Ren A Dian, Zhang B Gongquan, Chang C Fangrong, Huang D Helai
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China.
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China; Harvard Medical School, Harvard University, Boston 02115, the United States of America.
Accid Anal Prev. 2025 Nov;222:108161. doi: 10.1016/j.aap.2025.108161. Epub 2025 Sep 17.
The primary goal of traffic signals control (TSC) is to enhance safety and protect all traffic participants. However, there exists enhancement such as increasing safety for vulnerable road users (VRUs), especially pedestrians. This study proposes a novel two-step traffic signal control framework based on deep reinforcement learning (TSDRL-TSC) to improve pedestrian safety and overall traffic efficiency at intersections. Based on advanced communication technologies of connected vehicles (CV), the TSDRL-TSC acquires the data from real-time traffic conditions and dynamically adjusts traffic signals, aiming to minimize traffic conflicts and delays of pedestrians and vehicles. In the first step, TSDRL-TSC decides whether to use traditional four-signal phases or a modified version considering the protected/prohibited right turn (PPRT) strategy based on pedestrian conditions. In the second step, TSDRL-TSC optimizes the specific control scheme through deep reinforcement learning techniques, selecting the optimal signal phases/actions for the current intersection state to obtain long-term reward returns. The reward function considers the safety and efficiency of all traffic participant, designed to balance the requirement for pedestrian safety, pedestrian efficiency, and vehicle throughput. Simulation experiments at a representative six-lane bidirectional intersection in Changsha City validate the effectiveness of the proposed method. Results demonstrate that (1) TSDRL-TSC significantly reduces pedestrian-vehicle conflicts, jaywalking incidents, and total delays compared to adaptive traffic signal control and PPRT control; (2) TSDRL-TSC presents the potential as a robust solution to enhance pedestrian safety and traffic efficiency for complex urban traffic management.
交通信号控制（TSC）的主要目标是提高安全性并保护所有交通参与者。然而，也存在一些改进措施，例如提高弱势道路使用者（VRU），尤其是行人的安全性。本研究提出了一种基于深度强化学习的新型两步交通信号控制框架（TSDRL-TSC），以提高行人安全性和交叉口的整体交通效率。基于车联网（CV）的先进通信技术，TSDRL-TSC从实时交通状况中获取数据并动态调整交通信号，旨在最大限度地减少行人和车辆的交通冲突与延误。在第一步中，TSDRL-TSC根据行人状况决定是使用传统的四信号相位还是考虑保护/禁止右转（PPRT）策略的修改版本。在第二步中，TSDRL-TSC通过深度强化学习技术优化具体控制方案，为当前交叉口状态选择最优信号相位/动作以获得长期奖励回报。奖励函数考虑了所有交通参与者的安全性和效率，旨在平衡对行人安全、行人效率和车辆通行能力的要求。在长沙市一个具有代表性的六车道双向交叉口进行的仿真实验验证了该方法的有效性。结果表明：（1）与自适应交通信号控制和PPRT控制相比，TSDRL-TSC显著减少了行人与车辆的冲突、乱穿马路事件和总延误；（2）TSDRL-TSC作为一种强大的解决方案，在复杂城市交通管理中提高行人安全和交通效率方面具有潜力。
