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

Jay Przybyla; Jeffrey Taylor; Jason Jupe; Xuesong Zhou

doi:10.1016/j.trc.2014.07.013

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

Jay Przybyla, Jeffrey Taylor, Jason Jupe, Xuesong Zhou

Research output: Contribution to journal › Article › peer-review

36 Scopus citations

Abstract

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 language	English (US)
Pages (from-to)	117-129
Number of pages	13
Journal	Transportation Research Part C: Emerging Technologies
Volume	50
DOIs	https://doi.org/10.1016/j.trc.2014.07.013
State	Published - Jan 1 2015

Keywords

Car-following model
Distracted driving
Driving safety

ASJC Scopus subject areas

Civil and Structural Engineering
Automotive Engineering
Transportation
Computer Science Applications

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10.1016/j.trc.2014.07.013

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title = "Estimating risk effects of driving distraction: A dynamic errorable car-following model",

abstract = "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.",

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T2 - A dynamic errorable car-following model

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AU - Taylor, Jeffrey

AU - Jupe, Jason

AU - Zhou, Xuesong

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AB - 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.

KW - Car-following model

KW - Distracted driving

KW - Driving safety

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

Abstract

Keywords

ASJC Scopus subject areas

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