Analyzing Severity of Vehicle Crashes at Highway-Rail Grade Crossings: Multinomial Logit Modeling
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The purpose of this paper is to develop a nominal response multinomial logit model (MNLM)
to identify factors that are important in making an injury severity difference and to explore the
impact of such explanatory variables on three different severity levels of vehicle-related crashes at
highway-rail grade crossings (HRGCs) in the United States. Vehicle-rail and pedestrian-rail crash
data on USDOT highway-rail crossing inventory and 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 MNLM results indicate that when rail equipment
with high speed struck a vehicle, the chance of a fatality resulting increased. The study also reveals
that vehicle pick-up trucks, concrete, and rubber surfaces were more likely to be involved in more
severe crashes. On the other hand, truck-trailer vehicles in snow and foggy weather conditions,
development area types (residential, commercial, industrial, and institutional), and higher daily
traffic volumes were more likely to be involved in less severe crashes. Educating and equipping
drivers with good driving habits and short-term law enforcement actions, can potentially minimize
the chance of severe vehicle crashes at HRGCs.