportar a interação de motoristas BDI, foi efetivamente implementado e foram executados diferentes experimentos para testar a abordagem em cenários de tráfego urbano. MAS permite uma abordagem direcionada a processos que facilita a construção de representações modulares, robustas, e extensíveis, características pouco presentes em abordagens voltadas ao resultado. Suas premissas de abstração permitem uma associação direta entre modelo e implementação. Incerteza e variabilidade são assim tratadas de maneira mais intuitiva, uma vez que arquiteturas cognitivas permitem uma fácil representação do comportamento humano na estrutura do motorista. Desta forma, MAS estende a simulação microscópica de tráfego no sentido de melhor representar a complexidade inerente às tecnologias ITS. / The rapid growth of urban areas has a significant impact on traffic and transportation systems. New management policies and planning strategies are clearly necessary to cope with the more than ever limited capacity of existing road networks. The concept of Intelligent Transportation System (ITS) arises in this scenario; rather than attempting to increase road capacity by means of physical modifications to the infrastructure, the premise of ITS relies on the use of advanced communication and computer technologies to handle today’s traffic and transportation facilities. Influencing users’ behaviour patterns is a challenge that has stimulated much research in the ITS field, where human factors start gaining great importance to modelling, simulating, and assessing such an innovative approach. This work is aimed at using Multi-agent Systems (MAS) to represent the traffic and transportation systems in the light of the new performance measures brought about by ITS technologies. Agent features have good potentialities to represent those components of a system that are geographically and functionally distributed, such as most components in traffic and transportation. A BDI (beliefs, desires, and intentions) architecture is presented as an alternative to traditional models used to represent the driver behaviour within microscopic simulation allowing for an explicit representation of users’ mental states. Basic concepts of ITS and MAS are presented, as well as some application examples related to the subject. This has motivated the extension of an existing microscopic simulation framework to incorporate MAS features to enhance the representation of drivers. This way demand is generated from a population of agents as the result of their decisions on route and departure time, on a daily basis. The extended simulation model that now supports the interaction of BDI driver agents was effectively implemented, and different experiments were performed to test this approach in commuter scenarios. MAS provides a process-driven approach that fosters the easy construction of modular, robust, and scalable models, characteristics that lack in former result-driven approaches. Its abstraction premises allow for a closer association between the model and its practical implementation. Uncertainty and variability are addressed in a straightforward manner, as an easier representation of humanlike behaviours within the driver structure is provided by cognitive architectures, such as the BDI approach used in this work. This way MAS extends microscopic simulation of traffic to better address the complexity inherent in ITS technologies.

When further demands are placed on emissions and performance of cars, trucks and busses, the vehicle manufacturers are looking to have cheap ways to evaluate their products for specific customers' needs. Using simulation tools to quickly compare use cases instead of manually recording data is a possible way forward. However, existing traffic simulation tools do not provide enough detail in each vehicle for the driving to represent real life driving patterns with regards to road features. For the purpose of this thesis data has been recorded by having different people drive a specific route featuring highway driving, traffic lights and many curves. Using this data, models have then been estimated that describe how human drivers adjust their speed through curves, how long braking distances typically are with respect to the driving speed, and the varying deceleration during braking sequences. An additional model has also been created that produces a speed variation when driving on highways. In the end all models are implemented in Matlab using a traffic control interface to interact with the traffic simulation tool SUMO. The results of this work are promising with the improved simulation being able to replicate the most significant characteristics seen from human drivers when approaching curves, traffic lights and intersections.

L'analyse et la prévision du comportement des réseaux de transport sont aujourd'hui des éléments cruciaux pour la mise en place de politiques de gestion territoriale. La simulation informatique du trafic routier est un outil puissant permettant de tester des stratégies de gestion avant de les déployer dans un contexte opérationnel. La simulation du trafic à l'échelle d'un ville requiert cependant une puissance de calcul très importante, dépassant les capacité d'un seul ordinateur.Dans cette thèse, nous étudions des méthodes permettant d'effectuer des simulations de trafic multi-agent à large échelle. Nous proposons des solutions permettant de distribuer l'exécution de telles simulations sur un grand nombre de coe urs de calcul. L'une d'elle distribue directement les agents sur les coeurs disponibles, tandis que la seconde découpe l'environnement sur lequel les agents évoluent. Les méthodes de partitionnement de graphes sont étudiées à cet effet, et nous proposons une procédure de partitionnement spécialement adaptée à la simulation de trafic multi-agent. Un algorithme d'équilibrage de charge dynamique est également développé, afin d'optimiser les performances de la distribution de la simulation microscopique.Les solutions proposées ont été éprouvées sur un réseau réel représentant la zone de Paris-Saclay.Ces solutions sont génériques et peuvent être appliquées sur la plupart des simulateurs existants.Les résultats montrent que la distribution des agents améliore grandement les performances de la simulation macroscopique, tandis que le découpage de l'environnement est plus adapté à la simulation microscopique. Notre algorithme d'équilibrage de charge améliore en outre significativement l'efficacité de la distribution de l'environnement / Nowadays, analysis and prediction of transport network behavior are crucial elements for the implementation of territorial management policies. Computer simulation of road traffic is a powerful tool for testing management strategies before deploying them in an operational context. Simulation of city-wide traffic requires significant computing power exceeding the capacity of a single computer.This thesis studies the methods to perform large-scale multi-agent traffic simulations. We propose solutions allowing the distribution of such simulations on a large amount of computing cores.One of them distributes the agents directly on the available cores, while the second splits the environment on which the agents evolve. Graph partitioning methods are studied for this purpose, and we propose a partitioning procedure specially adapted to the multi-agent traffic simulation. A dynamic load balancing algorithm is also developed to optimize the performance of the microscopic simulation distribution.The proposed solutions have been tested on a real network representing the Paris-Saclay area.These solutions are generic and can be applied to most existing simulators.The results show that the distribution of the agents greatly improves the performance of the macroscopic simulation, whereas the environment distribution is more suited to microscopic simulation. Our load balancing algorithm also significantly improves the efficiency of the environment based distribution

Cette thèse est consacrée à la conception et au développement d’une plateforme multi-agent, en réalité virtuelle, de pilotage de véhicules intelligents pour la simulation du comportement microscopique du trafic. D’abord, un système de simulation intelligent des véhicules en réalité virtuelle (VR-ISSV), basé sur les multi-agents, est proposé : c’est un système modulaire hiérarchique de modélisation et de simulation, comprenant une couche matérielle, réseau et système d’exploitation ; une couche de gestion de la visualisation ; une couche de multi-agents et une couche d’interface homme-machine. Ensuite, pour le modèle d’agent du véhicule intelligent, un paradigme de conception décentralisée est utilisé basé sur l’approche multi-contrôleurs, où le comportement du suivi des véhicules et le comportement du dépassement des véhicules sont réalisées par coordination entre multi-contrôleurs. L’agent d’environnement est construit en tenant compte de l’interaction entre les véhicules et l’environnement naturel synthétique. Un système d’information géographique (GIS) est par ailleurs utilisé afin de définir l’agent d’environnement. Enfin, pour assurer la sécurité dans les manœuvres microscopiques du trafic, plusieurs contrôleurs du véhicule intelligent, adaptés à l’environnement complexe, sont considérés. Les contrôleurs, basés sur la logique floue, sont proposés pour envoyer les commandes appropriées aux actionneurs du véhicule - volant de direction, accélérateur, frein... Les modèles de comportement microscopique du trafic basé sur l’agent de véhicule intelligent sont étudiés considérant différents scénarios et l’environnement / This PhD thesis is dedicated to the modeling and simulation of microscopic traffic behavior in virtual reality system, with the intent of providing a new approach to effectively ensure traffic safety. At first, Virtual Reality Intelligent Simulation System of Vehicles (VR-ISSV), based on multi-agent, is proposed to simulate the intelligent microscopic traffic, which is a hierarchical modular modeling and simulation system consisting of hardware, network and operating system layers, visualization management layer, multi-agent layer, human-machine interface layer. The multi-agent layer includes entity agents (intelligent vehicle agents and around vehicle agents), service agent and environment agent. Second, for the intelligent vehicle agent model, a decentralized design paradigm is used for developing the multi-controller based intelligent vehicle, whereby the car following behavior and the overtaking behavior could be realized by the coordination of the multi-controller. The environment agent is constructed based on the conception of Synthetic Natural Environment (SNE), taking into account the interaction between the vehicles and the natural environment. Geographic Information System (GIS) is used to establish environment agent. Finally, to ensure the safety in microscopic traffic maneuver, the intelligent vehicle controllers adapting to complex environment are considered. Fuzzy logic based controllers are designed for sending the appropriate outputs to the vehicle’s actuators – the steering wheel and the throttle/brake pedals. Microscopic traffic behavior models based on the intelligent vehicle agent involving environment are studied