  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.
