Crash Injury Severity Analysis Using Bayesian Ordered Probit Models

Abstract

Understanding the underlying relationship between crash injury severity and factors such as driver’s characteristics, vehicle type, and roadway conditions is very important for improving traffic safety. Most previous studies on this topic used traditional statistical models such as ordered probit (OP), multinomial logit, and nested logit models. This research introduces the Bayesian inference and investigates the application of a Bayesian ordered probit (BOP) model in driver’s injury severity analysis. The OP and BOP models are compared based on datasets with different sample sizes from the 2003 National Automotive Sampling System General Estimates System (NASSGES). The comparison results show that these two types of models produce similar results for large sample data. When the sample data size is small, with proper prior setting, the BOP model can produce more reasonable parameter estimations and better prediction performance than the OP model. This research also shows that the BOP model provides a flexible framework that can combine information contained in the data with the prior knowledge of the parameters to improve model performance.