Estimating demand for new modes of transportation using a context-aware stated preference survey

Abstract

This thesis presents the design of a context-aware stated preference survey that will be used to estimate the demand for new transportation modes and services. It builds on the Future Mobility Survey, a smartphone-based prompted-recall survey that accurately gathers revealed preference information on respondents' travel patterns. By using this GPS data as the context for a hypothetical stated preference survey, we can present realistic travel scenarios to respondents that pivot off their actual behavior. The approach is the first of its kind to combine GPS and external data to generate hypothetical scenarios for a large number of modes. It does this by making use of freely available web services to gather information on travel times and distances on many modes, which then informs the presentation of these modes in the hypothetical scenario. The travel scenario is presented using a web interface that mimics trip-planning software, and the software can be readily applied across different cities and countries.