Rural non-interstate crashes induce a significant amount of severe injuries and fatalities. Examination of
such injury patterns and the associated contributing factors is of practical importance. Taking into account
the ordinal nature of injury severity levels and the hierarchical feature of crash data, this study employs a
hierarchical ordered logit modelto examine the significantfactors in predicting driver injury severities in
rural non-interstate crashes based on two-year New Mexico crash records. Bayesian inference is utilized
in model estimation procedure and 95% Bayesian Credible Interval (BCI) is applied to testing variable
significance. An ordinary ordered logit model omitting the between-crash variance effect is evaluated
as well for model performance comparison. Results indicate that the model employed in this study outperforms
ordinary ordered logit model in model fit and parameter estimation. Variables regarding crash
features, environment conditions, and driver and vehicle characteristics are found to have significant
influence on the predictions of driver injury severities in rural non-interstate crashes. Factors such as
road segments far from intersection, wet road surface condition, collision with animals, heavy vehicle
drivers, male drivers and driver seatbelt used tend to induce less severe driver injury outcomes than the
factors such as multiple-vehicle crashes, severe vehicle damage in a crash, motorcyclists, females, senior
drivers, driver with alcohol or drug impairment, and other major collision types. Research limitations
regarding crash data and model assumptions are also discussed. Overall, this research provides reasonable
results and insight in developing effective road safety measures for crash injury severity reduction
and prevention.