Proactive Detection of Crash Hotspots Using In-Vehicle Driving Recorder

Abstract

Crash hotspot detection is important to reduce traffic crashes by allowing effective deployment of countermeasures in those locations. However, current hotspot detection methods rely mostly on crash occurrences and, therefore, countermeasures can be implemented only after a number of crashes have been occurred. To prevent crashes prior to their actual occurrences, crash precedents, also known as surrogate safety measures, are required. In this regard, driving behavior is recognized as a reliable precedent of crash occurrence because it reflects how human drivers respond to their driving environments. Therefore, the objective of this study is to develop a proactive crash hotspot detection method by evaluating relation between crash and aggressive driving behavior that was extracted from 5 taxi drivers' driving records. The spatial correlation analysis results showed that there was a distinct positive correlation between crash and aggressive behavior occurrence as evident from high correlation coefficient (which is about 0. 863.) This finding implies that aggressive driving incidents that were suggested in this research could be considered as a surrogate safety measure for proactively detecting crash hotspots.