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A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions

Social and economic burdens caused by truck-involved rear-end collisions are of great concern to public health and the environment. However, few efforts focused on identifying the difference of impacting factors on injury severity between car-strike-truck and truck-strike-car in rear-end collisions....

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Autores principales: Shao, Xiaojun, Ma, Xiaoxiang, Chen, Feng, Song, Mingtao, Pan, Xiaodong, You, Kesi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7013549/
https://www.ncbi.nlm.nih.gov/pubmed/31936087
http://dx.doi.org/10.3390/ijerph17020395
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author Shao, Xiaojun
Ma, Xiaoxiang
Chen, Feng
Song, Mingtao
Pan, Xiaodong
You, Kesi
author_facet Shao, Xiaojun
Ma, Xiaoxiang
Chen, Feng
Song, Mingtao
Pan, Xiaodong
You, Kesi
author_sort Shao, Xiaojun
collection PubMed
description Social and economic burdens caused by truck-involved rear-end collisions are of great concern to public health and the environment. However, few efforts focused on identifying the difference of impacting factors on injury severity between car-strike-truck and truck-strike-car in rear-end collisions. In light of the above, this study focuses on illustrating the impact of variables associated with injury severity in truck-related rear-end crashes. To this end, truck involved rear-end crashes between 2006 and 2015 in the U.S. were obtained. Three random parameters ordered probit models were developed: two separate models for the car-strike-truck crashes and the truck-strike-car crashes, respectively, and one for the combined dataset. The likelihood ratio test was conducted to evaluate the significance of the difference between the models. The results show that there is a significant difference between car-strike-truck and truck-strike-car crashes in terms of contributing factors towards injury severity. In addition, indicators reflecting male, truck, starting or stopped in the road before a crash, and other vehicles stopped in lane show a mixed impact on injury severity. Corresponding implications were discussed according to the findings to reduce the possibility of severe injury in truck-involved rear-end collisions.
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spelling pubmed-70135492020-03-09 A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions Shao, Xiaojun Ma, Xiaoxiang Chen, Feng Song, Mingtao Pan, Xiaodong You, Kesi Int J Environ Res Public Health Article Social and economic burdens caused by truck-involved rear-end collisions are of great concern to public health and the environment. However, few efforts focused on identifying the difference of impacting factors on injury severity between car-strike-truck and truck-strike-car in rear-end collisions. In light of the above, this study focuses on illustrating the impact of variables associated with injury severity in truck-related rear-end crashes. To this end, truck involved rear-end crashes between 2006 and 2015 in the U.S. were obtained. Three random parameters ordered probit models were developed: two separate models for the car-strike-truck crashes and the truck-strike-car crashes, respectively, and one for the combined dataset. The likelihood ratio test was conducted to evaluate the significance of the difference between the models. The results show that there is a significant difference between car-strike-truck and truck-strike-car crashes in terms of contributing factors towards injury severity. In addition, indicators reflecting male, truck, starting or stopped in the road before a crash, and other vehicles stopped in lane show a mixed impact on injury severity. Corresponding implications were discussed according to the findings to reduce the possibility of severe injury in truck-involved rear-end collisions. MDPI 2020-01-07 2020-01 /pmc/articles/PMC7013549/ /pubmed/31936087 http://dx.doi.org/10.3390/ijerph17020395 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shao, Xiaojun
Ma, Xiaoxiang
Chen, Feng
Song, Mingtao
Pan, Xiaodong
You, Kesi
A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions
title A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions
title_full A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions
title_fullStr A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions
title_full_unstemmed A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions
title_short A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions
title_sort random parameters ordered probit analysis of injury severity in truck involved rear-end collisions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7013549/
https://www.ncbi.nlm.nih.gov/pubmed/31936087
http://dx.doi.org/10.3390/ijerph17020395
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