The research on the effects of roadway geometry on truck crashes is relatively limited in comparison to predictive models developed for total vehicle crashes. The most common predictive models currently used are Poissonand negative binomial models. This study uses a negative binomial model but applies the Full Bayes methods for improving model performance. To successfully use Bayes methods, a learning process was used to develop a final model, which was then compared to a separate validation data set to verify its accuracy. The data set used for this study is based on rural two-lane collector and arterial horizontal curves in Ohio, comprised of 15,390observations from crash records between 2002 through 2006. Specific areas of interest within this study include the impact of shoulder width, horizontal curve radius, curve length and other traffic parameters. The final results indicated a significant increase in truck crashes due to both horizontal curvature and passenger vehicle volumes. The final modelÆs predictions were improved compared to the initial model, indicating that the learning process is a viable tool for future crash model development.
Abstract