Predicting and Optimizing Crowd Evacuations: An Explainable AI Approach

Abstract

In this paper, we explore the usability of an explainable Artificial Neural Network (ANN) model to provide recommendations for architectural improvements aimed at enhancing crowd safety and comfort during emergency situations. We trained an ANN to predict the outcomes of crowd simulations without the need for direct simulation, while also generating recommendations for the studied space. Our dataset comprises approximately 36,000 simulations of diverse crowds evacuating rooms of different sizes, capturing data on room characteristics, crowd composition, evacuation densities, times, and velocities. To identify the most influential environmental factors affecting evacuation performance, we employ Shapley values. Based on these insights, we propose modifications to the architectural design of the space. Our results demonstrate that the proposed model effectively predicts crowd dynamics and provides meaningful recommendations for improving evacuation efficiency and safety.