用多智能体强化学习让救护车更快通过拥堵城市道路
Facilitating Emergency Vehicle Passage in Congested Urban Areas Using Multi-agent Deep Reinforcement Learning
- 设计分布式智能体框架，联动信号灯与路线规划
- 救护车通行时间缩短42.6%，普通车辆延误减少23.5%
- 兼顾效率与公平，适配不同城区交通特征
紧急响应时间（ERT）关乎城市安全，纽约市医疗急救响应时间从2014年的7.89分钟增至2024年的14.27分钟，增幅达72%；其中一半延误源于急救车辆通行时间。每延迟一分钟，脑卒中患者损失约200万脑细胞，心脏骤停存活率下降7-10%。本论文提出三项贡献：首先，提出EMVLight框架，采用去中心化多智能体强化学习，集成急救车路径规划与交通信号优先控制，使急救车通行时间缩短42.6%，其他车辆延误减少23.5%。其次，提出动态借道系统，基于多智能体近端策略优化，在混合自动驾驶与人工驾驶交通中实现协同车道清空，使急救车通行时间再降40%。最后，对纽约市急救服务进行公平性分析，发现史泰登岛因信号灯稀疏导致延误，曼哈顿则因拥堵严重。解决方案包括优化急救站布局与改进路口设计。研究为提升急救通行效率与服务公平性提供决策支持。
原文摘要 · Abstract (English)
Emergency Response Time (ERT) is crucial for urban safety, measuring cities' ability to handle medical, fire, and crime emergencies. In NYC, medical ERT increased 72% from 7.89 minutes in 2014 to 14.27 minutes in 2024, with half of delays due to Emergency Vehicle (EMV) travel times. Each minute's delay in stroke response costs 2 million brain cells, while cardiac arrest survival drops 7-10% per minute. This dissertation advances EMV facilitation through three contributions. First, EMVLight, a decentralized multi-agent reinforcement learning framework, integrates EMV routing with traffic signal pre-emption. It achieved 42.6% faster EMV travel times and 23.5% improvement for other vehicles. Second, the Dynamic Queue-Jump Lane system uses Multi-Agent Proximal Policy Optimization for coordinated lane-clearing in mixed autonomous and human-driven traffic, reducing EMV travel times by 40%. Third, an equity study of NYC Emergency Medical Services revealed disparities across boroughs: Staten Island faces delays due to sparse signalized intersections, while Manhattan struggles with congestion. Solutions include optimized EMS stations and improved intersection designs. These contributions enhance EMV mobility and emergency service equity, offering insights for policymakers and urban planners to develop safer, more efficient transportation systems.
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