A methodology for prioritizing safety indicators using individual vehicle trajectory data

Abstract

A methodology for assessing crash risk using vehicle driving trajectories based on data mining techniques was developed in this study. A variety of safety indicators reflecting the characteristics of traffic and road geometric conditions were evaluated in terms of their capability of capturing hazardous traffic flow. Comprehensive data preparation was conducted by matching driving trajectory data obtained from in-vehicle digital tachograph devices and crash data to classify and analyze hazardous and normal traffic flows. The random forest approach was adopted to quantify the importance of safety indicators. The crash risks were evaluated using the logistic regression model and multivariate adaptive regression splines model based on the set of safety indicators with high importance. The results show that the dangerous driving events rate and driving volatility indicators were found to be particularly significant in identifying hazardous conditions. The multivariate adaptive regression splines model showed better performance and a classification accuracy of 86% was achieved. The proposed methodology will be useful for deriving effective countermeasures to prevent crashes, which is the backbone of proactive traffic safety management. © 2023 Taylor & Francis Group, LLC and The University of Tennessee.