Does the decision rule matter for large-scale transport models?

This paper is the first to study to what extent decision rules, embedded in disaggregate discrete choice models, matter for large-scale aggregate level mobility forecasts. Such large-scale forecasts are a crucial underpinning for many transport infrastructure investment decisions. We show, in the particular context of (linear-additive)...

Sampling of alternatives in random regret minimization models

Sampling of alternatives is often required in discrete choice models to reduce the computational burden and to avoid describing a large number of attributes. This approach has been used in many areas, including modeling of route choice, vehicle ownership, trip destination, residential location, and activity scheduling. The need for sampling...

Specification of regret-based models of choice behaviour: formal analyses and experimental design based evidence—commentary

A recent paper published in this journal compares two regret based choice models, and concludes that one of them is theoretically inferior and has a worse empirical performance in the context of a particular data set [Rasouli and Timmermans, Transportation 6:1–22, 2016]. Unfortunately, those conclusions are ill-founded as they are based on a...

Accounting for variation in choice set size in Random Regret Minimization models

This paper derives a trick to account for variation in choice set size in Random Regret Minimization (RRM) models. In many choice situations the choice set size varies across choice observations. As in RRM models regret level differences increase with increasing choice set size, not accounting for variation in choice set size results in RRM...

A Generalized Random Regret Minimization Model

This paper presents, discusses and tests a generalized Random Regret Minimization (G-RRM) model. The G-RRM model is created by replacing a fixed constant in the attribute-specific regret functions of the RRM model, by a regret-weight variable. Depending on the value of the regret-weights, the G-RRM model generates predictions that equal those of...

Random regret minimization: Exploration of a new choice model for environmental and resource economics

This paper introduces the discrete choice model-paradigm of Random Regret Minimization (RRM) to the field of environmental and resource economics. The RRM-approach has been very recently developed in the context of travel demand modelling and presents a tractable, regret-based alternative to the dominant choice-modelling paradigm based on Random...

A new model of Random Regret Minimization

A new choice model is derived, rooted in the framework of Random Regret Minimization (RRM). The proposed model postulates that when choosing, people anticipate and aim to minimize regret. Whereas previous regret-based discrete choice-models assume that regret is experienced with respect to only the best of foregone alternatives, the proposed...