TY - JOUR
T1 - Modeling Individual Preferences for Ownership and Sharing of Autonomous Vehicle Technologies
AU - Lavieri, Patrícia S.
AU - Garikapati, Venu M.
AU - Bhat, Chandra R.
AU - Pendyala, Ram M.
AU - Astroza, Sebastian
AU - Dias, Felipe F.
N1 - Funding Information:
This research was partially supported by the U.S. Department of Transportation through the Data-Supported Transportation Operations and Planning Tier 1 University Transportation Center. P. S. Lavieri acknowledges funding support from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior and the Brazilian Government, and C.R. Bhat acknowledges support from a Humboldt Research Award from the Alexander von Humboldt Foundation, Bonn, Germany. The authors thank Neil Kilgren of the Puget Sound Regional Council for providing the survey data, Lisa Macias for her help in formatting the manuscript, and five anonymous referees who provided useful comments on an earlier version of the paper.
Publisher Copyright:
© 2017 National Academy of Sciences.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
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U2 - 10.3141/2665-01
DO - 10.3141/2665-01
M3 - Article
AN - SCOPUS:85070185416
SN - 0361-1981
VL - 2665
SP - 1
EP - 10
JO - Transportation Research Record
JF - Transportation Research Record
IS - 1
ER -