Information processing constraints in travel behaviour modelling: A generative learning approach
This work addresses travel behavior modeling for transportation researchers, but it is incremental as it applies an existing theoretical framework to a specific domain.
The paper tackled the problem of modeling travel behavior under uncertainty and information processing constraints by proposing a generative learning approach based on rational inattention theory, resulting in a model that shows strong correlation with the theory and can be generalized into an entropy-utility multinomial logit model.
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.