title: Predicting the severity of motor vehicle accident injuries using models of ordered multiple choice.
abstract: This paper presents statistical evidence showing how variations in the attributes of road users can lead to variations in the probabilities of sustaining different levels of injury in motor vehicle accidents. Data from New South Wales, Australia, is used to estimate two models of multiple choice which are reasonably commonplace in the econometrics literature: the ordered logit model and the ordered probit model. Our estimated parameters are significantly different from zero at small levels of significance and have signs which are consistent with our prior beliefs. As a benchmark for comparison, we consider the risks faced by a 33-year-old male driver of a 10-year-old motor vehicle who is involved in a head-on collision while travelling at 42 kilometres per hour. We estimate that this benchmark victim will remain uninjured with a probability of almost zero, will require treatment from a medical officer with a probability of approximately 0.7, will be admitted to hospital with a probability of approximately 0.3, and will be killed with a probability of almost zero. We find that increases in the age of the victim and vehicle speed lead to slight increases in the probabilities of serious injury and death. Other factors which have a similar or greater effect on the probabilities of different types of injury include seating position, blood alcohol level, vehicle type, vehicle make and type of collision.
authors.affiliation: Department of Econometrics, University of New England, Armidale, NSW, Australia.
authors.forename: C J
authors.initials: CJ
authors.lastname: O'Donnell
authors.forename: D H
authors.initials: DH
authors.lastname: Connor
mesh_terms: D000054:Acceleration; D000063:Accidents, Traffic / Q000517:prevention & control / Q000706:statistics & numerical data*; D000328:Adult; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D009104:Multiple Trauma / Q000453:epidemiology* / Q000517:prevention & control; D010343:Patient Admission / Q000706:statistics & numerical data; D011336:Probability