Information processing constraints in travel behaviour modelling: A generative learning approach

TL;DR

This paper introduces a data-driven generative model based on rational inattention theory to better understand how travelers process information and make decisions under uncertainty, capturing behavioral nuances often overlooked in traditional models.

Contribution

It presents a novel generative learning approach that models information processing constraints in travel behavior, linking behavioral theory with a practical econometric framework.

Findings

Strong correlation with rational inattention theory

Individuals rely on prior information under uncertainty

Model can be formulated as a generalized entropy utility model

Abstract

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.