Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/105496
Title: Guide them through : an automatic crowd control framework using multi-objective genetic programming
Authors: Hu, Nan
Zhong, Jinghui
Zhou, Joey Tianyi
Zhou, Suiping
Cai, Wentong
Monterola, Christopher
Keywords: Crowd Control
DRNTU::Engineering::Computer science and engineering
Crowd Modelling And Simulation
Issue Date: 2018
Source: Hu, N., Zhong, J., Zhou, J. T., Zhou, S., Cai, W., & Monterola, C. (2018). Guide them through : an automatic crowd control framework using multi-objective genetic programming. Applied Soft Computing, 66, 90-103. doi:10.1016/j.asoc.2018.01.037
Series/Report no.: Applied Soft Computing
Abstract: We propose an automatic crowd control framework based on multi-objective optimisation of strategy space using genetic programming. In particular, based on the sensed local crowd densities at different segments, our framework is capable of generating control strategies that guide the individuals on when and where to slow down for optimal overall crowd flow in realtime, quantitatively measured by multiple objectives such as shorter travel time and less congestion along the path. The resulting Pareto-front allows selection of resilient and efficient crowd control strategies in different situations. We first chose a benchmark scenario as used in [1] to test the proposed method. Results show that our method is capable of finding control strategies that are not only quantitatively measured better, but also well aligned with domain experts’ recommendations on effective crowd control such as “slower is faster” and “asymmetric control”. We further applied the proposed framework in actual event planning with approximately 400 participants navigating through a multi-story building. In comparison with the baseline crowd models that do no employ control strategies or just use some hard-coded rules, the proposed framework achieves a shorter travel time and a significantly lower (20%) congestion along critical segments of the path.
URI: https://hdl.handle.net/10356/105496
http://hdl.handle.net/10220/47865
ISSN: 1568-4946
DOI: 10.1016/j.asoc.2018.01.037
Schools: School of Computer Science and Engineering 
Rights: © 2018 Elsevier B.V. All rights reserved. This paper was published in Applied Soft Computing and is made available with permission of Elsevier B.V.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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