用强化学习统一调控信号灯与自动驾驶车，提升大城市交通效率
Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning
- 采用去中心化多智能体强化学习协调信号灯与自动驾驶车辆
- 在80%自动驾驶渗透率下，等待时间减少1.08秒，通行量提升39辆/500秒
- 首次在真实14个路口的大规模混合交通中验证，适合城市智能交通研究者
交通拥堵仍是现代城市网络的重大挑战。自动驾驶技术被视为潜在解决方案。强化学习在各类场景中已表现出优于传统信号灯的控制性能。然而，以往研究多集中于小规模网络或孤立路口，对大规模混合交通控制关注不足。本研究首次采用去中心化多智能体强化学习，实现部分路口由信号灯管理、部分由机器人车辆（RV）控制的大规模混合交通协同。在科罗拉多州斯普林斯市的真实路网（14个路口）上评估，以车辆平均等待时间和单位时间内到达目的地的车辆数（即吞吐量）为指标。在80%的自动驾驶车辆渗透率下，等待时间从6.17秒降至5.09秒，吞吐量从每500秒454辆增至493辆，显著优于全信号灯基准方案。结果表明，基于强化学习的大规模交通协同控制可有效提升整体效率，有助于未来城市交通规划。
原文摘要 · Abstract (English)
Traffic congestion remains a significant challenge in modern urban networks. Autonomous driving technologies have emerged as a potential solution. Among traffic control methods, reinforcement learning has shown superior performance over traffic signals in various scenarios. However, prior research has largely focused on small-scale networks or isolated intersections, leaving large-scale mixed traffic control largely unexplored. This study presents the first attempt to use decentralized multi-agent reinforcement learning for large-scale mixed traffic control in which some intersections are managed by traffic signals and others by robot vehicles. Evaluating a real-world network in Colorado Springs, CO, USA with 14 intersections, we measure traffic efficiency via average waiting time of vehicles at intersections and the number of vehicles reaching their destinations within a time window (i.e., throughput). At 80% RV penetration rate, our method reduces waiting time from 6.17s to 5.09s and increases throughput from 454 vehicles per 500 seconds to 493 vehicles per 500 seconds, outperforming the baseline of fully signalized intersections. These findings suggest that integrating reinforcement learning-based control large-scale traffic can improve overall efficiency and may inform future urban planning strategies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。