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