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Impacts of Autonomous Vehicles on Consumers Time-Use Patterns

Author

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  • Saptarshi Das

    (Golisano Institute for Sustainability, Rochester Institute of Technology, 190 Lomb Memorial Drive, Rochester, NY 14623, USA)

  • Ashok Sekar

    (LBJ School of Public Affairs, University of Texas at Austin, 2300 Red River Street, E-2700, Austin, TX 78712, USA)

  • Roger Chen

    (Golisano Institute for Sustainability, Rochester Institute of Technology, 190 Lomb Memorial Drive, Rochester, NY 14623, USA)

  • Hyung Chul Kim

    (Research and Advanced Engineering, Ford Motor Company, 2101 Village Road, Dearborn, MI 48121, USA)

  • Timothy J. Wallington

    (Research and Advanced Engineering, Ford Motor Company, 2101 Village Road, Dearborn, MI 48121, USA)

  • Eric Williams

    (Golisano Institute for Sustainability, Rochester Institute of Technology, 190 Lomb Memorial Drive, Rochester, NY 14623, USA)

Abstract

We use the American Time Use Survey (ATUS) to characterize how different consumers in the US might use Autonomous Vehicles (AVs). Our approach is to identify sub-groups of the population likely to benefit from AVs and compare their activity patterns with an otherwise similar group. The first subgroup is working individuals who drive to work with long total travel times. Auto-travelers in the top 20% of travel time number 19 million and travel 1.6 h more on a workday than those in the bottom 80%. For car-commuting professionals, the additional travel time of the long-traveling group comes from 30 min less work, 29 min less sleep, and 30 min less television watching per day. The second subgroup is working individuals with a long travel time and who take public transport. Long public transit riders show very similar differences in activity times as the driving subgroup. Work, sleep, and video functionalities of AVs are presumably in high demand by both groups. The third sub-group identified is elderly retired people. AVs enable mobility-restricted groups to travel more like those without restrictions. We compare two age groups, 60–75 years and >75 years old, the latter, on average, experiencing more mobility restrictions than their younger counterparts. The retired population older than 75 years numbers 16 million and travels 14 min less per day than retirees aged 60–75 years. The main activity change corresponding to this reduced travel is 7 min per day less shopping and 8 min per day less socializing. If older retired people use AVs to match the lifestyle of the 60–75 years old group, this would induce additional personal travel and retail sector demand. The economic, environmental and social implications of AV are very difficult to predict but expected to be transformative. The contribution of this work is that it utilizes time-use surveys to suggest how AV adoption could induce lifestyle changes inside and outside the vehicle.

Suggested Citation

  • Saptarshi Das & Ashok Sekar & Roger Chen & Hyung Chul Kim & Timothy J. Wallington & Eric Williams, 2017. "Impacts of Autonomous Vehicles on Consumers Time-Use Patterns," Challenges, MDPI, vol. 8(2), pages 1-15, December.
  • Handle: RePEc:gam:jchals:v:8:y:2017:i:2:p:32-:d:122720
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    References listed on IDEAS

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    Cited by:

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    2. Kassens-Noor, Eva & Cai, Meng & Kotval-Karamchandani, Zeenat & Decaminada, Travis, 2021. "Autonomous vehicles and mobility for people with special needs," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 385-397.
    3. Pudāne, Baiba, 2019. "Departure Time Choice and Bottleneck Congestion with Automated Vehicles: Role of On-board Activities," MPRA Paper 96328, University Library of Munich, Germany.
    4. George Xydis & Luca Pagliaricci & Živilė Paužaitė & Vygintas Grinis & Gyula Sallai & Peter Bakonyi & Radoslav Vician, 2021. "SMARTIES Project: The Survey of Needs for Municipalities and Trainers for Smart Cities," Challenges, MDPI, vol. 12(1), pages 1-10, May.
    5. Jamil Hamadneh & Domokos Esztergár-Kiss, 2021. "The Influence of Introducing Autonomous Vehicles on Conventional Transport Modes and Travel Time," Energies, MDPI, vol. 14(14), pages 1-28, July.
    6. Tamás Hegedűs & Dániel Fényes & Balázs Németh & Péter Gáspár, 2021. "Improving Sustainable Safe Transport via Automated Vehicle Control with Closed-Loop Matching," Sustainability, MDPI, vol. 13(20), pages 1-20, October.
    7. Zamparini, Luca & Vergori, Anna Serena, 2021. "Sustainable mobility at tourist destinations: The relevance of habits and the role of policies," Journal of Transport Geography, Elsevier, vol. 93(C).
    8. Gaurav Vyas & Pooneh Famili & Peter Vovsha & Daniel Fay & Ashish Kulshrestha & Greg Giaimo & Rebekah Anderson, 2019. "Incorporating features of autonomous vehicles in activity-based travel demand model for Columbus, OH," Transportation, Springer, vol. 46(6), pages 2081-2102, December.
    9. Eric Williams & Vivekananda Das & Andrew Fisher, 2020. "Assessing the Sustainability Implications of Autonomous Vehicles: Recommendations for Research Community Practice," Sustainability, MDPI, vol. 12(5), pages 1-13, March.
    10. Frank, Björn & Herbas-Torrico, Boris & Schvaneveldt, Shane J., 2021. "The AI-extended consumer: Technology, consumer, country differences in the formation of demand for AI-empowered consumer products," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
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