Reinforcement Learning-based Sequential Route Recommendation for System-Optimal Traffic Assignment
On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 12 of its 52 references that are in the map. First posted on arXiv 27 May 2025.
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- 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
- 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
- 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 721
- 2019Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Controlcited by 164
- 2023End-to-End Heterogeneous Graph Neural Networks for Traffic Assignmentcited by 13
- 2024AI-Driven Day-to-Day Route Choicecited by 13
- 2020Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignmentcited by 9
- 2022Model-based graph reinforcement learning for inductive traffic signal controlcited by 5
- 2024A Survey of Route Recommendations: Methods, Applications, and Opportunitiescited by 5
- 2022The Braess Paradox in Dynamic Trafficcited by 2
- 2025Route Recommendations for Traffic Management Under Learned Partial Driver Compliancecited by 2
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Citing papers per year: 2025: 2.
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- 2026AoI-Guaranteed Dynamic Route Planning for Connected Vehicles ↗
- 2025Network-Constrained Policy Optimization for Adaptive Multi-agent Vehicle Routing
- 2025DeepTravel: An End-to-End Agentic Reinforcement Learning Framework for Autonomous Travel Planning Agents
- 2025LLM-Guided Reinforcement Learning with Representative Agents for Traffic Modeling
- 2025Impact of Collective Behaviors of Autonomous Vehicles on Urban Traffic Dynamics: A Multi-Agent Reinforcement Learning Approach
- 2026Heterogeneous Vertiport Selection Optimization for On-Demand Air Taxi Services: A Deep Reinforcement Learning Approach
- 2026Human-Centric Traffic Signal Control for Equity: A Multi-Agent Action Branching Deep Reinforcement Learning Approach