Predicting the severity of pedestrian crashes on highway-rail grade crossings

The purpose of this paper is to develop a nominal response multinomial logit (MNL) model to explore the impact of various explanatory variables on three different severity levels of pedestrian-rail crashes on highway-rail grade crossings (HRGCs) in the United States. Pedestrian-rail crash data on USDOT public crossing sites from 2005 to 2012 are used in this study. A multinomial logit model is developed using SAS PROC LOGISTICS procedure and marginal effects are also calculated. The MNL model results indicate that as the speed of train increases, the probability of resulting in fatal crashes also increases. Crashes occurring under cloudy weather conditions, on unpaved highways and in city, are more likely to result in more severe crashes (particularly fatal injury) as compared to clear weather conditions, paved conditions, and near city, respectively. Results also suggest that pedestrian-rail crashes occurring on concrete, and concrete & rubber crossing surfaces are found to be more likely to result in injury and fatal crashes. It is also shown that compared to low temperature (less than 50oF), pedestrian-rail crashes occurring at high temperature (greater than 80oF) are less likely to be injury and fatal crashes.