Development of a crash risk prediction model using the k-Nearest Neighbor algorithm

Abstract

This study aims to create a crash risk prediction model using k-Nearest Neighbor, one of the machine learning algorithms. Based on the traffic flow information collected by an advanced traffic management system (ATMS) and the corresponding crash historical information and weather information, this model derives the probability of a crash occurrence by looking for the most similar conditions at the time of a past accident. The predicted results of the model were evaluated using the metrics of the receiver operating characteristic (ROC) curve and area under the curve (AUC), which indicated that model performance belongs to the good side. The results of this study are expected to upgrade the safety management system of the ATMS further and contribute to reducing crash occurrence by giving preemptive notification to drivers.