Future transport vision propensity segments: A latent class analysis of autonomous taxi market

Autonomous vehicle (AV) is considered a promising mode of transport that has been tested and deployed in various cities worldwide. However, research on how AVs will transform the field of transportation has been hindered by the heavy reliance on attitude measurements and the need for responses from the public who can better represent the future. This study investigates the autonomous taxi market in Huangpu District, Guangzhou, China, where paid and publicly available autonomous taxi service has been in operation for more than a year. This study analyzes 1,158 responses from pioneering and prospective users of autonomous taxi service. Latent class analysis is used to segment autonomous taxi users into classes to reveal the differences in their views on the potential uses of AVs. This study identifies three latent classes: neutral and diverse travelers, conservative and strict travelers, and open and enjoying travelers. Results show that sociodemographic characteristics, household characteristics, and current travel behavior affect the class membership in various means. In addition, users’ perceptions of the benefits and risks of autonomous driving vary across different latent classes. Findings from this study will help improve the quality of autonomous taxi service by enabling customized service based on mobility propensities. This study will also provide insights to help with future vehicle design and transportation policy making.

• Record URL:
  https://doi.org/10.1016/j.tra.2023.103699
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0965856423001192
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09658564
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Dai, Jingchen
  • 0000-0002-8135-8498
  • Wang, Xiaokun Cara
  • Ma, Wenxin
  • Li, Ruimin
  • 0000-0002-3405-1143
• Publication Date: 2023-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 103699
• Serial:
  • Transportation Research Part A: Policy and Practice
  • Volume: 173
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0965-8564
  • Serial URL: http://www.sciencedirect.com/science/journal/09658564

Subject/Index Terms

• TRT Terms: Attitudes; Autonomous vehicles; Markets; Revealed preferences; Taxicab drivers; Travelers
• Subject Areas: Economics; Highways; Passenger Transportation; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01890015
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 14 2023 8:55AM