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Emergency vehicles (EMVs) play a crucial role in responding to time-criticalevents such as medical emergencies and fire outbreaks in an urban area. Theless time EMVs spend traveling through the traffic, the more likely it wouldhelp save people's lives and reduce property loss. To reduce the travel time ofEMVs, prior work has used route optimization based on historical traffic-flowdata 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-emptionpractices usually lead to significant disturbances in traffic flow andsubsequently increase the travel time for non-EMVs. In this paper, we proposeEMVLight, a decentralized reinforcement learning (RL) framework forsimultaneous dynamic routing and traffic signal control. EMVLight extendsDijkstra's algorithm to efficiently update the optimal route for the EMVs inreal time as it travels through the traffic network. The decentralized RLagents learn network-level cooperative traffic signal phase strategies that notonly reduce EMV travel time but also reduce the average travel time of non-EMVsin the network. This benefit has been demonstrated through comprehensiveexperiments with synthetic and real-world maps. These experiments show thatEMVLight outperforms benchmark transportation engineering techniques andexisting RL-based signal control methods.

EMVLight: A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles