Reinforcement Learning-based Sequential Route Recommendation for System-Optimal Traffic Assignment

• URL: http://arxiv.org/abs/2505.20889v1
• Date: Tue, 27 May 2025 08:33:02 GMT
• Title: Reinforcement Learning-based Sequential Route Recommendation for System-Optimal Traffic Assignment
• Authors: Leizhen Wang, Peibo Duan, Cheng Lyu, Zhenliang Ma,
• Abstract summary: We propose a learning-based framework that reformulates the static SO traffic assignment problem as a single-agent deep reinforcement learning task.<n>We develop an MSA-guided deep Q-learning algorithm that integrates the iterative structure of traditional traffic assignment methods into the RL training process.<n>Results show that the RL agent converges to the theoretical SO solution in the Braess network and achieves only a 0.35% deviation in the OW network.
• Score: 8.598431584462944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern navigation systems and shared mobility platforms increasingly rely on personalized route recommendations to improve individual travel experience and operational efficiency. However, a key question remains: can such sequential, personalized routing decisions collectively lead to system-optimal (SO) traffic assignment? This paper addresses this question by proposing a learning-based framework that reformulates the static SO traffic assignment problem as a single-agent deep reinforcement learning (RL) task. A central agent sequentially recommends routes to travelers as origin-destination (OD) demands arrive, to minimize total system travel time. To enhance learning efficiency and solution quality, we develop an MSA-guided deep Q-learning algorithm that integrates the iterative structure of traditional traffic assignment methods into the RL training process. The proposed approach is evaluated on both the Braess and Ortuzar-Willumsen (OW) networks. Results show that the RL agent converges to the theoretical SO solution in the Braess network and achieves only a 0.35% deviation in the OW network. Further ablation studies demonstrate that the route action set's design significantly impacts convergence speed and final performance, with SO-informed route sets leading to faster learning and better outcomes. This work provides a theoretically grounded and practically relevant approach to bridging individual routing behavior with system-level efficiency through learning-based sequential assignment.

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