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.
BhatC. R.A New Generalized Heterogeneous Data Model (GHDM) to Jointly Model Mixed Types of Dependent Variables. Transportation Research Part B: Methodological, Vol. 79, 2015, pp. 50–77. https://doi.org/10.1016/j.trb.2015.05.017.
5.
TientrakoolP., HoY. C., and MaxemchukN. F.. Highway Capacity Benefits from Using Vehicle-To-Vehicle Communication and Sensors for Collision Avoidance. In Proceedings of the Vehicular Technology Conference (VTC Fall), 2011, pp. 1–5. https://doi.org/10.1109/VETECF.2011.6093130.
6.
GucwaM.Mobility and Energy Impacts of Automated Cars. In Proceedings of the Automated Vehicles Symposium, San Francisco, Calif., 2014.
7.
ZhangW., GuhathakurtaS., FangJ., and ZhangG.. Exploring the Impact of Shared Autonomous Vehicles on Urban Parking Demand: An Agent-Based Simulation Approach. Sustainable Cities and Society, Vol. 19, 2015, pp. 34–45. https://doi.org/10.1016/j.scs.2015.07.006.
8.
SpieserK., TreleavenK., ZhangR., FrazzoliE., MortonD., and PavoneM.. Toward a Systematic Approach to the Design and Evaluation of Automated Mobility-on-Demand Systems: A Case Study in Singapore. In Road Vehicle Automation, Springer International Publishing, 2014, pp. 229–245. https://doi.org/10.1007/978-3-319-05990-7_20.
MartinE., and ShaheenS.. The Impact of Carsharing on Public Transit and Non-Motorized Travel: An Exploration of North American Carsharing Survey Data. Energies, Vol. 4, No. 12, 2011, pp. 2094–2114. https://doi.org/10.3390/en4112094.
11.
FirnkornJ., and MüllerM.. What Will Be the Environmental Effects of New Free-Floating Car-Sharing Systems? The Case of car2go in Ulm. Ecological Economics, Vol. 70, No. 8, 2011, pp. 1519–1528. https://doi.org/10.1016/j.ecolecon.2011.03.014.
12.
KyriakidisM., HappeeR., and De WinterJ. C. F.. Public Opinion on Automated Driving: Results of an International Questionnaire Among 5000 Respondents. Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 32, 2015, pp. 127–140. https://doi.org/10.1016/j.trf.2015.04.014.
13.
BansalP., KockelmanK. M., and SinghA.. Assessing Public Opinions of and Interest in New Vehicle Technologies: An Austin Perspective. Transportation Research Part C: Emerging Technologies, Vol. 67, 2016, pp. 1–14. https://doi.org/10.1016/j.trc.2016.01.019.
14.
HowardD., and DaiD.. Public Perceptions of Self-Driving Cars: The Case of Berkeley, California. Presented at 93rd Annual Meeting of the Transportation Research Board, Washington, D.C., 2014.
15.
SchaefersT.Exploring Carsharing Usage Motives: A Hierarchical Means-End Chain Analysis. Transportation Research Part A: Policy and Practice, Vol. 47, 2013, pp. 69–77. https://doi.org/10.1016/j.tra.2012.10.024.
16.
de Almeida CorreiaG. H., and van AremB.. Solving the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP): A Model to Explore the Impacts of Self-Driving Vehicles on Urban Mobility. Transportation Research Part B: Methodological, Vol. 87, 2016, pp. 64–88. https://doi.org/10.1016/j.trb.2016.03.002.
17.
WolfA., and SeebauerS.. Technology Adoption of Electric Bicycles: A Survey Among Early Adopters. Transportation Research Part A: Policy and Practice, Vol. 69, 2014, pp. 196–211. https://doi.org/10.1016/j.tra.2014.08.007.
18.
SeebauerS., StolzR., and BergerM.. Technophilia as a Driver for Using Advanced Traveler Information Systems. Transportation Research Part C: Emerging Technologies, Vol. 60, 2015, pp. 498–510. https://doi.org/10.1016/j.trc.2015.10.009.
19.
BhatC. R., and DubeyS. K.. A New Estimation Approach to Integrate Latent Psychological Constructs in Choice Modeling. Transportation Research Part B: Methodological, Vol. 67, 2014, pp. 68–85. https://doi.org/10.1016/j.trb.2014.04.011.
20.
BhatC. R.A Comprehensive Dwelling Unit Choice Model Accommodating Psychological Constructs Within a Search Strategy for Consideration Set Formation. Transportation Research Part B: Methodological, Vol. 79, 2015, pp. 161–188. https://doi.org/10.1016/j.trb.2015.05.021.
21.
GarikapatiV. M., PendyalaR. M., MorrisE. A., MokhtarianP. L., and McDonaldN.. Activity Patterns, Time Use, and Travel of Millennials: A Generation in Transition?Transport Reviews, Vol. 36, No. 5, 2016, pp. 558–584. https://doi.org/10.1080/01441647.2016.1197337.
22.
BhatC. R., AstrozaS., BhatA. C., and NagelK.. Incorporating a Multiple Discrete-Continuous Outcome in the Generalized Heterogeneous Data Model: Application to Residential Self-Selection Effects Analysis in an Activity Time-Use Behavior Model. Transportation Research Part B: Methodological, Vol. 91, 2016, pp. 52–76. https://doi.org/10.1016/j.trb.2016.03.007.
23.
Morahan-MartinJ., and SchumacherP.. Attitudinal and Experiential Predictors of Technological Expertise. Computers in Human Behavior, Vol. 23, No. 5, 2007, pp. 2230–2239. https://doi.org/10.1016/j.chb.2006.03.003.
24.
ClewlowR. R.Carsharing and Sustainable Travel Behavior: Results from the San Francisco Bay Area. Transport Policy, Vol. 51, 2016, pp. 158–164. https://doi.org/10.1016/j.tranpol.2016.01.013.
