Density-based evolutionary framework for crowd model calibration

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....

Full description

Saved in:
Bibliographic Details
Main Authors: Zhong, Jinghui, Hu, Nan, Cai, Wentong, Lees, Micheal, Luo, Linbo
Other Authors: School of Computer Engineering
Format: Article
Language:English
Published: 2015
Subjects:
Online Access:https://hdl.handle.net/10356/107221
http://hdl.handle.net/10220/25350
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-107221
record_format dspace
spelling sg-ntu-dr.10356-1072212020-05-28T07:17:29Z Density-based evolutionary framework for crowd model calibration Zhong, Jinghui Hu, Nan Cai, Wentong Lees, Micheal Luo, Linbo School of Computer Engineering DRNTU::Engineering::Computer science and engineering 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. ASTAR (Agency for Sci., Tech. and Research, S’pore) Accepted version 2015-04-10T03:08:53Z 2019-12-06T22:27:00Z 2015-04-10T03:08:53Z 2019-12-06T22:27:00Z 2015 2015 Journal Article Zhong, J., Hu, N., Cai, W., Lees, M., & Luo, L. (2015). Density-based evolutionary framework for crowd model calibration. Journal of computational science, 6, 11-22. 1877-7503 https://hdl.handle.net/10356/107221 http://hdl.handle.net/10220/25350 10.1016/j.jocs.2014.09.002 184532 en Journal of computational science © 2014 Elsevier B.V. This is the author created version of a work that has been peer reviewed and accepted for publication by Journal of Computational Science, Elsevier B.V. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [Article DOI: http://dx.doi.org/10.1016/j.jocs.2014.09.002]. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Zhong, Jinghui
Hu, Nan
Cai, Wentong
Lees, Micheal
Luo, Linbo
Density-based evolutionary framework for crowd model calibration
description 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.
author2 School of Computer Engineering
author_facet School of Computer Engineering
Zhong, Jinghui
Hu, Nan
Cai, Wentong
Lees, Micheal
Luo, Linbo
format Article
author Zhong, Jinghui
Hu, Nan
Cai, Wentong
Lees, Micheal
Luo, Linbo
author_sort Zhong, Jinghui
title Density-based evolutionary framework for crowd model calibration
title_short Density-based evolutionary framework for crowd model calibration
title_full Density-based evolutionary framework for crowd model calibration
title_fullStr Density-based evolutionary framework for crowd model calibration
title_full_unstemmed Density-based evolutionary framework for crowd model calibration
title_sort density-based evolutionary framework for crowd model calibration
publishDate 2015
url https://hdl.handle.net/10356/107221
http://hdl.handle.net/10220/25350
_version_ 1681057887432998912