A novel data-driven multi-agent pedestrian flow risk assessment framework for avoiding stampede incident

This paper addresses the critical issue of monitoring high-density crowds in public spaces like transportation hubs to prevent accidents from overcrowding. It highlights the limitations of prevailing simulation tools in dealing with real-world challenges such as diverse pedestrian destinations, multi-directional flows, and the medley space designs in communal areas. The paper aims to introduce a data-driven, multi-agent framework that assesses crowd dynamics and early warning conditions in different spatial layouts. The model utilizes real-time visual information and reinforcement learning for decision-making, employing a self-iterative algorithm for trajectory planning that aligns with real-world movement characteristics. It enhances model compatibility across various scenarios without the need for parameter fine-tuning. The analysis shows the model's ability to accurately reproduce pedestrian flow motion in diverse scenarios and indicates a discontinuous state transition in pedestrian flow as density increases. A method for detecting building traffic capacity is proposed, which can identify the threshold of stable pedestrian flow that various spatial arrangements can accommodate, thereby allowing for the advance setting of crowding warning levels. The study suggests that rational spatial layout and information guidance can significantly improve spatial mobility and reduce the risk of crowd stampedes, without expanding the area of architectural spaces.