Automatic model construction for the behavior of human crowds

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

Date Published: 2017

DOI: 10.1016/j.asoc.2017.03.020

Sponsors: Chinese National Natural Science Foundation

Platforms: C++

Model Documentation: Other Narrative Flow charts Mathematical description

Model Code URLs: https://www.dropbox.com/sh/z8eos0qt63pqsv5/AAAgK4SeXdH22EBszA25fiB_a?dl=0

Abstract

Designing suitable behavioral rules of agents so as to generate realistic behaviors is a fundamental and challenging task in many forms of computational modeling. This paper proposes a novel methodology to automatically generate a descriptive model, in the form of behavioral rules, from video data of human crowds. In the proposed methodology, the problem of modeling crowd behaviors is formulated as a symbolic regression problem and the self-learning gene expression programming is utilized to solve the problem and automatically obtain behavioral rules that match data. To evaluate its effectiveness, we apply the proposed method to generate a model from a video dataset in Switzerland and then test the generality of the model by validating against video data from the United States. The results demonstrate that, based on the observed movement of people in one scenario, the proposed methodology can automatically construct a general model capable of describing the crowd dynamics of another scenario in a different context (e.g., Switzerland vs. U.S.) as long as that the crowd behavior patterns are similar. (C) 2017 Elsevier B.V. All rights reserved.

Tags

Simulation Agent-based modeling Agents calibration symbolic regression Crowd modeling and simulation Genetic programming Gene expression programming