Enhancing emergency pedestrian safety through flow rate design: Bayesian-Nash Equilibrium in multi-agent system

Abstract

To optimally design and safely manage public facilities (e.g. pedestrian tunnel), one need to understand the safe pedestrian flow rate. In this research, we employed a multi-agent system to simulate the pedestrians and use Bayesian-Nash Equilibrium to model their decision-making process. In the model, the tunnel is divided into cells, with each pedestrian in a cell receiving a utility based on the distance to the exit and the number of pedestrians in the cell. Then, each pedestrian uses the Bayesian-Nash Equilibrium to search for the target cell with the maximum expected utility, takes collision avoidance action before moving into the target cell and then searches for the next target cell until exits the tunnel. The proposed model is validated from a real-world scenario. We also conduct sensitivity analysis to derive insights and study the robustness of our model. From the proposed model, we find expanding the tunnel width by one meter will allow the safe pedestrian flow rate to increase by about 3 pedestrians per second. We contribute to the literature by combining incomplete information game with multi-agent system and to practice by offering a novel method for reducing potential losses caused by crowd emergencies. Our work could be a valuable reference for managing dense pedestrian flows and designing public places.