Motorcyclists tend to be more vulnerable to injuries than those using other motorised vehicles and this 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 crash can be more severe to motorcyclists than a non-junction case due to the fact that some of the injurious crashes such as angle crash commonly occur. Previous studies have applied crash prediction models to investigate influential factors on the occurrences of different crashes among motorised vehicles but statistical models of motorcyclist injury severity resulting from different collision types have rarely been developed. This paper develops injury severity models for different collision-types conditioned on crash occurrence at T-junctions in the UK. The ordered logit models are estimated using human, weather, road and vehicle factors as predictors and the data for the model estimation were extracted from the STATS19 accident injury database (1991-2004). The modelling results show that motorcyclist injury severity in specific crash types is associated with predictor variables in different ways. This study offers a guideline for future research, as well as insight into potential prevention strategies that might help prevent the most hazardous situation(s) from occurring in different collision types.
ASJC Scopus subject areas
- Chemical Health and Safety
- Safety, Risk, Reliability and Quality
- Public Health, Environmental and Occupational Health
- Health Professions(all)
- Human Factors and Ergonomics
- Safety Research