  Discrete choice models (DCM) are widely employed in travel demand analysis as
a powerful theoretical econometric framework for understanding and predicting
choice behaviors. DCMs are formed as random utility models (RUM), with their
key advantage of interpretability. However, a core requirement for the
estimation of these models is a priori specification of the associated utility
functions, making them sensitive to modelers' subjective beliefs. Recently,
machine learning (ML) approaches have emerged as a promising avenue for
learning unobserved non-linear relationships in DCMs. However, ML models are
considered "black box" and may not correspond with expected relationships. This
paper proposes a framework that expands the potential of data-driven approaches
for DCM by supporting the development of interpretable models that incorporate
domain knowledge and prior beliefs through constraints. The proposed framework
includes pseudo data samples that represent required relationships and a loss
function that measures their fulfillment, along with observed data, for model
training. The developed framework aims to improve model interpretability by
combining ML's specification flexibility with econometrics and interpretable
behavioral analysis. A case study demonstrates the potential of this framework
for discrete choice analysis.
