Zhang Gongquan, Li Fengze, Ren Dian, Huang Helai, Zhou Zilong, Chang Fangrong
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China; Harvard Medical School, Harvard University, Boston 02138, United States.
School of Information Engineering, Chang'an University, Xi'an 710064, China.
Accid Anal Prev. 2025 Mar;211:107890. doi: 10.1016/j.aap.2024.107890. Epub 2024 Dec 19.
Cooperative control of intersection signals and connected automated vehicles (CAVs) possess the potential for safety enhancement and congestion alleviation, facilitating the integration of CAVs into urban intelligent transportation systems. This research proposes an innovative deep reinforcement learning-based (DRL) cooperative control framework, including signal and speed modules, to dynamically adapt signal timing and CAV velocities for traffic safety and efficiency optimization. Among the DRL-based signal modules, a traffic state prediction model is merged with the current state to augment characteristics and the agent-learning process. A multi-objective reward function is designed to evaluate safety and efficiency using a traffic conflict prediction model and vehicle waiting time. The double deep Q network (DDQN) model is used to design the agent observing the traffic state, learning the optimal signal control policy, and then inputting the signal phase into the speed module. Based on the green duration analysis and constraints of mixed traffic flow of CAVs and human-driven vehicles, a speed planning model is constructed to optimize CAVs' speed and alter traffic state, which in turn affects the agent's next signal decisions. The proposed framework is tested at isolated intersections simulated by two real-world intersections in Changsha, China. The results reveal the superiority of the proposed method over DRL-based traffic signal control (DRL-TSC) in terms of coverage speed and computation time. Compared to actuated signal control, adaptive traffic signal control, and DRL-TSC, the proposed method significantly optimizes traffic safety and efficiency across diverse intersections, temporal spans, and traffic demands. Furthermore, the advantage of the proposed method substantially amplifies with the increased CAV penetration, regardless of the intersection types.
交叉路口信号与联网自动驾驶车辆（CAV）的协同控制具有提升安全性和缓解拥堵的潜力，有助于将CAV集成到城市智能交通系统中。本研究提出了一种基于深度强化学习（DRL）的创新协同控制框架，包括信号和速度模块，以动态调整信号配时和CAV速度，实现交通安全和效率的优化。在基于DRL的信号模块中，将交通状态预测模型与当前状态合并，以增强特征和智能体学习过程。设计了一种多目标奖励函数，使用交通冲突预测模型和车辆等待时间来评估安全性和效率。采用双深度Q网络（DDQN）模型设计智能体，用于观察交通状态、学习最优信号控制策略，然后将信号相位输入速度模块。基于绿色持续时间分析以及CAV和人工驾驶车辆混合交通流的约束，构建了速度规划模型，以优化CAV速度并改变交通状态，进而影响智能体的下一个信号决策。所提出的框架在中国长沙两个真实世界交叉路口模拟的孤立交叉路口进行了测试。结果表明，在覆盖速度和计算时间方面，所提出的方法优于基于DRL的交通信号控制（DRL-TSC）。与感应式信号控制、自适应交通信号控制和DRL-TSC相比，所提出的方法在不同交叉路口、时间跨度和交通需求下显著优化了交通安全和效率。此外，无论交叉路口类型如何，随着CAV渗透率的增加，所提出方法的优势会大幅放大。
