Collaborative Research: (DHB) - Modeling and Analyzing Individual and Collective Human Spatial Behavior

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A great deal of important human activity requires navigating and otherwise using physical spaces. Human spatial behavior, individual and collective, depends on the structure of the environment, the structure and content of cognitive representations of the environment (mental maps), individual and group goals, and interactions among individuals, groups, and crowds. As a result, human spatial behavior needs to be studied by considering all these aspects together; to do this requires a multidisciplinary effort. The proposed research brings together a team of researchers representing diverse disciplines of computation, psychology, and mathematics into an integrated program that will address human individual and collective use of physical space. This project's multidisciplinary effort uses the tools of cognitive science, computational modeling, and mathematical analysis of collective behavior to study the dynamics of human spatial behavior. The project's goals are as follows: 1) To understand how individuals develop cognitive maps of complex environments in real-world situations that include rich spatial interactions with other people, individually and in groups; 2) To examine how cognitive maps influence the behavior of groups in high-density crowd conditions, and to develop the cognitive foundations for models of pedestrian dynamics and emergency evacuations; 3) To forge a mathematical link between microscopic (small scale) and macroscopic (large scale) models of adaptive human social behavior in spatial tasks. The research program will develop novel cognitive map-driven models that will include the shared spatial information that individual people derive from social interactions. The models will be used to improve simulations of individuals and crowds, and provide a basis for mathematical analysis of collective spatial behavior dynamics. Validation and evaluation will exploit virtual reality technology, computer simulations of crowd behavior, and people's behavior in real environments. Through partnerships with two museums, the team will be working with real-world situations whose relevance to societal needs includes the design of emergency evacuation systems, automated detection of anomalies in large spaces and crowds, and the development of complex but human-friendly environments. The generation, validation, and free dissemination of powerful predictive models that will be developed will allow architects, planners, and developers of public spaces to design and/or modify public venues so as to optimize their navigability and safety. Finally, a comprehensive program of education, outreach, and dissemination will serve a broad audience of graduate and undergraduate students, inner-city K-12 students and teachers, and the general public will accompany the research.