A classi cation tree based modeling approach for segment related crashes on fi multilane highways

Introduction: This study presents a classification tree based alternative to crash frequency analysis for analyzing crashes on mid block segments of multilane arterials. Method: The traditional approach of modeling counts of crashes that occur over a period of time works well for intersection crashes where each intersection itself provides a well defined unit over which to aggregate the crash data. However, in the case of mid block segments the crash frequency based approach requires segmentation of the arterial corridor into segments of arbitrary lengths. In this study we have used random samples of time, day of week, and location (i.e., milepost) combinations and compared them with the sample of crashes from the same arterial corridor. For crash and non crash cases, geometric design/roadside and traffic characteristics were derived based on their milepost locations. The variables used in the analysis are non event specific and therefore more relevant for roadway safety feature improvement programs. First classification tree model is a model comparing all crashes with the non crash data and then four groups of crashes (rear end, lane change related, pedestrian, and single vehicle/ off road crashes) are separately compared to the non crash cases. The classification tree models provide a list of significant variables as well as a measure to classify crash from non crash cases. ADT along with time of day/ day of week are significantly related to all crash types with different groups of crashes being more likely to occur at different times. Conclusions: From the classification performance of different models it was apparent that using non event specific information may not be suitable for single vehicle/off road crashes. Impact on Industry: The study provides the safety analysis community an additional tool to assess safety without having to aggregate the corridor crash data over arbitrary segment lengths.