Crash Injury Severity Analysis at Signalized Intersections: Modeling Techniques and Significant Factors

Abstract

The purpose of this study is to examine crash injury severity. Ordered probability models with either logit or probit function is commonly applied in crash injury severity analyses; however, its critical assumption that the slope coefficients do not vary over different alternatives except the cut-off points is usually too restrictive. Partial proportional odds models are generalizations of ordered probability models, for which some of the beta coefficients can differ across alternatives. Left-turn crashes are the most frequently occurring collisions and they are prone to be severe. They were analyzed by partial proportional odds models. The results show that partial proportional odds model performs better than ordered probability model. Many variables in driver attributes, vehicular characteristics, roadway geometry design, environmental factors, and crash characteristics were identified. Specifically, the use of the partial proportional formulation allows a much better identification of the increasing effect of alcohol and/or drug use on crash injury severity, which previously was masked using the conventional ordered probability models.