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Cities are complex, dynamic, evolving adaptive systems comprised of people as well as interconnected physical infrastructure. Simulation modeling can help us understand and shape the evolution of our cities. In this paper, we describe an agent-based simulation modeling framework applied to Chicago, called chiSIM (for the Chicago Social Interaction Model). Each person residing in Chicago is represented as an agent in chiSIM; all places where people can be located in Chicago also are represented. The model simulates the movements of people between locations on an hourly basis during the course of a typical day. Co-located agents engage in various kinds of social interactions, such as exchanging information, engaging in business transactions, or simply sharing physical proximity. We discuss technical approaches to large-scale urban modeling including development of synthetic populations, efficiency gains through distributed processing, logging and analysis of simulation results, and visualization.
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