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Since often encountered the missing prediction of travel mode choice 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 travel mode and to increase the model fit. Three variances of RRM were implemented in this study: Classical RRM, µRRM, and PRRM. Meanwhile, Yogyakarta and Palembang were chosen as a case of the study by involving 708 respondents in both cities. A stated preference survey was carried out by offering six scenarios to the respondents. We implement 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 elasticity of travel time and travel cost. The result shows that by excluding the rho square, RRM outperforms RUM in both cities. The µRRM 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 µRRM in Palembang. We also found that RRM tends to generate a higher VTSS, travel time and cost elasticity than RUM. Travellers in both cities also tend to be more sensitive to change in travel time than travel cost.