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. @en

This paper presents a systematic way of understanding and modeling traveler behavior in response to on-demand mobility services. We explicitly consider the sequential and yet inter-connected decision-making stages specific to on-demand service usage. The framework includes a hybrid choice model for service subscription, and three logit mixture models with inter-consumer heterogeneity for the service access, menu product choice and opt-out choice. Different models are connected by feeding logsums. The proposed modeling framework is essential for accounting the impacts of real-time on-demand system’s dynamics on traveler behaviors and capturing consumer heterogeneity, thus being greatly relevant for integrations in multi-modal dynamic simulators. The methodology is applied to a case study of an innovative personalized on-demand real-time system which incentivizes travelers to select more sustainable travel options. The data for model estimation is collected through a smartphone-based context-aware stated preference survey. Through model estimation, lower values of time are observed when the respondents opt to use the reward system. The perception of incentives and schedule delay by different population segments are quantified. These results are fundamental in setting the ground for different behavioral scenarios of such a new on-demand system. The proposed methodology is flexible to be applied to model other on-demand mobility services such as ride-hailing services and the emerging mobility as a service.@en