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Title: Surrogate assisted calibration framework for crowd model calibration
Authors: Yi, Wenchao
Zhong, Jinghui
Tan, Singkuang
Cai, Wentong
Hu, Nan
Keywords: DRNTU::Engineering::Computer science and engineering
Crowd Simulation
Model Calibration
Issue Date: 2017
Source: Yi, 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.2017.8247868
Abstract: Surrogate 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.
DOI: 10.1109/WSC.2017.8247868
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:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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