A Multi-objective Traffic Control Method for Connected and Automated Vehicle at Signalized Intersection Based on Reinforcement Learning

To address the issue of high energy consumption and low efficiency of connected and autonomous vehicles (CAV) in dynamic traffic environments under traditional control methods, a reinforcement learning-based control approach for CAV is proposed, aiming at reducing energy consumption, improving travel efficiency, and enhancing driving comfort. By considering the interactions between CAV and traffic signal control systems, as well as physical environmental factors, we collect signal phase and timing (SPaT), preceding vehicle speed and position, and other information to establish the state space of the reinforcement learning framework. Furthermore, an energy consumption model is established with the limit of battery energy recovery, and a multi-objective weighted reward function is designed based on key performance indicators such as energy consumption per unit time, travel distance, and acceleration change rate. The optimal weights for each performance indicator are determined using the analytic hierarchy process, and the model is trained using a deep deterministic policy gradient algorithm, with the algorithm parameters optimized through gradient descent. Simulation experiments were carried out using the SUMO platform the results demonstrate that the proposed algorithm achieves the most balanced travel performance, with a 9.22% reduction in energy consumption and an 18.77% reduction in change rate of acceleration compared to the DQN algorithm, as well as an 8.39% reduction in travel time compared to the Krauss car-following model. In conclusion, the results validate the feasibility and effectiveness of the proposed CAV control approach in reducing energy consumption, improving travel efficiency, and enhancing driving comfort.