Context-dependent models (CRRM, MuRRM, PRRM, RAM) versus a context-free model (MNL) in transportation studies: a comprehensive comparisons for Swiss and German SP and RP data sets

ABSTRACT

The random regret minimization (RRM) model considers the relative performance of the alternatives and is therefore context-dependent. In RRM, an individual, when choosing between alternatives, is assumed to minimize anticipated regret as opposed to maximize his/her utility. There are three variants of RRM, the classical CRRM, the µRRM, and the P-RRM. There is also a further approach called relative advantage maximization (RAM). We compare multinomial logit with the four mentioned alternatives. We use stated choice data sets which include mode choice, location choice, parking choice, carpooling, car-sharing. We compare the performance of those five models by their model fit, values of travel time savings (VTTS), and elasticities. Looking at the model fit, RAM outperforms the other models in five cases, whereas the PRRM does so in two cases and µRRM only for one case. The VTTS and elasticities vary substantially which is relevant for cost–benefit analysis or simplified modeling approaches.

KEYWORDS:

• Context-dependent models
• random regret minimization
• RRM variants
• relative advantage maximization

Acknowledgements

We would like to thank Caspar Chorus, Sander Van Cranenburgh, and Michiel Bliemer for their suggestions regarding the RRM modeling. We are grateful for having a discussion with Michel Bierlaire, Claude Weiss, and Francesco Ciari regarding the seven data sets.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Prawira Fajarindra Belgiawan http://orcid.org/0000-0002-2017-787X

Ilka Dubernet http://orcid.org/0000-0003-4130-8303

Basil Schmid http://orcid.org/0000-0002-3310-9083

Kay Axhausen http://orcid.org/0000-0003-3331-1318