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Exploring Motorcyclist Injury Severity Resulting from Various Crash Configurations at T-Junctions in the United Kingdom—An Application of the Ordered Probit Models

Pai, Chih-Wei; Saleh, Wafaa

Authors

Chih-Wei Pai



Abstract

Objectives . The fact that motorcycle users tend to be more vulnerable to injuries than those using other motorized vehicles may act synergistically with the complexity of conflicting movements between vehicles and motorcycles to increase injury severity in a junction-type accident. A junction-type collision tends to be more severe than a non-junction case due to the fact that some of the injurious crashes such as angle-collision commonly occur. Existing studies have applied several statistical modeling techniques to examine influential factors on the occurrences of different crashes among motorized vehicles but surprisingly very little has empirically explored whether a particular crash type, resulting from a junction-type accident, is more injurious to motorcyclists. This article attempts to investigate whether a particular collision is more deadly to motorcyclists conditioned on crash occurrence at T-junctions in the U.K., while controlling for environment, vehicle, and demographic factors. Methods . The statistical modeling technique employed is the ordered probit models using the data extracted from the STATS19 accident injury database (1999–2004). Results . The modeling found determinants of injury severity among motorcyclists at T-junctions in the U.K. For example, an approach-turn/head-on collision is much more injurious to motorcyclists; and, those riding in early morning (i.e., 0000–0659) are more likely to be severely injured. Conclusions . This study offers a guideline for future research, as well as insight into potential prevention strategies that might help moderate motorcyclist injuries.

Citation

Pai, C., & Saleh, W. (2007). Exploring Motorcyclist Injury Severity Resulting from Various Crash Configurations at T-Junctions in the United Kingdom—An Application of the Ordered Probit Models. Traffic Injury Prevention, 8(1), 62-68. https://doi.org/10.1080/15389580600991376

Journal Article Type Article
Acceptance Date Sep 5, 2006
Online Publication Date Jan 24, 2007
Publication Date Jan 24, 2007
Deposit Date Apr 27, 2017
Journal Traffic Injury Prevention
Print ISSN 1538-9588
Electronic ISSN 1538-957X
Publisher Taylor & Francis
Peer Reviewed Peer Reviewed
Volume 8
Issue 1
Pages 62-68
DOI https://doi.org/10.1080/15389580600991376
Keywords Public Health, Environmental and Occupational Health; Safety Research
Public URL http://researchrepository.napier.ac.uk/Output/834315