dc.contributor.authorZhong, Jinghui
dc.contributor.authorHu, Nan
dc.contributor.authorCai, Wentong
dc.contributor.authorLees, Micheal
dc.contributor.authorLuo, Linbo
dc.identifier.citationZhong, 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.en_US
dc.description.abstractCrowd 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.en_US
dc.description.sponsorshipASTAR (Agency for Sci., Tech. and Research, S’pore)
dc.relation.ispartofseriesJournal of computational scienceen_US
dc.rights© 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].en_US
dc.subjectDRNTU::Engineering::Computer science and engineering
dc.titleDensity-based evolutionary framework for crowd model calibrationen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.versionAccepted version

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