Real-Time Highway Traffic Accident Prediction Based on the k-Nearest Neighbor Method

The occurrence of a highway traffic accident is associated with the short-term turbulence of traffic flow. In this paper, we investigate how to identify the traffic accident potential by using the k-nearest neighbor method with real-time traffic data. This is the first time the k-nearest neighbor method is applied in real-time highway traffic accident prediction. Traffic accident precursors and their calculation time slice duration are determined before classifying traffic patterns. The experimental results show the k-nearest neighbor method outperforming the conventional C-means clustering method.