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

Despite the importance of drivers’ actions and behaviors, the underlying factors to those actions have not received adequate attention. Understanding the factors contributing to various drivers’ actions before crashes could help policy makers to take appropriate actions to tackle those behaviors before crashes occur. One of the first steps could be to identify contributing factors to drivers’ actions by using a reliable statistical technique. It is reasonable to assume that drivers vary in their decision-making processes. Thus, 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, was considered. 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 the 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 the factors found to impact drivers’ choices. The results suggest that while the majority of attributes are processed according to the RUM, a significant portion of attributes are processed by the RRM. The hybrid model provides a richer understanding regarding factors to drivers’ actions before crashes based on different paradigms.

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