The application of the random regret minimization model to drivers’ choice of crash avoidance maneuvers

Publication: Research - peer-reviewJournal article – Annual report year: 2012

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Bibtex

@article{a4672afea0994169a1a37ad2833e5509,
title = "The application of the random regret minimization model to drivers’ choice of crash avoidance maneuvers",
publisher = "Pergamon",
author = "Sigal Kaplan and Prato, {Carlo Giacomo}",
year = "2012",
doi = "10.1016/j.trf.2012.06.005",
volume = "15",
pages = "699--709",
journal = "Transportation Research. Part F: Traffic Psychology and Behaviour",
issn = "1369-8478",

}

RIS

TY - JOUR

T1 - The application of the random regret minimization model to drivers’ choice of crash avoidance maneuvers

A1 - Kaplan,Sigal

A1 - Prato,Carlo Giacomo

AU - Kaplan,Sigal

AU - Prato,Carlo Giacomo

PB - Pergamon

PY - 2012

Y1 - 2012

N2 - This study explores the plausibility of regret minimization as behavioral paradigm underlying the choice of crash avoidance maneuvers. Alternatively to previous studies that considered utility maximization, this study applies the random regret minimization (RRM) model while assuming that drivers seek to minimize their anticipated regret from their corrective actions. The model accounts for driver attributes and behavior, critical events that made the crash imminent, vehicle and road characteristics, and environmental conditions. Analyzed data are retrieved from the General Estimates System (GES) crash database for the period between 2005 and 2009. The predictive ability of the RRM-based model is slightly superior to its RUM-based counterpart, namely the multinomial logit model (MNL) model. The marginal effects predicted by the RRM-based model are greater than those predicted by the RUM-based model, suggesting that both models should serve as a basis for evaluating crash scenarios and driver warning systems.

AB - This study explores the plausibility of regret minimization as behavioral paradigm underlying the choice of crash avoidance maneuvers. Alternatively to previous studies that considered utility maximization, this study applies the random regret minimization (RRM) model while assuming that drivers seek to minimize their anticipated regret from their corrective actions. The model accounts for driver attributes and behavior, critical events that made the crash imminent, vehicle and road characteristics, and environmental conditions. Analyzed data are retrieved from the General Estimates System (GES) crash database for the period between 2005 and 2009. The predictive ability of the RRM-based model is slightly superior to its RUM-based counterpart, namely the multinomial logit model (MNL) model. The marginal effects predicted by the RRM-based model are greater than those predicted by the RUM-based model, suggesting that both models should serve as a basis for evaluating crash scenarios and driver warning systems.

U2 - 10.1016/j.trf.2012.06.005

DO - 10.1016/j.trf.2012.06.005

JO - Transportation Research. Part F: Traffic Psychology and Behaviour

JF - Transportation Research. Part F: Traffic Psychology and Behaviour

SN - 1369-8478

VL - 15

SP - 699

EP - 709

ER -