Choice Behavior Analysis for Shared Autonomous Vehicles: A Latent Class Approach

Automation technology and the sharing economy have brought changes to transportation, such as the appearance of shared autonomous vehicles (SAVs). This paper proposes a mode choice model combining the latent class model (LCM) and discrete choice model (DCM), to analyze choice behavior for SAVs, and identify factors that influence the preference for SAVs. A stated preference survey was conducted to obtain data of four aspects. Considering the first three aspects of data as manifest variables, respondents are classified into four classes by LCM. The utility is formulated for these four classes and calibrated by multinomial logit (MNL) and mixed logit (MIXL) model. Estimation results show that the proposed latent class approach performs better than the traditional MNL model in explanation ability, and influencing factors for each class are different. Results imply that the preference for SAVs differs across classes, and the preference for various modes differs across modes.

• Record URL:
  • https://doi.org/10.1061/9780784483053.333
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784483053
• Supplemental Notes:
  • © 2020 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Yao, Rong-Han
  • Long, Meng
  • Qi, Wen-Yan
• Conference:
  • 20th COTA International Conference of Transportation Professionals
  • Location: Xi’an , China
  • Date: 2020-8-14 to 2020-8-16
• Publication Date: 2020

Language

• English

Media Info

• Media Type: Web
• Pagination: pp 3987-3998
• Monograph Title: CICTP 2020: Transportation Evolution Impacting Future Mobility

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Choice models; Consumer behavior; Consumer preferences; Data analysis; Mode choice; Multinomial logits; Shared mobility; Surveys; Variables
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01768425
• Record Type: Publication
• ISBN: 9780784483053
• Files: TRIS, ASCE
• Created Date: Mar 26 2021 5:47PM