IDEAS home Printed from https://ideas.repec.org/p/cdl/itsrrp/qt9h2233g0.html
   My bibliography  Save this paper

Shared Automated Vehicles Could Greatly Benefit Visually Impaired Travelers if Designed and Operated with Their Needs in Mind

Author

Listed:
  • Wang, Peggy

Abstract

Automated vehicles (AVs) are one of the most significant technological advances in transportation. The benefits of AV technologies could be maximized by increasing vehicle occupancy through pooling and ridesharing, integrating AV use with high-capacity transit systems (e.g., using AVs to complement existing transit), and promoting multimodality (e.g., connecting travelers to public transit). Additionally, shared automated vehicles (SAVs), in which ridesharing companies (similar as today’s Uber or Lyft) offer driverless on-demand mobility services to customers, could enhance transportation access for visually impaired travelers who face unique challenges navigating current transportation systems including public transit and rideshare services. To this point, we interviewed 15 visually impaired individuals to understand their current transportation experience (e.g., what challenges they face and how they cope with these challenges); how SAVs might address their transportation needs and challenges; potential issues and solutions for using SAVs; how their travel behavior may change due to SAVs; and how much they would pay for SAV rides.

Suggested Citation

  • Wang, Peggy, 2025. "Shared Automated Vehicles Could Greatly Benefit Visually Impaired Travelers if Designed and Operated with Their Needs in Mind," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt9h2233g0, Institute of Transportation Studies, UC Berkeley.
    • Handle: RePEc:cdl:itsrrp:qt9h2233g0
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/9h2233g0.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cdl:itsrrp:qt9h2233g0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/itucbus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.