Density-based evolutionary framework for crowd model calibration

Authored by Wentong Cai, Jinghui Zhong, Nan Hu, Michael Lees, Linbo Luo

Date Published: 2015

DOI: 10.1016/j.jocs.2014.09.002

Sponsors: Russian Scientific Foundation

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

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 overtime, 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. (C) 2014 Elsevier B.V. All rights reserved.

Tags

Genetic Algorithms Simulation Agent-based models Evacuation Environments