Optimal travel information provision strategies: an agent-based approach under uncertainty

Abstract

Information influences travel behavior a great deal. This paper applies and further develops an agent-based approach to modeling travel behavior under uncertainty and different information provision strategies. Artificially intelligent agents, with the capability of learning, information acquisition, searching, and decision, are constructed instead of the typical representative agents formulated by utility maximization theory. Meanwhile, the presumption of sequential behavior process is relaxed. Agents are flexible to adjust travel mode, departure time, and route in response to a stimulus. A traffic simulator is also integrated in order to simulate agents' travel experiences on a transportation network. The agent-based model is then integrated into a simulation-based optimization (SBO) framework to analyze the effects of different information provision strategies. It is found that providing real-time traffic information to agents does not always result in improved network traffic condition or higher network reliability. Thus, we employ SBO technique to identify the optimal information provision strategies to support policy/planning decision-making.

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