Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorYi, Wenchaoen
dc.contributor.authorZhong, Jinghuien
dc.contributor.authorTan, Singkuangen
dc.contributor.authorCai, Wentongen
dc.contributor.authorHu, Nanen
dc.identifier.citationYi, W., Zhong, J., Tan, S., Cai, W., & Hu, N. (2017). Surrogate assisted calibration framework for crowd model calibration. Proceedings of the 2017 Winter Simulation Conference, 1216-1227. doi:10.1109/WSC.2017.8247868en
dc.description.abstractSurrogate models are commonly used to approximate the multivariate input or output behavior of complex systems. In this paper, surrogate assisted calibration frameworks are proposed to calibrate the crowd model. To integrate the surrogate models into the evolutionary calibration framework, both the offline and online training based approaches are developed. The offline training needs to generate training set in advance, while the online training can adaptively build and re-build the surrogate model along the evolutionary process. Our simulation results demonstrate that the surrogate assisted calibration framework with the online training is effective and the surrogate model using artificial neural network obtains the best overall performance in the scenario evaluated in the case study.en
dc.format.extent12 p.en
dc.rights© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.subjectCrowd Simulationen
dc.subjectModel Calibrationen
dc.titleSurrogate assisted calibration framework for crowd model calibrationen
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.contributor.conference2017 Winter Simulation Conference (WSC)en
dc.description.versionAccepted versionen
item.fulltextWith Fulltext-
Appears in Collections:SCSE Conference Papers
Files in This Item:
File Description SizeFormat 
Surrogate assisted calibration framework for crowd model calibration.pdf971.49 kBAdobe PDFThumbnail

Google ScholarTM



Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.