A Day-to-Day Route Choice Model Based on Reinforcement Learning

Author:

Wei Fangfang1ORCID,Ma Shoufeng1,Jia Ning1ORCID

Affiliation:

1. College of Management and Economic, Tianjin University, Tianjin 300072, China

Abstract

Day-to-day traffic dynamics are generated by individual traveler’s route choice and route adjustment behaviors, which are appropriate to be researched by using agent-based model and learning theory. In this paper, we propose a day-to-day route choice model based on reinforcement learning and multiagent simulation. Travelers’ memory, learning rate, and experience cognition are taken into account. Then the model is verified and analyzed. Results show that the network flow can converge to user equilibrium (UE) if travelers can remember all the travel time they have experienced, but which is not necessarily the case under limited memory; learning rate can strengthen the flow fluctuation, but memory leads to the contrary side; moreover, high learning rate results in the cyclical oscillation during the process of flow evolution. Finally, both the scenarios of link capacity degradation and random link capacity are used to illustrate the model’s applications. Analyses and applications of our model demonstrate the model is reasonable and useful for studying the day-to-day traffic dynamics.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

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