AUTHOR=Rezapour Mahdi , Ksaibati Khaled 

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

JOURNAL=Frontiers in Built Environment

VOLUME=Volume 8 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2022.972253

DOI=10.3389/fbuil.2022.972253

ISSN=2297-3362

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