Literature DB >> 34214083

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

Shuo Han1, Jinliang Xu2, Menghua Yan2, Sunjian Gao2, Xufeng Li2, Xunjiang Huang2, Zhaoxin Liu3.   

Abstract

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.

Entities:  

Year:  2021        PMID: 34214083      PMCID: PMC8253438          DOI: 10.1371/journal.pone.0252767

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  5 in total

1.  Rainfall effect on single-vehicle crash severities using polychotomous response models.

Authors:  Soyoung Jung; Xiao Qin; David A Noyce
Journal:  Accid Anal Prev       Date:  2009-08-14

Review 2.  A review of the effect of traffic and weather characteristics on road safety.

Authors:  Athanasios Theofilatos; George Yannis
Journal:  Accid Anal Prev       Date:  2014-07-31

3.  Risk of a road accident in rainy weather.

Authors:  H Brodsky; A S Hakkert
Journal:  Accid Anal Prev       Date:  1988-06

4.  Traffic accident severity analysis with rain-related factors using structural equation modeling - A case study of Seoul City.

Authors:  Jonghak Lee; Junghyo Chae; Taekwan Yoon; Hojin Yang
Journal:  Accid Anal Prev       Date:  2018-01-03

5.  Modelling fundamental diagrams according to different water film depths from the perspective of the dynamic hydraulic pressure.

Authors:  Mingwei Liu; Matunaga Chiaki; Yoshinao Oeda; Tomonori Sumi
Journal:  Sci Rep       Date:  2020-04-16       Impact factor: 4.379

  5 in total

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