Abstract
This study focused on the characteristics of crashes reported on gravel roads, with the objective of identifying factors affecting severity of crashes on such roads. Crash data from Kansas over a 10 year period was used in the analysis. Logistic regression models were developed to estimate the probability of having a crash of different level of severity for a given set of explanatory variables. The regression modeling considered 29 candidate variables related to driver, road, environment, and collision type, which have been recorded by the police. It was found that multiple factors were very significant in these models, such as safety equipment usage, driver ejection, alcohol involvement, speed limit, and some driver-related factors. Existence of these factors was very likely to result in high-severity crashes on gravel roads, compared to the circumstances without them. The magnitude of such contributing effects was also estimated by computing the conditional odds ratios for individual predictors.
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