Abstract
Modelling the mode choice behaviours of travellers is a key to design effective transport management policies, particularly in shifting travellers to public transport. Abundant studies have analysed the impact of level of services on mode choice preferences through its Random Utility Maximization (RUM), but the possibility of minimalize the regret have been overlooked. This paper will discusses the possibility of using generalised Random Regret Minimization (G-RRM) model on choosing transportation modes. The study is performed in two cities for comparison: Jogjakarta in Indonesia and Matsuyama in Japan. A stated preference (SP) survey isconducted, in which respondents choose Bike or Bus under hypothetical situations. As the result of RUM revealed that travellers prefer the transportation mode with more ensuring level of service. While an empirical proof of concept, the G-RRM model is estimated on a stated mode choice dataset, and its outcomes are compared with RUM and RRM counterparts.
Highlights
Many modern cities are facing traffic problems due to rapid urbanisation
The regret of alternative i is described by the sum of binary regrets where alternative i is compared to every other alternative in the choice task on each attribute
RRi: denotes the random regret associated with a considered alternative i Ri : denotes the ‘observed’ regret associated with i εi : denotes the ‘unobserved’ regret associated with i βm : denotes the estimable parameter associated with attribute Xm Xim, Xjm : denote the values associated with attribute Xm for, respectively, the consideredalternative i and another alternative j
Summary
Many modern cities are facing traffic problems due to rapid urbanisation. Increasing population induces increasing number of mobility demand, which results in problems in transportation. Growing economies have enabled the residents to own private vehicles, such as motorbikes and cars This change makes the individuals more dependent on the private mode of travel and causes serious traffic jam on a daily basis. To the best of authors’ knowledge, Chorus, et al [6] has been empirically comparedbetween the application of Random Utility Maximization and Random Regret Minimization on consumer choice modelling. They identified that the RUM and RRM perform well on some aspects, but there are possibility of compromising effect on using RRM the study presents promising findings, it does not quantitatively evaluate the impact of using RRM while modelling the consumer choices
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