Fusing Repeated Cross-Sectional Revealed Preference Datasets based on Rational Inattention Theory: Accounting for Changing Modal Preferences

Abstract

To address the methodological limitation of cross-sectional studies and the data constraints of longitudinal/panel studies, this paper presents a model-based method to fuse repeated cross-sectional travel survey data based on the theory of rational inattention (RI) in discrete choice modeling. In the proposed framework, older cross-sectional data are used to model the prior probability of choice alternatives, and more recent cross-sectional data are used to capture conditional heterogeneous choices. The fusion method is theoretically more robust and computationally less burdensome than existing data pooling techniques. The method is empirically tested using data from two cycles of a large-sample post-secondary student travel survey in the Greater Toronto and Hamilton Area to investigate the commuting mode choices of post-secondary students. Parameter estimates of the RI-based multinomial logit (MNL) model indicate that the proposed method can generate behaviorally consistent results. Validation of the estimated model using a holdout sample indicates its improved forecasting performance compared with the classical random utility maximizing MNL model. The fusion method can be extended to more than two cycles of repeated cross-sectional data by updating the prior probabilities whenever new cross-sectional data become available. Thus, the study presents a continuous framework for fusing information from multiple time points using repeated cross-sectional datasets to capture preference evolution better and enhance the forecasting robustness of discrete choice models.