To Own or To Use – Shared Automated Vehicles

To Own or To Use - Shared Automated Vehicles

The future transportation system is anticipated to integrate automation and sharing of vehicles – potentially disrupting urban mobility systems. The long-term success of Shared Automated Vehicles (SAVs) depends on consumers’ affinity to use SAVs as well as their willingness to renounce existing vehicle(s) in the presence of SAVs. In this project, we examine the potential behavioral mechanisms influencing households’ willingness towards using SAVs and renouncing existing vehicle(s). By jointly considering the SAV use and vehicle ownership component, we will examine the characteristics of laggards, early adopters/early majority, and innovators.

By harnessing comprehensive revealed and stated preference behavioral data within the framework of heterogeneity-based discrete choice models, a broad spectrum of behavioral and demographic determinants of SAV adoption and vehicle relinquishment will be considered, including awareness and opinions about self-driving cars, sustainable travel behavior, vehicle ownership and ride hailing use, vehicle ownership status, selling intentions, vehicle fuel type, and sociodemographic factors.

By using behavioral and demographic indicators derived from a multi-method sampling approach, we expect the resulting analysis can assist in informing policy to promote use (including ownership) of SAVs. For example, findings can assist in targeting behavioral and demographic subgroups that are more likely to use SAVs and renounce existing vehicle(s). We also expect the findings to shed new light on the contrasting challenges in SAV deployment due to the supply and demand differentials between the use of SAVs and renouncement of existing vehicle(s).

Major Contributors:
Paolo Santi, Ph.D., MIT Senseable City Lab.
Carlo Ratti, Ph.D. MIT Senseable City Lab.

Publications:
Wali, B., Santi, P., Ratti, C. (2021). Are Californians Willing to Use Shared Automated Vehicles & Renounce Existing Vehicles? (Under-review).

Acknowledgements:
We would like to thank AMS Institute, Allianz, Anas S.p.A., Austrian Institute of Technology, Brose, Cisco, Dover Corporation, Ford, Fraunhofer Institute, KTH Royal Institute of Technology, Kuwait-MIT Center for Natural Resources, Lab Campus, Politecnico di Torino, RATP, SMART—Singapore-MIT Alliance for Research and Technology, SNCF Gares & Connexions, Teck, UBER, UTEC – Universidad de Ingeniería y Tecnología and all the members of the MIT Senseable City Lab Consortium for supporting this research.