Random regret minimization for analyzing driver actions, accounting for preference heterogeneity

Abstract

Increasingly more studies have implemented random regret minimization (RRM) as an alternative to random utility maximization (RUM) for modeling travelers’ choice-making behaviors. While for RUM, the focus is on utility maximization, for RRM the emphasis is on the regret of not selecting the best alternative. This study presented RRM and RUM for modeling actions made by drivers that resulted in crashes. The RRM method was considered in this study as the actions made before crashes might be the resultants of avoidance of regrets across the alternatives rather than the maximization of the utility related to the considered attributes. In addition, we extended the considered models to account for the unobserved heterogeneity in the datasets. Finally, we gave more flexibility to our model by changing the means of random parameters based on some observed attributes. This is one of the earliest studies, which considered the technique in the context of traffic safety for modeling drivers’ action while accounting for heterogeneity in the dataset by means of the random parameter. In addition, we considered the impact of inclusion of various predictors in the model fit of RRM and RUM. The results showed that while the standard RUM model outperforms the RRM model, the standard mixed models and the mixed models accounting for observed heterogeneity outperform the other techniques. As expected from the methodological structure of RRM, we found that the RRM performance is very sensitive to the included attributes. For instance, we found that by excluding the attributes of drivers’ condition and drivers under influence (DUI), the RRM model significantly outperforms the RUM model. The impact might be linked to the fact that when drivers are under abnormal conditions or influenced by drugs or alcohol, based on the sum of pairwise regret comparison, the inclusion of those attributes deteriorates the goodness-of-fit of the RRM model. It is possible that those parameters do not make a difference on regret pairwise comparison related to alternatives. The discussions at the end of this article examined possible reasons behind this performance.