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<p>Emergency vehicle (EV) progression at consecutive arterial intersections in an intelligent connected environment is often disrupted by upstream queues, downstream spillback, and mismatches in multi-intersection signal coordination. To address this problem, this study proposes a spatiotemporal graph multi-agent proximal policy optimization (STG-MAPPO) method for EV signal priority control. The proposed method models consecutive signalized intersections as cooperative agents, incorporates EV arrival prediction, target-lane yielding capacity, and downstream blockage risk into the state representation, and uses a spatiotemporal graph encoder to capture both topological coupling and traffic-state propagation among intersections. Heterogeneous yielding responses of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) are explicitly considered in the target-lane yielding model. Simulation experiments based on the Zhengzhou Longhu Autonomous Driving Test Zone show that STG-MAPPO achieves better EV progression efficiency, lower general-traffic delay, and improved safety indicators than fixed-time control, actuated control, rule-based EV priority, DDQN, IPPO, and MAPPO. Under the specified SUMO-based single-EV simulation conditions, the proposed method reduces EV travel time, EV delay, and EV stop frequency by 22.28%, 51.02%, and 60.00%, respectively, compared with DDQN, and by 8.54%, 22.58%, and 33.33%, respectively, compared with MAPPO.</p>

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Emergency vehicle signal priority control method for arterial intersections in an intelligent connected environment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月11日

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  • Sen Cao,
  • Xingchen Zhang,
  • Wenfang Li,
  • Pengfei Sun

摘要

Emergency vehicle (EV) progression at consecutive arterial intersections in an intelligent connected environment is often disrupted by upstream queues, downstream spillback, and mismatches in multi-intersection signal coordination. To address this problem, this study proposes a spatiotemporal graph multi-agent proximal policy optimization (STG-MAPPO) method for EV signal priority control. The proposed method models consecutive signalized intersections as cooperative agents, incorporates EV arrival prediction, target-lane yielding capacity, and downstream blockage risk into the state representation, and uses a spatiotemporal graph encoder to capture both topological coupling and traffic-state propagation among intersections. Heterogeneous yielding responses of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) are explicitly considered in the target-lane yielding model. Simulation experiments based on the Zhengzhou Longhu Autonomous Driving Test Zone show that STG-MAPPO achieves better EV progression efficiency, lower general-traffic delay, and improved safety indicators than fixed-time control, actuated control, rule-based EV priority, DDQN, IPPO, and MAPPO. Under the specified SUMO-based single-EV simulation conditions, the proposed method reduces EV travel time, EV delay, and EV stop frequency by 22.28%, 51.02%, and 60.00%, respectively, compared with DDQN, and by 8.54%, 22.58%, and 33.33%, respectively, compared with MAPPO.