Soft Actor-Critic based regional traffic signal control in connected environment and its application in priority signal control

With advancements in computational power and Vehicle-to-Infrastructure (V2I) technologies, modern traffic signal control (TSC) systems are designed in a more intelligent way by leveraging Connected Vehicle (CV) data. While prior research primarily focuses on CV positions and speeds, route data-which can provide foresight into future traffic demand-remains largely overlooked. To fill this gap, we propose a distributed TSC model based on the Soft Actor-Critic (SAC) reinforcement learning algorithm. Through V2I communication, the model incorporates vehicle positions, route information, downstream capacity, and neighboring traffic pressure into its state representation. This enables the agent to anticipate traffic demand and mitigate issues caused by partial observability. Furthermore, by acquiring the real-time data of priority vehicles and assigning weighted priority, the proposed model can adapt to real-time priority control. Simulation results show that the proposed model significantly outperforms baseline approaches, reducing average queue length and vehicle waiting time by approximately 19.2% and 57.9%, respectively. The model also demonstrates strong generalizability under traffic disturbances. In priority scenarios, it cuts the waiting time for high-priority vehicles by around 75%, with minimal impact on overall traffic efficiency. These findings demonstrate the model's effectiveness, adaptability, and potential for deployment in intelligent traffic management systems.