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

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.

► Anticipated regret minimization is assumed for choosing crash avoidance maneuvers.
► The random regret minimization (RRM) and the multinomial logit (MNL) models are compared.
► Compared to MNL, the predictive ability of the RRM model is superior.
► Compared to MNL, the predicted marginal effects of the RRM model are larger.