Discrete choice analysis aims to understand and predict decision-makers’ behaviour, a goal that is crucial across several disciplines, including transportation. This type of analysis has relied predominantly on static representations of preferences, principally through the Random Utility Maximisation (RUM) model, due to its ease of implementation, economic interpretability, and statistical formality. However, this model assumes that individuals possess complete information about all attributes of alternatives and that they can process and recall this information instantaneously, which may not align with actual human behaviour. In contrast, the Decision Field Theory (DFT) model from mathematical psychology explicitly incorporates the repeated scrutiny of attributes and recall effects within the decision-making process, which enables it to model attention weights, but lacks microeconomic interpretability and clear statistical parameter identification. This paper introduces the RUM-DFT model, which seeks to integrate strengths of both approaches. Through Monte Carlo simulations, the proposed model is shown to be able to: (i) recover parameters related to the deliberation process, (ii) replicate the dynamic behaviour of utilities during deliberation as observed in practice, (iii) maintain economic interpretability by estimating coefficients that can be used to calculate the marginal indirect utilities, and (iv) highlight the pitfalls of using a RUM model that disregards the true dynamics of data generation process. The SwissMetro case study is employed also to evaluate the RUM-DFT model using a real-world dataset, demonstrating the viability and superior goodness-of-fit of the proposed model.

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.

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.