Exploring Factors in a Crossroad Dataset Using Cluster-Based Association Rule Mining

Abstract

Investigating the contributory factors in crossroad accidents is a high-priority issue in the traffic safety analysis. This study exploits a method based on association rules to analyze these contributory factors. Using data about one year of crossroad traffic accidents in Isfahan, Iran, 63 and 156 association rules are generated for non-serious and serious accidents, respectively. The results show that both accident severity levels are associated with head-to-the-side collisions and the spring season. The frequency of non-serious accidents is about 38% higher than that of serious accidents. However, the association analysis results show that serious accidents are associated with more influencing factors than non-serious. Seat belt usage and road surface condition are additional decisive factors for serious accidents but not so for non-serious. The association analysis reveals that many influencing factors (such as traffic lights and the existence of a traffic enforcement camera) exhibit effects only under some specific circumstances (e.g., the peak of traffic).