Revisiting the Benefits of Combining Data of a Different Nature: Strategic Forecasting of New Mode Alternatives

Abstract

We revisit the practice of combining revealed (RP) and stated preference (SP) data (i.e., the data enrichment, DE, paradigm) in discrete choice models using secondary data obtained from emerging sources; these facilitate access to massive information about travel choices and can be used to improve transport models. Even though the benefits of the DE paradigm have been known for years, there is a large gap between the state of practice and the state of the art, particularly in Global South countries (but also in many industrialized nations). We use a SP dataset considering two new transport alternatives (train and metro) and a RP dataset based on a large mobility survey in Bogotá, Colombia, complemented with fairly precise level-of-service data obtained using GIS utilities and the Distance Matrix API by Google. Our results allow us to discuss good practice, identify barriers and challenges to the paradigm’s application, and draw recommendations for forecasting the demand for new alternatives using joint RP and SP data.