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

Authored by Lei Zhang, Chenfeng Xiong, Zheng Zhu, Xiqun Chen

Date Published: 2018

DOI: 10.1080/21680566.2017.1336126

Sponsors: Chinese National Natural Science Foundation United States National Science Foundation (NSF)

Platforms: No platforms listed

Model Documentation: Other Narrative Flow charts Mathematical description

Model Code URLs: Model code not found

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.

Tags

Agent-based model behavior Reliability Optimization Network Model Travel behavior time Market penetration Choice User equilibrium Simulation-based optimization Transportation systems Information provision