title: Exploring motorcyclist injury severity resulting from various crash configurations at T-junctions in the United Kingdom--an application of the ordered probit models.
abstract: OBJECTIVE
  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.
authors: Transport Research Institute, Napier University, Edinburgh, Scotland. c.pai@napier.ac.uk
authors: Chih-Wei
authors: CW
authors: Pai
authors: Wafaa
authors: W
authors: Saleh
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D009049:Motorcycles / Q000706:statistics & numerical data*; D015599:Trauma Severity Indices; D006113:United Kingdom / Q000453:epidemiology; D014887:Weather; D014947:Wounds and Injuries / Q000453:epidemiology*