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

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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 ...

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