Analysis of Types of Crashes at Signalized Intersections by Using Complete Crash Data and Tree-Based Regression

Abstract

Many studies have shown that intersections are among the most dangerous locations of a roadway network. Therefore, there is a need to understand the factors that contribute to traffic crashes at such locations. One approach is to classify intersections and quantify the effects that configuration, geometric characteristics, and traffic volume have on the number of crashes at signalized intersections. This paper addresses the different factors that affect crashes, by type of collision, at signalized intersections. It also looks into the quality and completeness of the crash data and the effect that incomplete data have on the final results. Data from multiple sources were cross-checked to ensure the completeness of all crashes, including minor crashes that were usually unreported or were not coded into crash databases. The tree-based regression methodology was adopted in this study to cope with multicollinearity between variables, missing observations, and the fact that the true model form was unknown. The results showed a significant discrepancy in the factors that were found to affect the different collision types and their influence in each model. The two most significant differences in comparison with the total crash model as a base case were found to be in the models of head-on and left-turn crashes. The results also showed that the important factors were relatively consistent for rear-end, right-turn, and sideswipe crashes when minor crashes were considered. However, angle and head-on crashes showed significant changes in the model structure when minor crashes were added to the data set because these types of crashes were less stable. Finally, different roadway characteristics were correlated with different types of crashes.