Modeling consumers' likelihood to adopt autonomous vehicles based on their peer network

Adoption of connected and autonomous vehicles (CAVs) is viewed as one of the vital factors by public and private agencies as benefits are slowly getting quantified with further advancement in technology. From a wide variety of CAV perception and demand estimation studies, the literature lacks the impact of adoption based on an individual's social network and values. In this paper, the authors utilize an integrated choice and latent variable model to capture individuals' likelihood to adopt level 4 CAVs based on their social values in their peer network using an institutional survey dataset. The model results suggest that households with high income and frequent car buyers are more likely to adopt CAVs. CAV adoption will have a positive influence on an individual's social values among his peers. The proposed framework can be used to provide useful insights for policymakers to quantify consumers' preferences about CAV adoption based on their social values.

• Record URL:
  https://doi.org/10.1016/j.trd.2020.102509
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S1361920920306969
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13619209
• Supplemental Notes:
  • © 2020 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Sharma, Ishant
  • Mishra, Sabyasachee
• Publication Date: 2020-10

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Serial:
  • Transportation Research Part D: Transport and Environment
  • Volume: 87
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1361-9209
  • Serial URL: http://www.sciencedirect.com/science/journal/13619209

Subject/Index Terms

• TRT Terms: Automobile ownership; Autonomous vehicles; Connected vehicles; Consumer preferences; Factor analysis; Households; Income; Logits; Peer groups; Social factors; Structural equation modeling
• Subject Areas: Economics; Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01753890
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 30 2020 4:41PM