Preferences for shared automated vehicles: A hybrid latent class modeling approach

The authors model the mode choice between three novel emerging transportation services and identify latent market segments not studied yet in the context of shared automated vehicles, including ridesharing, car sharing and automated transit, using a hybrid choice model. The authors use a discrete distribution to capture taste heterogeneity of distinct latent classes. Latent variables, socio-demographics and travel habits inform latent class assignment estimated simultaneously with a discrete choice kernel. Respondents chose their preferred mode for going to work in a set of stated preference choice tasks, based on the attributes of their current commutes using a Bayesian D-Efficient design. Users were segmented into two latent classes based on latent factors that capture time style orientation and public transit dislike. The effects of seating designation, not modeled previously in this context, trip cost and travel times in a shared ride, were estimated for the two classes. Users who neither like transit nor ridesharing with strangers are less likely to choose a shared ride if their designated seat is the middle seat, and overall less likely to choose automated transit. Individuals who have more organized time styles demonstrate higher marginal sensitivity to travel times and costs and are more likely to choose automated transit. Value of time analysis reveals that wait time of services that offer a convenient home pickup is valued lower than in-vehicle time. The implications for future adoption of shared automated vehicles is further discussed.

• Record URL:
  https://doi.org/10.1016/j.trc.2021.103013
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X21000450
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Etzioni, Shelly
  • Daziano, Ricardo A
  • Ben-Elia, Eran
  • Shiftan, Yoram
• Publication Date: 2021-4

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 125
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Bayes' theorem; Commuters; Costs; Mode choice; Ridesharing; Seats; Stated preferences; Travel time; Value of time; Vehicle sharing
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Public Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 01770435
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 26 2021 9:31AM