Defining, Screening, and Validating Crash Surrogate Events Using Naturalistic Driving Data

Naturalistic driving studies provide an excellent opportunity to better understand crash causality and to supplement crash observations with a much larger number of near crash events. The goal of this research is the development of a rigorous set of diagnostic procedures to identify and validate useful crash and near crash events that can be used in enhanced safety analyses. As such, the research seeks to apply statistical methods as part of the methodology. A way to better understand crash occurrence and identify potential countermeasures to improve safety is to learn from and use near-crash events, particularly those near-crashes that have a common etiology to crash outcomes. This paper demonstrates that a multi-stage modeling framework can make the analysis of naturalistic driving data tractable. The procedure is tested using data from the Virginia Tech Transportation Institute (VTTI) 100-car study for road departure events. A total of 51 non-intersections and 12 intersection-related events are included in an application of the framework. While the sample sizes are limited in this empirical study, the authors believe the procedure is ready for testing in other applications.

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