Identification of crash hotspots using kernel density estimation and kriging methods: a comparison

Abstract

This paper presents a study aimed at comparing the outcome of two geostatistical-based approaches, namely kernel density estimation (KDE) and kriging, for identifying crash hotspots in a road network. Aiming at locating high-risk locations for potential intervention, hotspot identification is an integral component of any comprehensive road safety management programs. A case study was conducted with historical crash data collected between 2003 and 2007 in the Hennepin County of Minnesota, U.S. The two methods were evaluated on the basis of a prediction accuracy index (PAI) and a comparison in hotspot ranking. It was found that, based on the PAI measure, the kriging method outperformed the KDE method in its ability to detect hotspots, for all four tested groups of crash data with different times of day. Furthermore, the lists of hotspots identified by the two methods were found to be moderately different, indicating the importance of selecting the right geostatistical method for hotspot identification. Notwithstanding the fact that the comparison study presented herein is limited to one case study, the findings have shown the promising perspective of the kriging technique for road safety analysis.