Individuals’ Attitudes of Acceptance to Carsharing Mode: A Web-Based Survey in China

In this study, individuals’ attitudes of acceptance to carsharing were measured from three aspects, namely: carsharing mode choice behavior, highest acceptable price for using carsharing, and willingness to forgo car purchases. The data were collected by a web-based survey. The hierarchical tree-based regression (HTBR) method was applied to explore the effects of potential influencing factors, and some interesting findings were obtained: participants knowing carsharing were more likely to use carsharing, pay higher price and forgo car purchases; the most competitive trip purpose and trip distance for choosing carsharing were respectively running errands and 11- 20 km; most of participants (47.1%) were willing to pay 1-2 Yuan per minute for using carsharing; when car purchase restrain policy (CPRP) was carried out in a city or the urban public transport service level (UPTSL) was high, participants were more willing to give up buying new cars. Based on above findings, corresponding policies were proposed to provide guidance for successful establishment of carsharing

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AP020 Standing Committee on Emerging and Innovative Public Transport and Technologies. Alternate title: Individuals’ Attitudes of Acceptance to Carsharing Mode: Web-Based Survey in China
  • Corporate Authors:

    Transportation Research Board

    500 Fifth Street, NW
    Washington, DC  United States  20001
  • Authors:
    • Wang, Yun
    • Yan, Xuedong
    • Zhou, Yu
    • Xue, Qingwan
  • Conference:
  • Date: 2017

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 25p
  • Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

Filing Info

  • Accession Number: 01624563
  • Record Type: Publication
  • Report/Paper Numbers: 17-02315
  • Files: PRP, TRIS, TRB, ATRI
  • Created Date: Jan 30 2017 4:14PM