Estimating risk effects of driving distraction: A dynamic errorable car-following model

Jay Przybyla, Jeffrey Taylor, Jason Jupe, Xuesong Zhou

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


This paper aims to estimate the risk effects of distracted driving, by incorporating a dynamic, data-driven car-following model in an algorithmic framework. The model was developed to predict the situational risk associated with distracted driving. To obtain longitudinal driving patterns, this paper analyzed and synthesized the NGSIM naturalistic driver and traffic database, through a dynamic time warping algorithm, to identify essential driver behavior and characteristics. Cognitive psychology concepts, distracted driving simulator, and experimental data were adapted to examine the probabilistic nature of distracted driving due to internal vehicle distractions. An extended microscopic car-following model was developed and validated, which can be fully integrated with the naturalistic data and incorporate the probabilities of driver distraction.

Original languageEnglish (US)
Pages (from-to)117-129
Number of pages13
JournalTransportation Research Part C: Emerging Technologies
StatePublished - Jan 1 2015


  • Car-following model
  • Distracted driving
  • Driving safety

ASJC Scopus subject areas

  • Transportation
  • Automotive Engineering
  • Civil and Structural Engineering
  • Management Science and Operations Research


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