Using naturalistic driving data to explore the association between traffic safety-related events and crash risk at driver level

Kun-Feng Wu*, Jonathan Aguero-Valverde, Paul P. Jovanis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

76 Scopus citations

Abstract

There has been considerable research conducted over the last 40 years using traffic safety-related events to support road safety analyses. Dating back to traffic conflict studies from the 1960s these observational studies of driver behavior have been criticized due to: poor quality data; lack of available and useful exposure measures linked to the observations; the incomparability of self-reported safety-related events; and, the difficulty in assessing culpability for safety-related events. This study seeks to explore the relationships between driver characteristics and traffic safety-related events, and between traffic safety-related events and crash involvement while mitigating some of those limitations. The Virginia Tech Transportation Institute 100-Car Naturalistic Driving Study dataset, in which the participants' vehicles were instrumented with various cameras and sensors during the study period, was used for this study. The study data set includes 90 drivers observed for 12-13 months driving. This study focuses on single vehicle run-off-road safety-related events only, including 14 crashes and 182 safety-related events (30 near crashes, and 152 crash-relevant incidents). Among the findings are: (1) drivers under age 25 are significantly more likely to be involved in safety-related events and crashes; and (2) significantly positive correlations exist between crashes, near crashes, and crash-relevant incidents. Although there is still much to learn about the factors affecting the positive correlation between safety-related events and crashes, a Bayesian multivariate Poisson log-normal model is shown to be useful to quantify the associations between safety-related events and crash risk while controlling for driver characteristics.

Original languageEnglish
Pages (from-to)210-218
Number of pages9
JournalAccident Analysis and Prevention
Volume72
DOIs
StatePublished - 1 Jan 2014

Keywords

  • Multivariate Poisson log-normal models
  • Naturalistic driving data
  • Safety-related events
  • Surrogate events

Fingerprint

Dive into the research topics of 'Using naturalistic driving data to explore the association between traffic safety-related events and crash risk at driver level'. Together they form a unique fingerprint.

Cite this