A Data Driven Approach to Simulate Pedestrian Competitiveness Using the Social Force Model

Abstract

The research of pedestrian evacuation dynamics is of significance to understanding and preventing human stampedes. Since empirical approach of reproducing true emergency evacuations is impossible due to safety issues. Theoretical approach based on numerical simulation has called the attention from researchers. In the simulation of pedestrian evacuation, a critical problem is how to simulate pedestrian competitiveness to reproduce emergency evacuation. Based on the social force model, researchers have tried to simulate pedestrian competitiveness through adjusting some model parameters. However, in most cases handcrafted values are adopted without calibration, thus unrealistic results might be produced. In this study, we applied a differential evolutionary algorithm to determine the optimal parameter specifications of the social force model by adjustment to empirical data. We conducted pedestrian experiments where five participants including patient and impatient individuals proceeded through a narrow corridor. Taking the distance between simulation results and empirical data as objective function, a minimization problem was generated. A differential evolutionary algorithm was adopted to search for the optimal combination of parameters. We found that though at initialization all the parameter values were randomly determined, the difference between patient and impatient pedestrians could be captured by adjustment to empirical data. This highlights the need to better understand and research pedestrian heterogeneity in terms of competitiveness.