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

Fangfang Wei, Shoufeng Ma and Ning Jia

Mathematical Problems in Engineering, 2014, vol. 2014, 1-19

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.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:646548

DOI: 10.1155/2014/646548

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