用AI动态调控红绿灯，比传统定时系统更高效。
Smart Traffic Signals: Comparing MARL and Fixed-Time Strategies
- 每个路口独立决策，通过学习优化信号配时。
- 平均等待时间下降，车辆通行效率提升显著。
- 适合城市交通优化、智能交通系统研究者。
城市交通拥堵，尤其在交叉口，严重影响出行时间、燃油消耗和排放。传统定时信号控制系统难以适应动态交通变化。本研究探索多智能体强化学习（MARL）在模拟交通网络中的应用，优化多个交叉口的信号协调。通过构建包含随机车流的互联交叉口仿真环境，实现去中心化MARL控制器，每个信号灯作为独立智能体，基于局部观测与邻近信息决策。与基准定时控制器对比，评估指标包括平均车辆等待时间和整体通行量。结果表明，MARL方法在统计上显著改善了平均等待时间和通行效率，证明其在提升城市交通管理效率方面具有巨大潜力。建议进一步研究可扩展性与真实世界部署挑战。
原文摘要 · Abstract (English)
Urban traffic congestion, particularly at intersections, significantly affects travel time, fuel consumption, and emissions. Traditional fixed-time signal control systems often lack the adaptability to effectively manage dynamic traffic patterns. This study explores the application of multi-agent reinforcement learning (MARL) to optimize traffic signal coordination across multiple intersections within a simulated environment. A simulation was developed to model a network of interconnected intersections with randomly generated vehicle flows to reflect realistic traffic variability. A decentralized MARL controller was implemented in which each traffic signal operates as an autonomous agent, making decisions based on local observations and information from neighboring agents. Performance was evaluated against a baseline fixed-time controller using metrics such as average vehicle wait time and overall throughput. The MARL approach demonstrated statistically significant improvements, including reduced average waiting times and improved throughput. These findings suggest that MARL-based dynamic control strategies hold substantial promise to improve urban traffic management efficiency. More research is recommended to address the challenges of scalability and real-world implementation.
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