Estimation of Multi-Agent Simulation Results of Crowd Evacuation Using Graph Neural Network

Abstract

In this paper, we estimated the evacuation behavior, computed by Multi-Agent Simulation (MAS), using Graph Neural Network (GNN). The results computed by MAS are useful for determining guidance strategies and building design in the event of a disaster. However, the computational time strongly depends on the number of agents and the complexity of the agents model. Therefore, We perform simulations by replacing the computation of MAS with machine learning models. Specifically, we construct a GNN that represents the building structure as a graph and estimate the subsequent agent positions using the agent positions until a certain point in the MAS as input. We evaluated GNN with evacuation behavior data computed by MAS and found that GNN express the unique properties of evacuation behavior.