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Travel behavior research is, by definition, concerned with the behavior of travelers. In order to make those research results useful for policy decisions, often the behavior of many travelers needs to be considered in conjunction. This can either be achieved by models which look at groups of travelers, or by models which look at individual travelers. This paper is concerned with the latter, and how such models of individual travel behavior research can be combined into a model-based transportation forecasting tool. The method to achieve this is called multi-agent simulation.

This paper looks at multi-agent simulation for travel behavior research first from a modeling and then from a computational perspective. The modeling part discusses the main issues mobility simulation, strategy generation, learning/feedback, and initial/boundary conditions. The computational part then investigates how those modeling approaches can be implemented on existing computer architectures. Special emphasis is put on the interoperability between modules coming from different authors, and on parallel and distributed computing in order to achieve computability of large scenarios.