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

Sigal Kaplan, Carlo Giacomo Prato

    Research output: Contribution to conferencePaperResearchpeer-review

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    Abstract

    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.
    Original languageEnglish
    Publication date2012
    Number of pages17
    Publication statusPublished - 2012
    Event12th Conference of the Pan American Society of Transportation Research - Santiago, Chile
    Duration: 24 Sep 201227 Sep 2012
    Conference number: 12

    Conference

    Conference12th Conference of the Pan American Society of Transportation Research
    Number12
    Country/TerritoryChile
    CitySantiago
    Period24/09/201227/09/2012

    Keywords

    • Random Regret Minimization
    • Crash avoidance maneuvers
    • Traffic safety

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