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Predicting the water film depth: A model based on the geometric features of road and capacity of drainage facilities

The water film depth is a key variable that affects traffic safety under rainfall conditions. According to the Federal Highway Administration, approximately 5700 people are killed and more than 544 700 people are injured in crashes on wet pavements annually. While several studies have attempted to a...

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Detalles Bibliográficos
Autores principales: Han, Shuo, Xu, Jinliang, Yan, Menghua, Gao, Sunjian, Li, Xufeng, Huang, Xunjiang, Liu, Zhaoxin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8253438/
https://www.ncbi.nlm.nih.gov/pubmed/34214083
http://dx.doi.org/10.1371/journal.pone.0252767
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author Han, Shuo
Xu, Jinliang
Yan, Menghua
Gao, Sunjian
Li, Xufeng
Huang, Xunjiang
Liu, Zhaoxin
author_facet Han, Shuo
Xu, Jinliang
Yan, Menghua
Gao, Sunjian
Li, Xufeng
Huang, Xunjiang
Liu, Zhaoxin
author_sort Han, Shuo
collection PubMed
description The water film depth is a key variable that affects traffic safety under rainfall conditions. According to the Federal Highway Administration, approximately 5700 people are killed and more than 544 700 people are injured in crashes on wet pavements annually. While several studies have attempted to address water film depth issues by establishing prediction models, a few focused on the relationship among road geometric features, capacity of drainage facilities and water film depth. To ascertain the influence of the geometric features of road and facility drainage capacities on the water film depth, the road geometry features were first classified into four types, and the facility drainage capacities were considered from three aspects in this study. Furthermore, the concept of short-time rainfall grade was proposed according to the results of the field test. Finally, the theoretical prediction model for the water film depth was conceived, based on the geometric features of road and facility drainage capacities with different rainfall intensities. Compared with the traditional regression prediction models, the theoretical prediction model clearly shows the effects of the geometric features of road and facility drainage capacities. When the road drainage facilities have no drainage capacity, the water film depth increases rapidly with the rainfall intensity. This model can be used to predict the water film depth of road surfaces on rainy days, evaluate the effect of rainfall on the driving environment, and provide guidance for determining safety control measures on rainy days.
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spelling pubmed-82534382021-07-13 Predicting the water film depth: A model based on the geometric features of road and capacity of drainage facilities Han, Shuo Xu, Jinliang Yan, Menghua Gao, Sunjian Li, Xufeng Huang, Xunjiang Liu, Zhaoxin PLoS One Research Article The water film depth is a key variable that affects traffic safety under rainfall conditions. According to the Federal Highway Administration, approximately 5700 people are killed and more than 544 700 people are injured in crashes on wet pavements annually. While several studies have attempted to address water film depth issues by establishing prediction models, a few focused on the relationship among road geometric features, capacity of drainage facilities and water film depth. To ascertain the influence of the geometric features of road and facility drainage capacities on the water film depth, the road geometry features were first classified into four types, and the facility drainage capacities were considered from three aspects in this study. Furthermore, the concept of short-time rainfall grade was proposed according to the results of the field test. Finally, the theoretical prediction model for the water film depth was conceived, based on the geometric features of road and facility drainage capacities with different rainfall intensities. Compared with the traditional regression prediction models, the theoretical prediction model clearly shows the effects of the geometric features of road and facility drainage capacities. When the road drainage facilities have no drainage capacity, the water film depth increases rapidly with the rainfall intensity. This model can be used to predict the water film depth of road surfaces on rainy days, evaluate the effect of rainfall on the driving environment, and provide guidance for determining safety control measures on rainy days. Public Library of Science 2021-07-02 /pmc/articles/PMC8253438/ /pubmed/34214083 http://dx.doi.org/10.1371/journal.pone.0252767 Text en © 2021 Han 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
Han, Shuo
Xu, Jinliang
Yan, Menghua
Gao, Sunjian
Li, Xufeng
Huang, Xunjiang
Liu, Zhaoxin
Predicting the water film depth: A model based on the geometric features of road and capacity of drainage facilities
title Predicting the water film depth: A model based on the geometric features of road and capacity of drainage facilities
title_full Predicting the water film depth: A model based on the geometric features of road and capacity of drainage facilities
title_fullStr Predicting the water film depth: A model based on the geometric features of road and capacity of drainage facilities
title_full_unstemmed Predicting the water film depth: A model based on the geometric features of road and capacity of drainage facilities
title_short Predicting the water film depth: A model based on the geometric features of road and capacity of drainage facilities
title_sort predicting the water film depth: a model based on the geometric features of road and capacity of drainage facilities
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8253438/
https://www.ncbi.nlm.nih.gov/pubmed/34214083
http://dx.doi.org/10.1371/journal.pone.0252767
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