关键词: 智能交通, 行人流管控, 深度强化学习, 行人流仿真建模, 近端策略优化

Abstract: This study addresses the dynamic and coordinated control of pedestrian flow within metro stations, specifically during peak hours and under the conditions of sudden passenger surges. A station-level pedestrian flow control as a Markov decision process is formulated, a state space is constructed centered on key-area crowd density and travel time, and a multi-objective design is rewarded which jointly account for safety, efficiency, and operating cost. Using the Proximal Policy Optimization (PPO) within a simulation environment, the agent performs online joint optimization over entrance inflow control, train headway adjustment, queue-guidance barrier deployment, and other strategies. The application of reinforcement learning enables coordinated pedestrian flow control through dynamic online decision-making, rather than traditional static offline optimization. Compared with a static optimization benchmark, the PPO policy reduces high-density risk and service delays: the share of high-density states in the security screening area decreases from 22% to 10%, and the proportion of passengers with security queue times more than 3 minutes drops from 23% to 13%. These results indicate that the proposed method improves the operational efficiency while it satisfies safety thresholds and balancing relative operating costs.

Key words: intelligent transportation, pedestrian crowd management, deep reinforcement learning, pedestrian flow modeling and simulation, proximal policy optimization