Efficient Merging at Work Zones to Support Emergency Response in Mixed Traffic Environments: A Reward Weight Adjustment Method for Multi‐Agent Reinforcement Learning

Merging behaviours in work zones pose significant traffic management challenges due to lane closures that create bottlenecks and increase accident risk. In mixed traffic environments involving emergency vehicles (EVs), connected automated vehicles (CAVs), and human‐driven vehicles, addressing these challenges becomes more complex. EVs, in particular, require priority movement to minimize response times, but their movement is often hindered by traditional traffic management strategies. While previous merging strategies have shown promising results in managing traffic flow, they fail to account for the unique challenges posed by different vehicle types in work zone environments. In this paper, we propose a new reward weight adjustment method for multi‐agent proximal policy optimization based CAVs in work zone for emergency vehicles (MAPPO‐WEV). The proposed method improves EV's response times without compromising travel time of other vehicles. MAPPO‐WEV introduces a dynamic reward weighting mechanism that adjusts the importance weight of speed, headway, and merging behaviour based on the type and number of vehicles. This approach allows EVs to travel more freely while maintaining safety in mixed traffic conditions. The simulation results of MAPPO‐WEV show significant improvements in both travel times and waiting times of EVs by 25% and 33% respectively compared to the Baseline method.