Emergency vehicles (EMVs) play a crucial role in responding to time-critical events such as medical emergencies and fire outbreaks in an urban area. The less time EMVs spend traveling through the traffic, the more likely it would help save people's lives and reduce property loss. To reduce the travel time of EMVs, prior work has used route optimization based on historical traffic-flow data and traffic signal pre-emption based on the optimal route. However, traffic signal pre-emption dynamically changes the traffic flow which, in turn, modifies the optimal route of an EMV. In addition, traffic signal pre-emption practices usually lead to significant disturbances in traffic flow and subsequently increase the travel time for non-EMVs. In this paper, we propose EMVLight, a decentralized reinforcement learning (RL) framework for simultaneous dynamic routing and traffic signal control. EMVLight extends Dijkstra's algorithm to efficiently update the optimal route for the EMVs in real time as it travels through the traffic network. The decentralized RL agents learn network-level cooperative traffic signal phase strategies that not only reduce EMV travel time but also reduce the average travel time of non-EMVs in the network. This benefit has been demonstrated through comprehensive experiments with synthetic and real-world maps. These experiments show that EMVLight outperforms benchmark transportation engineering techniques and existing RL-based signal control methods.
翻译:紧急车辆(EMVs)在应对医疗紧急情况和城市火灾爆发等时间紧迫事件方面发挥着关键作用。快速机动车辆在交通中花费的时间越少,就越有可能帮助拯救人们的生命和减少财产损失。为缩短快速机动车辆的旅行时间,先前的工作根据历史交通流数据和交通信号在最佳路线上预先消除交通信号,采用了路线优化办法。然而,交通信号先发制人地动态改变交通流量,转而改变快速机动车辆的最佳路线。此外,交通信号先发制人做法通常导致交通流量严重中断,并随后增加非紧急车辆的旅行时间。在本文件中,我们提议采用EMVLight,即分散的强化学习框架,以同时进行动态路线运行和交通信号控制。EMVLight将D的算法扩展为Dijkstra在通过交通网络实时运行时有效更新快速机动车辆的最佳路线。分散的RL代理商学习网络级合作交通信号阶段战略,不仅减少快速机动车辆流动流动流动,而且随后增加非紧急机动车辆的旅行时间,还展示了当前导航系统的平均时间实验方法。