Travel-time information is essential in Advanced Traveler Information Systems (ATISs) and Advanced Traffic Management Systems (ATMSs). A key component of these systems is the prediction of the spatiotemporal evolution of roadway traffic state and travel time. From the perspective of travelers, such information can result in better traveler route choice and departure time decisions. From the transportation agency perspective, such data provide enhanced information with which to better manage and control the transportation system to reduce congestion, enhance safety, and reduce the carbon footprint of the transportation system. The objective of the research presented in this dissertation is to develop a framework that includes three major categories of methodologies to predict the spatiotemporal evolution of the traffic state. The proposed methodologies include macroscopic traffic modeling, computer vision and recursive probabilistic algorithms. Each developed method attempts to predict traffic state, including roadway travel times, for different prediction horizons. In total, the developed multi-tool framework produces traffic state prediction algorithms ranging from short – (0~5 minutes) to medium-term (1~4 hours) considering departure times up to an hour into the future. The dissertation first develops a particle filter approach for use in short-term traffic state prediction. The flow continuity equation is combined with the Van Aerde fundamental diagram to derive a time series model that can accurately describe the spatiotemporal evolution of traffic state. The developed model is applied within a particle filter approach to provide multi-step traffic state prediction. The testing of the algorithm on a simulated section of I-66 demonstrates that the proposed algorithm can accurately predict the propagation of shockwaves up to five minutes into the future. The developed algorithm is further improved by incorporating on- and off-ramp effects and more realistic boundary conditions. Furthermore, the case study demonstrates that the improved algorithm produces a 50 percent reduction in the prediction error compared to the classic LWR density formulation. Considering the fact that the prediction accuracy deteriorates significantly for longer prediction horizons, historical data are integrated and considered in the measurement update in the developed particle filter approach to extend the prediction horizon up to half an hour into the future. The dissertation then develops a travel time prediction framework using pattern recognition techniques to match historical data with real-time traffic conditions. The Euclidean distance is initially used as the measure of similarity between current and historical traffic patterns. This method is further improved using a dynamic template matching technique developed as part of this research effort. Unlike previous approaches, which use fixed template sizes, the proposed method uses a dynamic template size that is updated each time interval based on the spatiotemporal shape of the congestion upstream of a bottleneck. In addition, the computational cost is reduced using a Fast Fourier Transform instead of a Euclidean distance measure. Subsequently, the historical candidates that are similar to the current conditions are used to predict the experienced travel times. Test results demonstrate that the proposed dynamic template matching method produces significantly better and more stable prediction results for prediction horizons up to 30 minutes into the future for a two hour trip (prediction horizon of two and a half hours) compared to other state-of-the-practice and state-of-the-art methods. Finally, the dissertation develops recursive probabilistic approaches including particle filtering and agent-based modeling methods to predict travel times further into the future. Given the challenges in defining the particle filter time update process, the proposed particle filtering algorithm selects particles from a historical dataset and propagates particles using data trends of past experiences as opposed to using a state-transition model. A partial resampling strategy is then developed to address the degeneracy problem in the particle filtering process. INRIX probe data along I-64 and I-264 from Richmond to Virginia Beach are used to test the proposed algorithm. The results demonstrate that the particle filtering approach produces less than a 10 percent prediction error for trip departures up to one hour into the future for a two hour trip. Furthermore, the dissertation develops an agent-based modeling approach to predict travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in the decision making system, which predicts the travel time for each time interval according to past experiences from a historical dataset. A set of agent interactions are developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents with negligible weights with new agents. Consequently, the aggregation of each agent's recommendation (predicted travel time with associated weight) provides a macroscopic level of output – predicted travel time distribution. The case study demonstrated that the agent-based model produces less than a 9 percent prediction error for prediction horizons up to one hour into the future. / Ph. D.

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A evolução tecnológica das redes de telecomunicações sem fio permite que organizações de redes mais inteligentes sejam vislumbradas. É possível imaginar um sistema de telefonia formado por dispositivos móveis autônomos que não necessite de nenhuma infraestrutura pré-estabelecida para trocar informações com seus vizinhos, de acordo com o alcance do raio de transmissão. Assim, as informações poderiam ser repassadas de nó em nó, formando uma rede de múltiplos saltos. A ausência de uma entidade central também poderia melhorar a tolerância a falhas do sistema, principalmente por gerar uma redundância de caminhos possíveis entre os nós. Analisamos o desempenho desse sistema em diferentes cenários e a sensibilidade à variação de parâmetros como o raio de transmissão, interferências, a quantidade de nós e número de saltos máximo permitido (TTL), e testamos estratégias de comunicação com raio fixo, raio variável, número de vizinhos mínimo e etc., através de modelagem e simulação baseada em agentes. De maneira geral, a estratégia de transmissão com raio variável apresentou a melhor taxa de mensagens recebidas e a menor média de saltos até o destino, porém com maior nível de energia do sistema. A estratégia de raio fixo apresentou a menor energia total gasta pelo sistema para enviar as mensagens, porém, com uma taxa menor de mensagens recebidas. Além disso, avaliamos que as principais causas de perdas de pacotes estão associadas com o aumento da mobilidade, a redução do TTL e as interferências, sendo que cada uma contribui mais ou menos de acordo com o cenário estudado. / The technological development of Wireless Networks leads to more intelligent networks structures. One can imagine a mobile data system consisting of autonomous mobile devices that do not require any pre-established infrastructure to exchange information one with another, limited mainly by the transmission radius. Thus, data could be forwarded from node to node, forming a multihop network. The absence of a central entity could also improve fault tolerance by allowing redundant paths for nodes to communicate. We analyzed the performance of the system in different scenarios and system behavior regarding parameters variations such as transmission radius, interferences, the number of nodes and maximum allowed number of hops (TTL), and tested communication strategies with fixed radius, variable radius, minimum number of neighbors to transmit, etc., through modeling and simulation-based agents. In general, variable radius strategy had the best rate of incoming messages and the lowest average number of hops to the destination. However it presented the higher level of system energy. In one hand, fixed radius strategy presented the lowest total energy expended by the system to send messages, but, in the other hand, the rate of incoming messages was lower. Furthermore, we discovered the main causes of packet losses are associated with increased mobility, reducing the TTL and interference, each of which contributes more or less in accordance with the scenario.

L'infection d'un organisme par un agent pathogène déclenche l'activation des lymphocytes T-CD8 et l'initiation de la réponse immunitaire. Il s'ensuit un programme complexe de prolifération et de différenciation des lymphocytes T-CD8, contrôlé par l'évolution de leur contenu moléculaire. Dans ce manuscrit, nous présentons deux modèles mathématiques de la réponse T-CD8. Le premier se présente comme une équation différentielle à impulsions grâce à laquelle nous étudions l'effet du partage inégal des protéines lors des divisions cellulaires sur la régulation de l'hétérogénéité moléculaire. Le second est un modèle à base d'agents couplant la description d'une population discrète de lymphocytes T-CD8 à celle du contenu moléculaire de ces derniers. Ce modèle s'avère capable de reproduire les différentes phases caractéristiques de la réponse T-CD8 aux échelle cellulaire et moléculaire. Ces deux travaux supportent l'hypothèse que la dynamique cellulaire observée in vivo est le reflet de l'hétérogénéité moléculaire qui structure la population de lymphocytes T-CD8 / Infection of an organism by a pathogen triggers the activation of the CD8 T-cells and the initiation of the immune response. The result is a complex program of proliferation and differentiation of the CD8 T-cells, controlled by the evolution of their molecular content. In this manuscript, we present two mathematical models of the CD8 T-cell response. The first one is presented as an impulsive differential equation by which we study the effect of unequal molecular partitioning at cell division on the regulation of molecular heterogeneity. The second one is an agent-based-model that couples the description of a discrete population of CD8 T-cells and that of their molecular content. This model can reproduce the different typical phases of the CD8 T-cell response at both the cellular and the molecular scales. These two studies support the hypothesis that the cell dynamics observed in vivo is a consequence of the molecular heterogeneity structuring the CD8 T-cell population

A evolução tecnológica das redes de telecomunicações sem fio permite que organizações de redes mais inteligentes sejam vislumbradas. É possível imaginar um sistema de telefonia formado por dispositivos móveis autônomos que não necessite de nenhuma infraestrutura pré-estabelecida para trocar informações com seus vizinhos, de acordo com o alcance do raio de transmissão. Assim, as informações poderiam ser repassadas de nó em nó, formando uma rede de múltiplos saltos. A ausência de uma entidade central também poderia melhorar a tolerância a falhas do sistema, principalmente por gerar uma redundância de caminhos possíveis entre os nós. Analisamos o desempenho desse sistema em diferentes cenários e a sensibilidade à variação de parâmetros como o raio de transmissão, interferências, a quantidade de nós e número de saltos máximo permitido (TTL), e testamos estratégias de comunicação com raio fixo, raio variável, número de vizinhos mínimo e etc., através de modelagem e simulação baseada em agentes. De maneira geral, a estratégia de transmissão com raio variável apresentou a melhor taxa de mensagens recebidas e a menor média de saltos até o destino, porém com maior nível de energia do sistema. A estratégia de raio fixo apresentou a menor energia total gasta pelo sistema para enviar as mensagens, porém, com uma taxa menor de mensagens recebidas. Além disso, avaliamos que as principais causas de perdas de pacotes estão associadas com o aumento da mobilidade, a redução do TTL e as interferências, sendo que cada uma contribui mais ou menos de acordo com o cenário estudado. / The technological development of Wireless Networks leads to more intelligent networks structures. One can imagine a mobile data system consisting of autonomous mobile devices that do not require any pre-established infrastructure to exchange information one with another, limited mainly by the transmission radius. Thus, data could be forwarded from node to node, forming a multihop network. The absence of a central entity could also improve fault tolerance by allowing redundant paths for nodes to communicate. We analyzed the performance of the system in different scenarios and system behavior regarding parameters variations such as transmission radius, interferences, the number of nodes and maximum allowed number of hops (TTL), and tested communication strategies with fixed radius, variable radius, minimum number of neighbors to transmit, etc., through modeling and simulation-based agents. In general, variable radius strategy had the best rate of incoming messages and the lowest average number of hops to the destination. However it presented the higher level of system energy. In one hand, fixed radius strategy presented the lowest total energy expended by the system to send messages, but, in the other hand, the rate of incoming messages was lower. Furthermore, we discovered the main causes of packet losses are associated with increased mobility, reducing the TTL and interference, each of which contributes more or less in accordance with the scenario.

Economic changes such as rising açaí prices and the availability of off-farm employment are transforming the landscape of the Amazonian várzea, subject to decision-making at the farming household level. Land use change results from complex human-environment interactions which can be addressed by an agent-based model. An agent-based model is a simulation model composed of autonomous interacting entities known as agents, built from the bottom-up. Coupled with cellular automata, which forms the agents’ environment, agent-based models are becoming an important tool of land use science, complementing traditional methods of induction and deduction. The decision-making methods employed by agent-based models in recent years have included optimization, imitation, heuristics, classifier systems and genetic algorithms, among others, but multiple methods have rarely been comparatively analyzed. A modular agent-based model is designed to allow the researcher to substitute alternative decision-making methods. For a smallholder farming community in Marajó Island near Ponta de Pedras, Pará, Brazil, 21 households are simulated over a 40-year period. In three major scenarios of increasing complexity, these households first face an environment where goods sell at a constant price throughout the simulated period and there are no outside employment opportunities. This is followed by a scenario of variable prices based on empirical data. The third scenario combines variable prices with limited employment opportunities, creating multi-sited households as members emigrate. In each scenario, populations of optimizing agents and heuristic agents are analyzed in parallel. While optimizing agents allocate land cells to maximize revenue using linear programming, fast and frugal heuristic agents use decision trees to quickly pare down feasible solutions and probabilistically select between alternatives weighted by expected revenue. Using distributed computing, the model is run through several parameter sweeps and results are recorded to a cenral database. Land use trajectories and sensitivity analyses highlight the relative biases of each decision-making method and illustrate cases where alternative methods lead to significantly divergent outcomes. A hybrid approach is recommended, employing alternative decision-making methods in parallel to illustrate inefficiencies exogenous and endogenous to the decision-maker, or allowing agents to select among multiple methods to mitigate bias and best represent their real-world analogues.

Economic changes such as rising açaí prices and the availability of off-farm employment are transforming the landscape of the Amazonian várzea, subject to decision-making at the farming household level. Land use change results from complex human-environment interactions which can be addressed by an agent-based model. An agent-based model is a simulation model composed of autonomous interacting entities known as agents, built from the bottom-up. Coupled with cellular automata, which forms the agents’ environment, agent-based models are becoming an important tool of land use science, complementing traditional methods of induction and deduction. The decision-making methods employed by agent-based models in recent years have included optimization, imitation, heuristics, classifier systems and genetic algorithms, among others, but multiple methods have rarely been comparatively analyzed. A modular agent-based model is designed to allow the researcher to substitute alternative decision-making methods. For a smallholder farming community in Marajó Island near Ponta de Pedras, Pará, Brazil, 21 households are simulated over a 40-year period. In three major scenarios of increasing complexity, these households first face an environment where goods sell at a constant price throughout the simulated period and there are no outside employment opportunities. This is followed by a scenario of variable prices based on empirical data. The third scenario combines variable prices with limited employment opportunities, creating multi-sited households as members emigrate. In each scenario, populations of optimizing agents and heuristic agents are analyzed in parallel. While optimizing agents allocate land cells to maximize revenue using linear programming, fast and frugal heuristic agents use decision trees to quickly pare down feasible solutions and probabilistically select between alternatives weighted by expected revenue. Using distributed computing, the model is run through several parameter sweeps and results are recorded to a cenral database. Land use trajectories and sensitivity analyses highlight the relative biases of each decision-making method and illustrate cases where alternative methods lead to significantly divergent outcomes. A hybrid approach is recommended, employing alternative decision-making methods in parallel to illustrate inefficiencies exogenous and endogenous to the decision-maker, or allowing agents to select among multiple methods to mitigate bias and best represent their real-world analogues.

This research address the problem of inferring, through Radio-Frequency Identification (RFID) tracking data, the graph structures underlying social interactions in a group of rhesus macaques (a species of monkey). These social interactions are considered as independent affiliative and dominative components and are characterized by a variety of visual and auditory displays and gestures. Social structure in a group is an important indicator of its members’ relative level of access to resources and has interesting implications for an individual’s health. Automatic inference of the social structure in an animal group enables a number of important capabilities, including: 1. A verifiable measure of how the social structure is affected by an intervention such as a change in the environment, or the introduction of another animal, and 2. A potentially significant reduction in person hours normally used for assessing these changes. The behaviors of interest in the context of this research are those definable using the macaques’ spatial (x,y,z) position and motion inside an enclosure. Periods of time spent in close proximity with other group members are considered to be events of passive interaction and are used in the calculation of an Affiliation Matrix. This represents the strength of undirected interaction or tie-strength between individual animals. Dominance is a directed relation that is quantified using a heuristic for the detection of withdrawal and displacement behaviors. The results of an analysis based on these approaches for a group of 6 male monkeys that were tracked over a period of 60 days at the Yerkes Primate Research Center are presented in this Thesis.

無論在電腦動畫、電玩或電影產業，利用電腦自動產生虛擬人群已逐漸成為不可或缺的要素之一。這些虛擬人群，往往是系統先賦與每個虛擬代理人(agent)基礎智能，然後藉由個體之間的互動法則所自動產生。然而，過去因為普遍未考量真實群體情境中的傳播與互動模式，使得虛擬人群所表現的群體行為與現實情況仍有些差距。因此，我們引用社會心理學文獻，建立一個具有溝通機制的人群模擬平台(IMCrowd)，以期自動產生與現實群眾動態更相似的模擬人群。IMCrowd是多代理人(Multi-agent)基礎的系統，其中每個虛擬代理人都具有區域的感知範圍與自主能力，因此他們能夠自動地與環境中的其它物件互動與反應。由於我們為IMCrowd所建立的溝通模型考量了社會心理學的理論，因此虛擬人群能浮現真實群體動態中的社會互動模式，如情緒傳染與從眾效應。本研究以IMCrowd執行了多種情境下群眾暴動與群眾控制的模擬，藉此展現本系統的應用將不僅可提升群體模擬的真實度，亦可做為社會心理學家研究群體行為的工具。 / Using computer to automatically generate simulated crowd has become a trend in animation, computer game, and film productions. Many of these works were produced by modeling the intelligence of the agents in a crowd and their interactions with other nearby agents and the environment. However, the perceived facts or elicited emotions usually do not propagate in the crowd as they should in the real life. In this work we attempt to build up a communication model to simulate a large variety of crowd behaviors including the course of crowd formation. The proposed crowd simulation system, IMCrowd, has been implemented with a multi-agent system in which each agent has a local perception and autonomous abilities to improvise their actions. The algorithms used in our communication model in IMCrowd are based heavily on sociology research. Therefore, the collective behaviors will emerge out of the social process such as emotion contagion and conformity effect among individual agents. Several elaborate riot simulations and riot control simulations are demonstrated and reported in this thesis as the application examples of IMCrowd. Thus, we claim that IMCrowd may not only benefit on enhancing realism of crowd animation but also be useful in studying crowd behaviors such as panic, gathering, and riots.

Techn. Univ., Diss--Berlin, 2007.