EVPRT: A MARL-Based Approach for Efficient Passage of Emergency Vehicles in Urban Vehicular Networks

Since emergency vehicles (EVs) are essential for urban emergency response, it is essential to help EVs arrive faster. Existing work has investigated route optimization or traffic signal preemption, but they are insufficient because most studies consider the two areas separately and lack a deep understanding of their relationship. For instance, traffic signal preemption can cause changes in traffic flow, affecting the optimal route for EVs. Moreover, previous work does not distinguish between EV types or consider the negative impact on ordinary vehicles (OVs). To address these issues, we propose a framework that jointly considers priority allocation, routing optimization, and traffic signal preemption (EVPRT) in conjunction with the Vehicle to Everything (V2X) environment. To this end, we design an emergency vehicle priority system (EVPS) to assign priorities to different EV types. Then, we design a dynamic route optimization method to update the optimal routes for EVs. Finally, we design a multi-agent reinforcement learning (MARL) based traffic signal preemption algorithm and use a Graph Attention Network (GAT) to extract potential features of different intersections. Furthermore, to provide communication conditions for EVPRT, we adopt a V2X-based communication technology for information interaction. The simulation findings show that our proposed method significantly decreases EV travel time and enhances the capacity of urban emergency service management.