Literature DB >> 24667236

Comprehensive analysis of single- and multi-vehicle large truck at-fault crashes on rural and urban roadways in Alabama.

Samantha Islam1, Steven L Jones2, Daniel Dye3.   

Abstract

The research described in this paper analyzed injury severities at a disaggregate level for single-vehicle (SV) and multi-vehicle (MV) large truck at-fault accidents for rural and urban locations in Alabama. Given the occurrence of a crash, four separate random parameter logit models of injury severity (with possible outcomes of major, minor, and possible or no injury) were estimated. The models identified different sets of factors that can lead to effective policy decisions aimed at reducing large truck-at-fault accidents for respective locations. The results of the study clearly indicated that there are differences between the influences of a variety of variables on the injury severities resulting from urban vs. rural SV and MV large truck at-fault accidents. The results showed that some variables were significant only in one type of accident model (SV or MV) but not in the other accident model. Again, some variables were found to be significant in one location (rural or urban) but not in other locations. The study also identified important factors that significantly impact the injury severity resulting from SV and MV large truck at-fault accidents in urban and rural locations based on the estimated values of average direct pseudo-elasticity. A careful study of the results of this study will help policy makers and transportation agencies identify location specific recommendations to increase safety awareness related to large truck involved accidents and to improve overall highway safety.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Large truck at-fault crashes; Multi-vehicle crashes; Random parameter logit models; Rural; Single-vehicle crashes; Urban

Mesh:

Year:  2014        PMID: 24667236     DOI: 10.1016/j.aap.2014.02.014

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  7 in total

1.  Exploring background risk factors for fatigue crashes involving truck drivers on regional roadway networks: a case control study in Jiangxi and Shaanxi, China.

Authors:  Changkun Chen; Jun Zhang
Journal:  Springerplus       Date:  2016-05-10

2.  Investigation on occupant injury severity in rear-end crashes involving trucks as the front vehicle in Beijing area, China.

Authors:  Quan Yuan; Meng Lu; Athanasios Theofilatos; Yi-Bing Li
Journal:  Chin J Traumatol       Date:  2016-11-09

3.  Risk factors for extremely serious road accidents: Results from national Road Accident Statistical Annual Report of China.

Authors:  Guodong Liu; Siyu Chen; Ziqian Zeng; Huijie Cui; Yanfei Fang; Dongqing Gu; Zhiyong Yin; Zhengguo Wang
Journal:  PLoS One       Date:  2018-08-01       Impact factor: 3.240

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

Authors:  Xiaojun Shao; Xiaoxiang Ma; Feng Chen; Mingtao Song; Xiaodong Pan; Kesi You
Journal:  Int J Environ Res Public Health       Date:  2020-01-07       Impact factor: 3.390

5.  Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network.

Authors:  Arshad Jamal; Waleed Umer
Journal:  Int J Environ Res Public Health       Date:  2020-10-14       Impact factor: 3.390

6.  How did the COVID-19 pandemic affect road crashes and crash outcomes in Alabama?

Authors:  Emmanuel Kofi Adanu; David Brown; Steven Jones; Allen Parrish
Journal:  Accid Anal Prev       Date:  2021-10-06

7.  Severity Analysis of Hazardous Material Road Transportation Crashes with a Bayesian Network Using Highway Safety Information System Data.

Authors:  Ming Sun; Ronggui Zhou; Chengwu Jiao; Xiaoduan Sun
Journal:  Int J Environ Res Public Health       Date:  2022-03-28       Impact factor: 3.390

  7 in total

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