Analysis of naturalistic driving data Prospective view on methodological paradigms

Venkataraman Shankar, Paul P. Jovanis, Jonathan Aguero-Valverde, Frank Gross

Research output: Contribution to journalArticle

26 Scopus citations

Abstract

Recently completed naturalistic (i.e., unobtrusive) driving studies provide safety researchers with an unprecedented opportunity to study and analyze the occurrence of crashes and a range of near-crash events. Rather than focus on the details of the events immediately before the crash, this study seeks to identify methodological paradigms that can be used to answer questions long of interest to safety researchers. In particular, an attempt is made to shed some light on the four important components of methodological paradigms for naturalistic driving analysis: surrogates, evaluative aspects related to model structures, interpretation of driving context, and assessment of risk and associated sampling issues. The methodological paradigms are founded on a formal definition of the attributes of a valid crash surrogate that can be used in model formulation and testing. After a brief summary of the type of data collected in the studies, an overall framework for the analysis and a range of specific models to test hypotheses of interest are presented, A summary is given of how the systematic analyses with statistical models can extend safety knowledge beyond an assessment of "causes" of individual crashes.

Original languageEnglish (US)
Pages (from-to)1-8
Number of pages8
JournalTransportation Research Record
Issue number2061
DOIs
StatePublished - Dec 1 2008

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Mechanical Engineering

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