A Bayesian hierarchical approach to the joint modelling of Revealed and stated choices

Abstract

Revealed and stated choice data are fundamental inputs to understanding individuals’ preferences. Owning to the distinctive characteristics and complementary nature of these two types of data, making joint inference based on their combined information content represents an attractive approach to preference studies. However, complications may arise from the different decision protocols under the two distinct choice contexts. In this study, a Bayesian hierarchical model is proposed to make joint inference from combined RP and SP data, with special attention paid to capturing the behavioural differences between the two choice contexts. In addition to the well-recognised issues of decision inertia and scale differences, the proposed model also takes into account other behavioural characteristics such as a decision-maker ignoring situation constraints, non-attending attributes, and misinterpreting attributes. An empirical analysis of a combined RP and SP dataset of travel mode choices is used to demonstrate the advantageous features of the model. Upon examining the empirical evidence, two main advantages emerge: the model provides direct measures of the effect of behavioural issues arising from ignoring situation constraints and non-attending attributes, as well as evidence for the misinterpretation of attributes.