mlsanityData to 14 Oct 2026

Large-scale Regional Traffic Signal Control Based on Single-Agent Reinforcement Learning

arXiv 2503.09252 ↗First posted 12 Mar 2025cs.LG cs.SY eess.SYcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

In the context of global urbanization and motorization, traffic congestion has become a significant issue, severely affecting the quality of life, environment, and economy. This paper puts forward a single-agent reinforcement learning (RL)-based regional traffic signal control (TSC) model. Different from multi - agent systems, this model can coordinate traffic signals across a large area, with the goals of alleviating regional traffic congestion and minimizing the total travel time. The TSC environment is precisely defined through specific state space, action space, and reward functions. The state space consists of the current congestion state, which is represented by the queue lengths of each link, and the current signal phase scheme of intersections. The action space is designed to select an intersection first and then adjust its phase split. Two reward functions are meticulously crafted. One focuses on alleviating congestion and the other aims to minimize the total travel time while considering the congestion level. The experiments are carried out with the SUMO traffic simulation software. The performance of the TSC model is evaluated by comparing it with a base case where no signal-timing adjustments are made. The results show that the model can effectively control congestion. For example, the queuing length is significantly reduced in the scenarios tested. Moreover, when the reward is set to both alleviate congestion and minimize the total travel time, the average travel time is remarkably decreased, which indicates that the model can effectively improve traffic conditions. This research provides a new approach for large-scale regional traffic signal control and offers valuable insights for future urban traffic management.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 12 references that are in the map. First posted on arXiv 12 Mar 2025.

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5 of 12 references.

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Same direction, newest first

  1. 2025DreamerV3 for Traffic Signal Control: Hyperparameter Tuning and Performance4 Mar

Other references · 4

  1. 2019Dream to Control: Learning Behaviors by Latent Imaginationcited by 1,653
  2. 2018World Modelscited by 1,513
  3. 2023Mastering Diverse Domains through World Modelscited by 1,253
  4. 2020Mastering Atari with Discrete World Modelscited by 1,007

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  1. 2025Robust Single-Agent Reinforcement Learning for Regional Traffic Signal Control Under Demand Fluctuations
  2. 2025Single-agent Reinforcement Learning Model for Regional Adaptive Traffic Signal Control
  3. 2025Reinforcement Learning Based Traffic Signal Design to Minimize Queue Lengths
  4. 2024Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion
  5. 2026A Robust and Efficient Multi-Agent Reinforcement Learning Framework for Traffic Signal Control
  6. 2025Robustness of Reinforcement Learning-Based Traffic Signal Control under Incidents: A Comparative Study
  7. 2025Convergence of Multiagent Learning Systems for Traffic control
  8. 2023A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning