Modeling Injury Outcomes Of Crashes Involving Driver And Pedestrian Victims

Abstract

Understanding the factors which influence the severity of accidents is crucial to preventing serious injuries. In this study, we aim at investigating the contribution of several variables concerning the severity of accidents on the driver and pedestrian. This will be achieved by means of Multinomial Logit models (MNL) using the data of 10000 subjects involved in serious accidents. The empirical results of Multinomial Logit models are of a great variety. The results of the first model of drivers show that drivers whose ages lie between 26 and 45 years are more likely than younger age groups to be fatally injured when they are involved in motor vehicle accidents. Male drivers are proven to experience higher severity levels than female drivers.  Added to that, accidents occurring in rural areas increase the likelihood of serious injuries by 136.28 % but have no significant effect on other injury severity levels. Another finding of the study is that one-way streets increase the probability of fatal injury by 94.54%. For the second model which is the pedestrian, results suggest that the degree of injury severity of elderly pedestrians is higher than that of younger pedestrians. Another significant factor contributing to the injury severity model is darkness. Indeed, the risk of fatal injury increases by 110% at night as compared to day time. Another important factor is speed as pedestrians have been found to be seriously injured when they are involved in accidents caused by high-speed limit. Besides, the crashes involving pedestrian victims and which occur in rural areas or in a small town have a higher proportion of fatal injury with 61.22% and 67.91% respectively as compared to urban area. The factors identified in this research are expected to help developing potential countermeasures to reduce the severity and the number of single vehicle crashes.