Collisions, crashes, and other incidents on road networks, if left unmitigated, can potentially cause cascading failures that can affect large parts of the system. Timely handling such extreme congestion scenarios is imperative to reduce emissions, enhance productivity, and improve the quality of urban living. In this work, we propose a Deep Reinforcement Learning (DRL) approach to reduce traffic congestion on multi-lane freeways during extreme congestion. The agent is trained to learn adaptive detouring strategies for congested freeway traffic such that the freeway lanes along with the local arterial network in proximity are utilized optimally, with rewards being congestion reduction and traffic speed improvement. The experimental setup is a 2.6-mile-long 4-lane freeway stretch in Shoreline, Washington, USA with two exits and associated arterial roads simulated on a microscopic and continuous multi-modal traffic simulator SUMO (Simulation of Urban MObility) while using parameterized traffic profiles generated using real-world traffic data. Our analysis indicates that DRL-based controllers can improve average traffic speed by 21\% when compared to no-action during steep congestion. The study further discusses the trade-offs involved in the choice of reward functions, the impact of human compliance on agent performance, and the feasibility of knowledge transfer from one agent to other to address data sparsity and scaling issues.

翻译：交通事故、撞车及其他道路事件若未得到及时处置，可能引发级联故障，影响系统大范围运行。及时处理此类极端拥堵场景对于减少排放、提升生产力、改善城市生活质量至关重要。本研究提出一种基于深度强化学习的交通拥堵缓解方法，针对多车道高速公路在极端拥堵条件下的通行问题。智能体通过训练学习自适应绕行策略，使高速公路车道与邻近主干路网络得到最优利用，奖励函数设定为降低拥堵程度与提升交通速度。实验场景基于美国华盛顿州岸线市一段长2.6英里的四车道高速公路，设有两个出口及关联主干路，采用微观连续多模式交通仿真平台SUMO进行模拟，并使用基于真实交通数据生成的参数化交通流剖面。分析表明，在严重拥堵情况下，深度强化学习控制器相较无干预措施可使平均交通速度提升21%。研究进一步探讨了奖励函数选择的权衡机制、人类依从性对智能体性能的影响，以及通过知识迁移解决数据稀疏性与规模扩展问题的可行性。