Modeling Individual Preferences for Ownership and Sharing of Autonomous Vehicle Technologies

DOI Web Site 被引用文献2件
  • Patrícia S. Lavieri
    Department of Civil, Architectural, and Environmental Engineering, University of Texas at Austin, Ernest Cockrell Jr. Hall, Room 6.810, Stop C1761, 301 East Dean Keeton Street, Austin, TX 78712
  • Venu M. Garikapati
    School of Sustainable Engineering and the Built Environment, Ira A. Fulton Schools of Engineering, Arizona State University, 660 South College Avenue, Tempe, AZ 85281
  • Chandra R. Bhat
    Department of Civil, Architectural, and Environmental Engineering, University of Texas at Austin, Ernest Cockrell Jr. Hall, Room 6.810, Stop C1761, 301 East Dean Keeton Street, Austin, TX 78712
  • Ram M. Pendyala
    School of Sustainable Engineering and the Built Environment, Ira A. Fulton Schools of Engineering, Arizona State University, 660 South College Avenue, Tempe, AZ 85281
  • Sebastian Astroza
    Department of Civil, Architectural, and Environmental Engineering, University of Texas at Austin, Ernest Cockrell Jr. Hall, Room 6.810, Stop C1761, 301 East Dean Keeton Street, Austin, TX 78712
  • Felipe F. Dias
    Department of Civil, Architectural, and Environmental Engineering, University of Texas at Austin, Ernest Cockrell Jr. Hall, Room 6.810, Stop C1761, 301 East Dean Keeton Street, Austin, TX 78712

抄録

<jats:p> Considerable interest exists in modeling and forecasting the effects of autonomous vehicles on travel behavior and transportation network performance. In an autonomous vehicle (AV) future, individuals may privately own such vehicles, use mobility-on-demand services provided by transportation network companies that operate shared AV fleets, or adopt a combination of those two options. This paper presents a comprehensive model system of AV adoption and use. A generalized, heterogeneous data model system was estimated with data collected as part of the Puget Sound, Washington, Regional Travel Study. The results showed that lifestyle factors play an important role in shaping AV usage. Younger, urban residents who are more educated and technologically savvy are more likely to be early adopters of AV technologies than are older, suburban and rural individuals, a fact that favors a sharing-based service model over private ownership. Models such as the one presented in this paper can be used to predict the adoption of AV technologies, and such predictions will, in turn, help forecast the effects of AVs under alternative future scenarios. </jats:p>

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