Abstract
This study identifies predictable factors affecting crash severity on rural highways using ordered and multinomial logit models. Crash data are integrated with traffic observations from loop detectors and the models are evaluated for short-term severity prediction on the Khorasan Razavi rural-road network. The multinomial model provides a slightly better fit. Heavy-vehicle flow and speeds above 85 km/h are significant in the ordered model. In the multinomial model, minimum headway is also significant for crashes with serious damage, alongside heavy-vehicle flow and high speed for injury and fatality outcomes.
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