Development in discrete choice modelling has been dominated by Random Utility Maximization approaches due to their ease of application and high economic interpretability. However, this model assumes that decision-makers perform an in-depth information search process implicitly and instantaneously. It has not been investigated in detail whether the information search process (ISP) of transport users is in depth or breadth-first in a public transport choice context, a gap that this research aims to fill. To this end, the ISP of public transport users has been characterized in three stated preference surveys with click-tracking, which were pivoted concerning morning peak trips and varied in the number of areas of interest shown (AOI). These values were hidden and only one attribute at a time was made visible, and clicks were recorded to evaluate the respondents' ISP. Three main conclusions can be drawn from the findings. First, the pattern of searching for information in breadth-first predominates independently of the AOIs displayed. Second, more searches are performed than the amount of information displayed and this value increases at a decreasing rate with increasing AOIs. Third, the most likely transitions during the deliberation process are those that arise from breadth-first searches.@en

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 of alternatives is accentuated for random regret minimization (RRM) models because, unlike random utility models, the regret function for each alternative depends on all of the alternatives in the choice-set. In this paper we develop and test a method to achieve consistency, asymptotic normality, and relative efficiency of the estimators while sampling alternatives in a class of models that includes RRM. The proposed method can be seen as an extension of the approach used to address sampling of alternatives in multivariate extreme value models. We illustrate the methodology using Monte Carlo experimentation and a case study with real data. Experiments show that the proposed method is practical, performs better than a truncated model, and results in finite-sample estimates that provide a good approximation of those obtained with a model considering all of the alternatives.

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