Learning to Help Emergency Vehicles Arrive Faster: A Cooperative Vehicle-Road Scheduling Approach

TL;DR

This paper introduces LEVID, a cooperative vehicle-road scheduling system that uses real-time data and reinforcement learning to optimize emergency vehicle routing and traffic signals, significantly reducing response times.

Contribution

The paper presents a novel learning-based cooperative scheduling approach integrating real-time route planning and traffic signal control for emergency vehicles.

Findings

LEVID outperforms existing methods in real-world traffic scenarios.

The approach effectively reduces emergency vehicle travel time.

The system demonstrates adaptability to dynamic traffic conditions.

Abstract

The ever-increasing heavy traffic congestion potentially impedes the accessibility of emergency vehicles (EVs), resulting in detrimental impacts on critical services and even safety of people's lives. Hence, it is significant to propose an efficient scheduling approach to help EVs arrive faster. Existing vehicle-centric scheduling approaches aim to recommend the optimal paths for EVs based on the current traffic status while the road-centric scheduling approaches aim to improve the traffic condition and assign a higher priority for EVs to pass an intersection. With the intuition that real-time vehicle-road information interaction and strategy coordination can bring more benefits, we propose LEVID, a LEarning-based cooperative VehIcle-roaD scheduling approach including a real-time route planning module and a collaborative traffic signal control module, which interact with each other and make decisions iteratively. The real-time route planning module adapts the artificial potential field method to address the real-time changes of traffic signals and avoid falling into a local optimum. The collaborative traffic signal control module leverages a graph attention reinforcement learning framework to extract the latent features of different intersections and abstract their interplay to learn cooperative policies. Extensive experiments based on multiple real-world datasets show that our approach outperforms the state-of-the-art baselines.