An empirical discourse on forecasting the use of autonomous vehicles using consumers’ preferences

Given many known and unknown uncertainties, it is hard to forecast reliably the mode choices, expected to prevail with autonomous vehicle (AV) technology; however, the key to getting some idea lies in understanding the preferences of end users. In this vein, a random parameters logit model is employed to study the consumers’ preferences in small- and medium-sized metropolitan areas, based on their travel behavior and household characteristics, socio-demographic features, awareness about AV technology and new travel choices, psychological factors, and built environment features. Most of the past studies hypothesize that due to a wide range of anticipated benefits, there would be increased use of AVs especially as a shared service where multiple travelers use the same AV concomitantly. However, the results from this study do not support the hypothesis that vehicle ownership will be an obsolete model at least during the early phase of transitioning to the self-driving era (when roads are expected to contain vehicles with and without human drivers). The findings of this study reveal key factors influencing consumer preferences and offer important insights to technology developers and service providers in understanding the ways consumers would like to use this technology and hence, defining the business model.

• Record URL:
  https://doi.org/10.1016/j.techfore.2020.120130
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0040162520309562
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00401625
• Supplemental Notes:
  • © 2020 Elsevier Inc. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Saeed, Tariq Usman
  • Burris, Mark W
  • Labi, Samuel
  • Sinha, Kumares C
• Publication Date: 2020-9

Language

• English

Media Info

• Media Type: Web
• Features: References; Tables;
• Pagination: 120130
• Serial:
  • Technological Forecasting and Social Change
  • Volume: 158
  • Issue Number: 0
  • Publisher: Elsevier Science Publishing Company, Incorporated
  • ISSN: 0040-1625
  • Serial URL: http://www.sciencedirect.com/science/journal/00401625

Subject/Index Terms

• TRT Terms: Automobile ownership; Autonomous vehicles; Commuters; Consumer preferences; Forecasting; Residential location
• Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01917393
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 30 2024 3:18PM