An Optimization Method for Traffic Signals Oriented Towards Multi-Priority Vehicles

The traditional traffic signal control system usually relies on fixed or dynamic preset time period regulation schemes, which has a limited response to the real-time changes in traffic flow and the demand of different vehicle priorities. Existing control strategies are difficult to ensure the fast passage of high-priority vehicles while providing sufficient passage guarantees for normal vehicles. In this paper, we propose a deep reinforcement learning traffic signal control method for multi-priority vehicles. Firstly, we improve the DQN by using the dueling structure. Secondly, we assign different weights to different priority vehicles and redesign the agent's state information and reward function. In simulation experiments, we compare four similar traffic signal control methods, and the experimental results prove the effectiveness of our proposed method.