Shi Jingjing. / "August 2006." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 192-205). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese.

M.Sc. (Information Technology) / The purpose of the research is to investigate agent-based approaches to virtual crowd simulation. Crowds are ubiquitous and are becoming an increasingly common phenomena in modern society, particularly in urban settings. As such, crowd simulation systems are becoming increasingly popular in training simulations, pedestrian modelling, emergency simulations, and multimedia. One of the primary challenges in crowd simulation is the ability to model realistic, large-scale crowd behaviours in real time. This is a challenging problem, as the size, visual fidelity, and complex behaviour models of the crowd all have an impact on the available computational resources. In the last few years, the graphics processing unit (GPU) has presented itself as a viable computational resource for general purpose computation. Traditionally, GPUs were used solely for their ability to efficiently compute operations related to graphics applications. However, the modern GPU is a highly parallel programmable processor, with substantially higher peak arithmetic and memory bandwidth than its central processing unit (CPU) counterpart. The GPU’s architecture makes it a suitable processing resource for computations that are parallel or distributed in nature. One attribute of multi-agent systems (MASs) is that they are inherently decentralised. As such, a MAS that leverages advancements in GPU computing may provide a solution for crowd simulation. The research investigates techniques and methods for general purpose crowd simulation, including topics in agent behavioural modes, pathplanning, collision avoidance and agent steering. The research also investigates how GPU computing has been utilised to address these computationally intensive problem domains. Based on the outcomes of the research, an agent-based model, Massively Parallel Crowds (MPCrowds), is proposed to address virtual crowd simulation, using the GPU as an additional resource for agent computation.

Orientadores: Siome Klein Goldenstein, Jacques Wainer / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-22T23:53:33Z (GMT). No. of bitstreams: 1 Coelho_RenatoSchattanPereira_M.pdf: 864445 bytes, checksum: 8f57902047a23925af4b81fa0d7f3188 (MD5) Previous issue date: 2013 / Resumo: O trânsito é um problema cada vez maior nas cidades, consumindo recursos e agravando a poluição; em São Paulo perdem-se cerca de R$33 bilhões por ano por causa do trânsito. Neste trabalho de mestrado desenvolvemos um sistema que une as áreas de simulação de multidões e planejamento probabilístico para otimizar semáforos de tempo fixo. Essas duas áreas apresentam algoritmos que permitem soluções eficientes para os problemas, mas a sua aplicação ainda depende largamente da intervenção de especialistas no problema a ser estudado, seja descrevendo o problema de planejamento probabilístico, seja interpretando os dados devolvidos pelo simulador. Nosso sistema diminui essa dependência ao utilizar autômatos celulares para simular o tráfego e gerar informações que são então utilizadas para descrever o problema de planejamento probabilístico. Com isso podemos: (i) reduzir a necessidade de coleta de dados, que passam a ser gerados pelo simulador e (ii) produzir bons planos para o controle de semáforos de tempo fixo sem que seja necessária a intervenção de especialistas para a análise dos dados. Nos dois testes realizados a solução proposta pelo sistema diminuiu o tempo médio de percurso em 18:51% e 13:51%, respectivamente / Abstract: Traffic is an ever increasing problem, draining resources and aggravating pollution. In Sao Paulo, for instance, financial losses caused by traffic represent a sum of about R$33 billions a year. In this work we've developed a system that puts together the areas of Crowd Simulation and Probabilistic Planning to optimize fixed time traffic lights. Although both areas present good algorithms their use is limited by their reliance on specialists, whether to describe the probabilistic planning problem or to analyze the data produced by the simulations. Our approach contributes to minimize this dependence by using cellular automata simulations to generate the data that is used to describe the probabilistic planning problem. This allows us to: (i) reduce the amount of data collection, since the data is now generated by the simulator and (ii) produce good policies for fixed time traffic light control without the intervention of specialists to analyze the data. In the two tests performed the solution proposed by the system was able to reduce travel times by 18:51% and 13:51%, respectively / Mestrado / Ciência da Computação / Mestre em Ciência da Computação