Density-based evolutionary framework for crowd model calibration

Abstract

Crowd modeling and simulation is an important and active research field, with a wide range of applications such as computer games, military training and evacuation modeling. One important issue in crowd modeling is model calibration through parameter tuning, so as to produce desired crowd behaviors. Common methods such as trial-and-error are time consuming and tedious. This paper proposes an evolutionary framework to automate the crowd model calibration process. In the proposed framework, a density-based matching scheme is introduced. By using the dynamic density of the crowd over time, and a weight landscape to emphasize important spatial regions, the proposed matching scheme provides a generally applicable way to evaluate the simulated crowd behaviors. Besides, a hybrid search mechanism based on differential evolution is proposed to efficiently tune parameters of crowd models. Simulation results demonstrate that the proposed framework is effective and efficient to calibrate the crowd models in order to produce desired macroscopic crowd behaviors.