  Travel decisions tend to exhibit sensitivity to uncertainty and information
processing constraints. These behavioural conditions can be characterized by a
generative learning process. We propose a data-driven generative model version
of rational inattention theory to emulate these behavioural representations. We
outline the methodology of the generative model and the associated learning
process as well as provide an intuitive explanation of how this process
captures the value of prior information in the choice utility specification. We
demonstrate the effects of information heterogeneity on a travel choice,
analyze the econometric interpretation, and explore the properties of our
generative model. Our findings indicate a strong correlation with rational
inattention behaviour theory, which suggest that individuals may ignore certain
exogenous variables and rely on prior information for evaluating decisions
under uncertainty. Finally, the principles demonstrated in this study can be
formulated as a generalized entropy and utility based multinomial logit model.

Econometrics;Machine Learning