A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles

TL;DR

This paper presents EMVLight, a decentralized reinforcement learning framework that optimizes emergency vehicle routing and traffic signals simultaneously, significantly reducing travel times for emergency and regular vehicles in urban traffic networks.

Contribution

The paper introduces EMVLight, a novel decentralized RL approach that integrates dynamic routing with traffic signal control, addressing the coupling issue and improving overall traffic efficiency.

Findings

EMVLight reduces emergency vehicle travel time by up to 30%.

The framework outperforms existing traffic signal control methods in simulations.

Network-level cooperation improves overall traffic flow and emergency response times.

Abstract

Emergency vehicles (EMVs) play a critical role in a city's response to time-critical events such as medical emergencies and fire outbreaks. The existing approaches to reduce EMV travel time employ route optimization and traffic signal pre-emption without accounting for the coupling between route these two subproblems. As a result, the planned route often becomes suboptimal. In addition, these approaches also do not focus on minimizing disruption to the overall traffic flow. To address these issues, we introduce EMVLight in this paper. This is 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 an EMV in real-time as it travels through the traffic network. Consequently, the decentralized RL agents learn network-level cooperative traffic signal phase strategies that reduce EMV travel time and the average travel time of non-EMVs in the network. We have carried out comprehensive experiments with synthetic and real-world maps to demonstrate this benefit. Our results show that EMVLight outperforms benchmark transportation engineering techniques as well as existing RL-based traffic signal control methods.