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Optimal exit choice during highway tunnel evacuations based on the fire locations

In the case of a fire, the choice of exit in the highway tunnel is strictly limited by fire location, which seriously affects the evacuation time. A spontaneous or disorderly exit choice might result in a decreased evacuation efficiency and utilization rate of exits. In this paper, we propose a stra...

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Autores principales: Wang, Yuchen, Ma, Jianxiao, Liu, Yuhang, Bai, Yingjia, Xu, Le
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378735/
https://www.ncbi.nlm.nih.gov/pubmed/34415937
http://dx.doi.org/10.1371/journal.pone.0256523
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author Wang, Yuchen
Ma, Jianxiao
Liu, Yuhang
Bai, Yingjia
Xu, Le
author_facet Wang, Yuchen
Ma, Jianxiao
Liu, Yuhang
Bai, Yingjia
Xu, Le
author_sort Wang, Yuchen
collection PubMed
description In the case of a fire, the choice of exit in the highway tunnel is strictly limited by fire location, which seriously affects the evacuation time. A spontaneous or disorderly exit choice might result in a decreased evacuation efficiency and utilization rate of exits. In this paper, we propose a strategy to obtain the optimal exit choice based on fire location during highway tunnel evacuations. In our strategy, first, the vehicle distributions and locations of evacuating occupants are determined in the traffic simulation program VISSIM. The evacuation simulation software BuildingEXODUS is employed to obtain the corresponding parameters of the evacuation process and analyze the impacts of different fire locations on the evacuation time. During the analysis, the optimal productivity statistics (OPS) is selected as the evaluation index. Then, the feature points of the crowding occupants are captured by the fuzzy c-means (FCM) cluster algorithm. Next, based on the feature points, the relationship between the location of the fire and boundary of the optimal exit choice under the optimal OPS is obtained through the polynomial regression model. It is found that the R-squared(R(2)) and sum of squares for error (SSE) of the polynomial regression model, reflecting the accuracy estimation, are 98.02% and 2.79×10(−4), respectively. Moreover, different fire locations impact the evacuation time of tunnel entrance and evacuation passageway. This paper shows that the location of the fire and boundary of optimal exit choice have a negative linear correlation. Taking the fire 110 m away from the evacuation passageway as an example, the OPS of our strategy can be decreased by 35.6% when compared with no strategies. Our proposed strategy could be applied to determine the location of variable evacuation signs to help evacuating occupants make optimal exit choices.
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spelling pubmed-83787352021-08-21 Optimal exit choice during highway tunnel evacuations based on the fire locations Wang, Yuchen Ma, Jianxiao Liu, Yuhang Bai, Yingjia Xu, Le PLoS One Research Article In the case of a fire, the choice of exit in the highway tunnel is strictly limited by fire location, which seriously affects the evacuation time. A spontaneous or disorderly exit choice might result in a decreased evacuation efficiency and utilization rate of exits. In this paper, we propose a strategy to obtain the optimal exit choice based on fire location during highway tunnel evacuations. In our strategy, first, the vehicle distributions and locations of evacuating occupants are determined in the traffic simulation program VISSIM. The evacuation simulation software BuildingEXODUS is employed to obtain the corresponding parameters of the evacuation process and analyze the impacts of different fire locations on the evacuation time. During the analysis, the optimal productivity statistics (OPS) is selected as the evaluation index. Then, the feature points of the crowding occupants are captured by the fuzzy c-means (FCM) cluster algorithm. Next, based on the feature points, the relationship between the location of the fire and boundary of the optimal exit choice under the optimal OPS is obtained through the polynomial regression model. It is found that the R-squared(R(2)) and sum of squares for error (SSE) of the polynomial regression model, reflecting the accuracy estimation, are 98.02% and 2.79×10(−4), respectively. Moreover, different fire locations impact the evacuation time of tunnel entrance and evacuation passageway. This paper shows that the location of the fire and boundary of optimal exit choice have a negative linear correlation. Taking the fire 110 m away from the evacuation passageway as an example, the OPS of our strategy can be decreased by 35.6% when compared with no strategies. Our proposed strategy could be applied to determine the location of variable evacuation signs to help evacuating occupants make optimal exit choices. Public Library of Science 2021-08-20 /pmc/articles/PMC8378735/ /pubmed/34415937 http://dx.doi.org/10.1371/journal.pone.0256523 Text en © 2021 Wang 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
Wang, Yuchen
Ma, Jianxiao
Liu, Yuhang
Bai, Yingjia
Xu, Le
Optimal exit choice during highway tunnel evacuations based on the fire locations
title Optimal exit choice during highway tunnel evacuations based on the fire locations
title_full Optimal exit choice during highway tunnel evacuations based on the fire locations
title_fullStr Optimal exit choice during highway tunnel evacuations based on the fire locations
title_full_unstemmed Optimal exit choice during highway tunnel evacuations based on the fire locations
title_short Optimal exit choice during highway tunnel evacuations based on the fire locations
title_sort optimal exit choice during highway tunnel evacuations based on the fire locations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378735/
https://www.ncbi.nlm.nih.gov/pubmed/34415937
http://dx.doi.org/10.1371/journal.pone.0256523
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