Hybrid random utility-random regret model in the presence of preference heterogeneity, modeling drivers’ actions

Despite the importance of drivers’ actions and behaviors, underlying factors to those have not received adequate attention. Understanding contributing various before crashes could help policy makers take appropriate tackle behaviors occur. One first steps be identify by using a reliable statistical technique. It is reasonable assume that drivers vary in their decision-making processes. Thus, this study, addition random utility maximization (RUM), regret minimization (RRM), as psychological representation choice-making process, was considered. While most past studies, context traffic safety, focused on either RRM or RUM, both models’ frameworks hybrid models might needed account for heterogeneity behaviors. In addition, we accounted additional dimensions preference latent class (LC) model capture. The results showed significant improvement fit mixed LC compared with standard simple RUM models. emotional conditions drivers, distraction, environmental conditions, gender are some found impact choices. suggest while majority attributes processed according portion RRM. provides richer understanding regarding based different paradigms.