Exploring the feasibility of classification trees versus ordinal discrete choice models for analyzing crash severity

• Classification Trees compared with Discrete Choice Models to study crash severity.
• GUIDE Classification Tree is robust compared with Discrete Choice Models.
• Use of custom misclassification costs explored with mixed results.
• Median width, traffic volume, etc. affect crash severity.
• Multicollinearity and variable redundancy not an issue for GUIDE.

A cross-median crash (CMC) is one of the most severe types of crashes in which a vehicle crosses the median and sometimes collides with opposing traffic. A study of severity of CMCs in the state of Wisconsin was conducted by Lu et al. in 2010. Discrete choice models, namely ordinal logit and probit models were used to analyze factors related to the severity of CMCs. Separate models were developed for single and multi-vehicle CMCs. Although 25 different crash, roadway, and geometric variables were used, only 3 variables were found to be statistically significant which were alcohol usage, posted speed, and road conditions. The objective of this research was to explore the feasibility of GUIDE Classification Tree method to analyze the severity of CMCs to discover if any additional information could be revealed.A dataset of CMCs in the state of Wisconsin between 2001 and 2007, used in the study by Lu et al. was used to develop three different GUIDE Classification Trees. Additionally, the effects of variable types (continuous or discrete), misclassification costs, and tree pruning characteristics on models results were also explored. The results were directly compared with discrete choice models developed in the study by Lu et al. showing that the GUIDE Classification Trees revealed new variables (median width and traffic volume) that affect CMC severity and provided useful insight on the data. The results of this research suggest that the use of Classification Tree analysis should at least be considered in conjunction with regression-based crash models to better understand factors affecting crashes. Classification Tree models were able to reveal additional information about the dependent variable and offer advantages with respect to multicollinearity and variable redundancy issues.