In-Depth Understanding of Near-Crash Events Through Pattern Recognition

Abstract

Studying near-crashes can help safety researchers understand the nature of crashes from different perspectives. Conventional crash data sets lack information about what occurred directly before the crash event. This study used a near-crash data set extracted from a naturalistic driving study that includes features describing the vehicles, drivers, and information on other vehicles involved before and during the near-crash incidents. This data set provides us with a unique perspective to understand the patterns of near-crashes. This study applied the cluster correspondence analysis (cluster CA) algorithm to explore the patterns and the magnitude of each feature’s dominance within and between the clusters through dimension reduction. The analysis identifies six clusters with four types of near-crashes: near-crash with adjacent vehicles; near-crash with the following or leading vehicles; near-crash with turning vehicles; and near-crash with objects on the roadway. The results also show that the first two types of near-crash are the most common. The patterns for these two most common types of near-crash are different with or without the engagement of secondary tasks. The findings of this study provide a fresh perspective to understand different types of crash and associated patterns. Furthermore, these findings could help transportation agencies or vehicle designers develop a more effective countermeasure to mitigate the risk of collision.