关键词: 智能交通, 动态权重联合优化, 双深度Q网络, 交通信号, CAV轨迹

Abstract: There is an optimization objective conflict issue in the joint optimization of traffic signals and trajectories of connected and automated vehicles (CAV) based on deep reinforcement learning. To address this conflict, a dual-agent system consisting of traffic signals and CAVs is designed, and a dynamic weight joint optimization framework (Dynamic-Weighted Optimization into Positioning with Double Deep Q-Network, DOP-DDQN) is proposed. By constructing a joint reward function that integrates traffic signals and CAVs, the balance between the efficiency of traffic signal and CAV on safety, energy consumption, and efficiency is achieved. The weights of traffic signals and CAVs in the joint reward are dynamically adjusted by using fuzzy logic, which convert the objective conflict into a dynamic priority allocation problem, and thus enable the adaptive adjustment of the optimization focus based on traffic conditions. Simulation results show that compared to MaxPressure, DOP-DDQN reduces average queue length, travel time, and fuel consumption by 15.97%~23.74%, 8.69%~9.89%, and 4.19%~9.53%, respectively. Compared to other similar methods, these three indicators decrease by 4.06%~15.19%, 2.30%~6.62%, and 1.42%~5.11%, respectively. Further research indicates that the control effectiveness of DOP-DDQN significantly increases with the penetration rate of CAVs, but the improvement tends to level off when the penetration rate exceeds 0.6.

Key words: intelligent transportation, dynamic weight joint optimization, dual deep Q-network, traffic signal, CAV trajectory