Is Random Regret Minimization More Suitable in Predicting Mode Choice Decision for Indonesian Context than Random Utility Maximization?

Abstract

: Since often encountered the missing prediction by using the concept of random utility maximization (RUM) for Indonesian context, this study proposed a theory of random regret minimization (RRM) aiming to more precisely predict the chosen mode and to increase the model fit. Three variances of RRM were implemented: Classical RRM, µ RRM, and PRRM. Yogyakarta and Palembang were chosen as a case of the study by involv-ing 708 respondents. A stated preference survey was carried out by offering six scenarios to the respondents. We apply the value of final log-likelihood, rho-square, Akaike and Bayesian Information Criterion, and hit rate to compare the model fit. We also calculate the value of travel time saving, and the time and cost elasticity. The result shows that by excluding the rho square, RRM outperforms RUM in both cities. The µ RRM produces the best model fit in a case of travel mode choice in Yogyakarta, while there is a tendency that PRRM produces a better model fit than µ RRM in Palembang. We also found that RRM tends to generate a higher VTSS, time and cost elasticity than RUM. Travellers in both cities also tend to be more sensitive to change in travel time than travel cost.