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arXiv · 2506.17029

Scalable and Reliable Multi-agent Reinforcement Learning for Traffic Assignment

Leizhen Wang·Peibo Duan·Cheng Lyu·Zewen Wang·Zhiqiang He·Nan Zheng·Zhenliang Ma
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Abstract

The evolution of metropolitan cities and the increase in travel demands impose stringent requirements on traffic assignment methods. Multi-agent reinforcement learning (MARL) approaches outperform traditional methods in modeling adaptive routing behavior without requiring explicit system dynamics, which is beneficial for real-world deployment. However, MARL frameworks face challenges in scalability and reliability when managing extensive networks with substantial travel demand, which limiting their practical applicability in solving large-scale traffic assignment problems. To address these challenges, this study introduces MARL-OD-DA, a new MARL framework for the traffic assignment problem, which redefines agents as origin-destination (OD) pair routers rather than individual travelers, significantly enhancing scalability. Additionally, a Dirichlet-based action space with action pruning and a reward function based on the local relative gap are designed to enhance solution reliability and improve convergence efficiency. Experiments demonstrate that the proposed MARL framework effectively handles medium-sized networks with extensive and varied city-level OD demand, surpassing existing MARL methods. When implemented in the SiouxFalls network, MARL-OD-DA achieves better assignment solutions in 10 steps, with a relative gap that is 94.99% lower than that of conventional methods.

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Leizhen Wang, Peibo Duan, Cheng Lyu, Zewen Wang, Zhiqiang He, Nan Zheng, Zhenliang Ma. 2025-06-20. Scalable and Reliable Multi-agent Reinforcement Learning for Traffic Assignment. https://doi.org/10.1016/j.commtr.2025.100225

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