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.

KEYWORDS:

• Agent-based model
• travel behavior
• information provision
• simulation-based optimization

Acknowledgements

The opinions in this paper do not necessarily reflect the official views of our sponsors. They assume no liability for the content or use of this paper. The authors are responsible for all statements in this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Chenfeng Xiong http://orcid.org/0000-0003-4237-1750

Additional information

Funding

This research is financially supported by a National Science Foundation CAREER Award, ‘Reliability as an Emergent Property of Transportation Networks’, and the U.S. Federal Highway Administration’s Strategic Highway Research Program 2 (SHRP2) C10: ‘Proof of Concept – Partnership to Develop an Integrated, Advanced Travel Demand Model and a Fine-grained, Time-sensitive Network’. The research is partially supported by Zhejiang Provincial Natural Science Foundation of China [grant number LR17E080002], National Natural Science Foundation of China [grant number 51508505], [grant number 51338008], and Fundamental Research Funds for the Central Universities [grant number 2017QNA4025].