Motor vehicle drivers' injuries in train-motor vehicle crashes.

The objectives of this research were to: (1) identify a more suitable model for modeling injury severity of motor vehicle drivers involved in train-motor vehicle crashes at highway-rail grade crossings from among three commonly used injury severity models and (2) to investigate factors associated with injury severity levels of motor vehicle drivers involved in train-motor vehicle crashes at such crossings. The 2009-2013 highway-rail grade crossing crash data and the national highway-rail crossing inventory data were combined to produce the analysis dataset. Four-year (2009-2012) data were used for model estimation while 2013 data were used for model validation. The three injury severity levels-fatal, injury and no injury-were based on the reported intensity of motor-vehicle drivers' injuries at highway-rail grade crossings. The three injury severity models evaluated were: ordered probit, multinomial logit and random parameter logit. A comparison of the three models based on different criteria showed that the random parameter logit model and multinomial logit model were more suitable for injury severity analysis of motor vehicle drivers involved in crashes at highway-rail grade crossings. Some of the factors that increased the likelihood of more severe crashes included higher train and vehicle speeds, freight trains, older drivers, and female drivers. Where feasible, reducing train and motor vehicle speeds and nighttime lighting may help reduce injury severities of motor vehicle drivers.