用强化学习让汽车自动调节速度，缓解无信号灯道路拥堵。
Self-Regulating Cars: Automating Traffic Control in Free Flow Road Networks
- 基于强化学习动态调节车速，不依赖新硬件。
- 实测通行量提升5%，平均延迟降低13%。
- 适合城市高速、自动驾驶等场景落地应用。
郊区高速公路等自由流道路因通勤量增加和基础设施有限，正面临日益严重的交通拥堵。传统信号灯或局部规则在高速、无信号的环境中无效。本文提出自调节车辆机制，一种基于强化学习的交通控制协议，通过动态调节车速来优化通行效率并预防拥堵，无需新增物理设施。该方法融合经典交通流理论、间隙接受模型与微观仿真，将道路抽象为超路段，使智能体从实时交通观测中学习鲁棒的速度调控策略。在高保真PTV Vissim仿真器中对真实高速公路网络进行评估，结果表明，相比无控制情形，本方法使总通行量提升5%，平均延迟减少13%，总停车次数下降3%。同时实现更平稳、抗拥堵的行车流，并在不同交通模式下具备良好泛化能力，展现出可扩展的机器学习驱动交通管理潜力。
原文摘要 · Abstract (English)
Free-flow road networks, such as suburban highways, are increasingly experiencing traffic congestion due to growing commuter inflow and limited infrastructure. Traditional control mechanisms, such as traffic signals or local heuristics, are ineffective or infeasible in these high-speed, signal-free environments. We introduce self-regulating cars, a reinforcement learning-based traffic control protocol that dynamically modulates vehicle speeds to optimize throughput and prevent congestion, without requiring new physical infrastructure. Our approach integrates classical traffic flow theory, gap acceptance models, and microscopic simulation into a physics-informed RL framework. By abstracting roads into super-segments, the agent captures emergent flow dynamics and learns robust speed modulation policies from instantaneous traffic observations. Evaluated in the high-fidelity PTV Vissim simulator on a real-world highway network, our method improves total throughput by 5%, reduces average delay by 13%, and decreases total stops by 3% compared to the no-control setting. It also achieves smoother, congestion-resistant flow while generalizing across varied traffic patterns, demonstrating its potential for scalable, ML-driven traffic management.
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