Simulation investigation on crowd evacuation strategies for helping vulnerable pedestrians at different stages of egress

Abstract

In emergencies, evacuations move pedestrians to safety. Extensive research has investigated strategies that can improve evacuation efficiency of pedestrian crowds in various contexts. However, most of this research considers pedestrians as homogeneous agents and the question of how vulnerable pedestrians can be supported in evacuations has not been convincingly resolved. Here, we focus not only on pedestrians with the same characteristics but also on vulnerable pedestrians defined by moving at a lower speed or being located further away from exits. We investigate strategies for helping these vulnerable pedestrians in three evacuation stages: the pre-evacuation stage where vulnerable pedestrians can respond to incidents quickly, the response stage where vulnerable pedestrians can be preferentially assigned to exits in the case of limited exit resources, and the evacuation phase where we place an obstacle in front of the exit. Our simulation results indicate that the effectiveness of these strategies depends on crowd characteristics and contexts (e.g., crowd size and pedestrian initial distribution), and different strategies may have other consequences, such as making pedestrians experience a higher accumulated force or walk longer distances. We found that only in some cases machine learning methods including kernel naïve Bayes and Linear SVM can predict which strategy will work best based on the initial distribution of pedestrians, suggesting that at least sometimes it may be possible to select strategies based on observed conditions. Our work highlights the importance of context and heterogeneous populations when developing strategies for evacuations and may thus be helpful for crowd safety and evacuation management.