Understanding preferences for Automated Mobility on Demand using a smartphone-based stated preference survey

Abstract

The urban mobility landscape is witnessing widespread changes with the emergence of several disruptive technologies including mobility-as-a-service and automated vehicles. The convergence of these two developments in the form of automated mobility-on-demand (AMoD) services (i.e., a system of shared driverless taxis) is receiving growing interest from industry, governments and researchers worldwide as a promising solution to meet mobility needs in the future in a sustainable manner. However, there is a large degree of uncertainty surrounding the potential adoption of these systems, and their impact on individual travel/activity patterns and the transportation system as a whole. In this context, this paper attempts to gain insights into behavioral preferences and attitudes towards AMoD through a novel context-aware app-based stated preferences survey conducted in Singapore. The SP survey leverages a state-of-the-art smartphone-based platform (Future Mobility Sensing) and its ability to collect revealed preference (RP) and contextual data. Logit mixture models accounting for inter-person heterogeneity and panel effects were estimated on a sample of 2500 observations from 350 respondents. The results indicate the presence of heterogeneity in the valuation of in-vehicle travel time and out-of-vehicle travel time and significant differences across demographic categories. An analysis of price elasticity of demand for AMoD indicates a higher elasticity for AMoD taxi followed by AMoD shared19 taxi and AMOD mini-bus. The importance of modeling inertia in switching from the currently used mode is also highlighted. The results have important policy implications and the models have applications within detailed activity-based microsimulation models to examine the impact of AMoD in future scenarios.