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A high-density crowd state judgment model based on entropy theory

A high-density crowd state is prone to cause large-scale crowd stampede accidents that seriously threaten the people’s security and property. The key to preventing crowd congestion is to accurately predict the location and time of crowd events, particularly when there is a high density of people. In...

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Detalles Bibliográficos
Autores principales: Zhao, Guomin, Li, Cong, Xu, Guangji, He, Falong, Zhang, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8412320/
https://www.ncbi.nlm.nih.gov/pubmed/34473772
http://dx.doi.org/10.1371/journal.pone.0255468
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author Zhao, Guomin
Li, Cong
Xu, Guangji
He, Falong
Zhang, Jing
author_facet Zhao, Guomin
Li, Cong
Xu, Guangji
He, Falong
Zhang, Jing
author_sort Zhao, Guomin
collection PubMed
description A high-density crowd state is prone to cause large-scale crowd stampede accidents that seriously threaten the people’s security and property. The key to preventing crowd congestion is to accurately predict the location and time of crowd events, particularly when there is a high density of people. In this paper, the entropy theory is used to characterize the state of a crowded system. The theoretical entropy S(r) and the actual entropy S of the crowd system are obtained according to the area occupied by the different crowd state The maximum entropy value and the actual entropy value under different conditions of the system are compared to judge the state of crowded extent. The results show that the model is practical and effective. According to the situation of the crowd, different management and evacuation measures are considered to prevent the occurrence of crowd accidents.
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spelling pubmed-84123202021-09-03 A high-density crowd state judgment model based on entropy theory Zhao, Guomin Li, Cong Xu, Guangji He, Falong Zhang, Jing PLoS One Research Article A high-density crowd state is prone to cause large-scale crowd stampede accidents that seriously threaten the people’s security and property. The key to preventing crowd congestion is to accurately predict the location and time of crowd events, particularly when there is a high density of people. In this paper, the entropy theory is used to characterize the state of a crowded system. The theoretical entropy S(r) and the actual entropy S of the crowd system are obtained according to the area occupied by the different crowd state The maximum entropy value and the actual entropy value under different conditions of the system are compared to judge the state of crowded extent. The results show that the model is practical and effective. According to the situation of the crowd, different management and evacuation measures are considered to prevent the occurrence of crowd accidents. Public Library of Science 2021-09-02 /pmc/articles/PMC8412320/ /pubmed/34473772 http://dx.doi.org/10.1371/journal.pone.0255468 Text en © 2021 Zhao et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zhao, Guomin
Li, Cong
Xu, Guangji
He, Falong
Zhang, Jing
A high-density crowd state judgment model based on entropy theory
title A high-density crowd state judgment model based on entropy theory
title_full A high-density crowd state judgment model based on entropy theory
title_fullStr A high-density crowd state judgment model based on entropy theory
title_full_unstemmed A high-density crowd state judgment model based on entropy theory
title_short A high-density crowd state judgment model based on entropy theory
title_sort high-density crowd state judgment model based on entropy theory
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8412320/
https://www.ncbi.nlm.nih.gov/pubmed/34473772
http://dx.doi.org/10.1371/journal.pone.0255468
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