PREDICTING THE SEVERITY OF MOTOR-VEHICLE ACCIDENT INJURIES USING MODELS OF ORDERED MULTIPLE-CHOICE

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. D ata from New South Wales, Australia, is used to estimate two models of multiple choice which are reasonably commonplace in the econometrics literature: the ordered legit model and the ordered probit model. Our estimated parameters are significantly different from zero at small le vels of significance and have signs which are consistent with our prio r 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 inv olved in a head-on collision while travelling at 42 kilometres per hou r. We estimate that this benchmark victim will remain uninjured with a probability of almost zero, will require treatment from a medical off icer with a probability of approximately 0.7, will be admitted to hosp ital 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 probabilitie s of serious injury and death. Other factors which have a similar or g reater effect on the probabilities of different types of injury includ e seating position, blood alcohol level, vehicle type, vehicle make an d type of collision. Copyright (C) 1996 Elsevier Science Ltd.