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Investigation of injury severity in urban expressway crashes: A case study from Beijing

Urban expressway is the main artery of traffic network, and an in-depth analysis of the crashes is crucial for improving the traffic safety level of expressways. This study intended to address the injury severity of expressways in Beijing by proposing Bayesian ordered logistic regression model. Cras...

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Detalles Bibliográficos
Autores principales: Yuan, Quan, Xu, Xuecai, Zhao, Junwei, Zeng, Qiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6957292/
https://www.ncbi.nlm.nih.gov/pubmed/31929601
http://dx.doi.org/10.1371/journal.pone.0227869
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author Yuan, Quan
Xu, Xuecai
Zhao, Junwei
Zeng, Qiang
author_facet Yuan, Quan
Xu, Xuecai
Zhao, Junwei
Zeng, Qiang
author_sort Yuan, Quan
collection PubMed
description Urban expressway is the main artery of traffic network, and an in-depth analysis of the crashes is crucial for improving the traffic safety level of expressways. This study intended to address the injury severity of expressways in Beijing by proposing Bayesian ordered logistic regression model. Crash data were collected from urban express rings and expressways in 2015 and 2016. The results showed that crash location, time and crash season are significant variables influencing injury severity. The findings revealed that the proposed model can address the ordinal feature of injury severity, while accommodating the data with small sample sizes that may not adequately represent population characteristics. The conclusions can provide the management departments with valuable suggestions for the injury prevention and safety improvement on the urban expressways.
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spelling pubmed-69572922020-01-26 Investigation of injury severity in urban expressway crashes: A case study from Beijing Yuan, Quan Xu, Xuecai Zhao, Junwei Zeng, Qiang PLoS One Research Article Urban expressway is the main artery of traffic network, and an in-depth analysis of the crashes is crucial for improving the traffic safety level of expressways. This study intended to address the injury severity of expressways in Beijing by proposing Bayesian ordered logistic regression model. Crash data were collected from urban express rings and expressways in 2015 and 2016. The results showed that crash location, time and crash season are significant variables influencing injury severity. The findings revealed that the proposed model can address the ordinal feature of injury severity, while accommodating the data with small sample sizes that may not adequately represent population characteristics. The conclusions can provide the management departments with valuable suggestions for the injury prevention and safety improvement on the urban expressways. Public Library of Science 2020-01-13 /pmc/articles/PMC6957292/ /pubmed/31929601 http://dx.doi.org/10.1371/journal.pone.0227869 Text en © 2020 Yuan et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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
Yuan, Quan
Xu, Xuecai
Zhao, Junwei
Zeng, Qiang
Investigation of injury severity in urban expressway crashes: A case study from Beijing
title Investigation of injury severity in urban expressway crashes: A case study from Beijing
title_full Investigation of injury severity in urban expressway crashes: A case study from Beijing
title_fullStr Investigation of injury severity in urban expressway crashes: A case study from Beijing
title_full_unstemmed Investigation of injury severity in urban expressway crashes: A case study from Beijing
title_short Investigation of injury severity in urban expressway crashes: A case study from Beijing
title_sort investigation of injury severity in urban expressway crashes: a case study from beijing
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6957292/
https://www.ncbi.nlm.nih.gov/pubmed/31929601
http://dx.doi.org/10.1371/journal.pone.0227869
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