Simplified, data-driven, errorable car-following model to predict the safety effects of distracted driving

J. Przybyla, J. Taylor, J. Jupe, X. Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

Abstract

An errorable car-following model is presented in this paper. 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 to identify essential driver behavior and characteristics. NGSIM data was modified according to data from cognitive psychology concepts to examine the probabilistic nature of distracted driving due to internal vehicle distractions. The errorable microscopic car-following model was developed and validated, which can be fully integrated with the naturalistic data and incorporate the probabilities of driver distraction. The proposed model predicts that distracted driving in congested conditions can result in crash rates 3.25 times that of normal driving conditions.

Original languageEnglish (US)
Title of host publication2012 15th International IEEE Conference on Intelligent Transportation Systems, ITSC 2012
Pages1149-1154
Number of pages6
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 15th International IEEE Conference on Intelligent Transportation Systems, ITSC 2012 - Anchorage, AK, United States
Duration: Sep 16 2012Sep 19 2012

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Other

Other2012 15th International IEEE Conference on Intelligent Transportation Systems, ITSC 2012
Country/TerritoryUnited States
CityAnchorage, AK
Period9/16/129/19/12

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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