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An Experimental Approach to Understanding the Impacts of Monitoring Methods on Use Intentions for Autonomous Vehicle Services: Survey Evidence from Japan

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  • Ryosuke Abe

    (Japan Transport and Tourism Research Institute, 3-18-19 Toranomon, Minato-ku, Tokyo 105-0001, Japan)

  • Yusuke Kita

    (School of Environment and Society, Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552, Japan y.kita@plan.cv.titech.ac.jp (Y.K.), fukuda@plan.cv.titech.ac.jp (D.F.))

  • Daisuke Fukuda

    (School of Environment and Society, Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552, Japan y.kita@plan.cv.titech.ac.jp (Y.K.), fukuda@plan.cv.titech.ac.jp (D.F.))

Abstract

Safety guidelines for autonomous vehicles (AVs) in many regions or countries require AV service providers to have the means to communicate with vehicles and the ability to stop them safely in case of emergencies. The transition to full deployment of AV services is dependent on more advanced monitoring methods. This study uses a survey of approximately 2000 residents of Japanese cities to investigate how monitoring methods affect their intentions to use these services. In particular, the survey is designed to understand how individuals react to unattended operations and remote monitoring in road passenger services including buses and taxis; the survey includes direct questions about intentions to use autonomous buses and taxis and a stated choice experiment based on the respondents’ preferences over their current mode of transportation and autonomous taxis. The results show that monitoring methods have mixed impacts. On one hand, monitoring could affect the general acceptance of AV services. The difference in the overall resistance to using these services is particularly large between the onboard human and remote monitoring options. Individuals tend to express stronger resistance to more advanced remote monitoring. On the other hand, the stated choice results show that the effects of these monitoring factors could be less significant in the actual settings of transportation mode choices; the effects of travel cost and time factors are likely to be more significant. These results suggest that when individuals consider AVs in the context of real-world decisions, their resistance to new technologies is diminished in comparison to their responses to abstract questions.

Suggested Citation

  • Ryosuke Abe & Yusuke Kita & Daisuke Fukuda, 2020. "An Experimental Approach to Understanding the Impacts of Monitoring Methods on Use Intentions for Autonomous Vehicle Services: Survey Evidence from Japan," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2157-:d:331075
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    References listed on IDEAS

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    1. Correia, Gonçalo Homem de Almeida & Looff, Erwin & van Cranenburgh, Sander & Snelder, Maaike & van Arem, Bart, 2019. "On the impact of vehicle automation on the value of travel time while performing work and leisure activities in a car: Theoretical insights and results from a stated preference survey," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 359-382.
    2. Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D., 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304, September.
    3. Yap, Menno D. & Correia, Gonçalo & van Arem, Bart, 2016. "Preferences of travellers for using automated vehicles as last mile public transport of multimodal train trips," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 1-16.
    4. Nielsen, Thomas Alexander Sick & Haustein, Sonja, 2018. "On sceptics and enthusiasts: What are the expectations towards self-driving cars?," Transport Policy, Elsevier, vol. 66(C), pages 49-55.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    6. Xiaoxia Dong & Matthew DiScenna & Erick Guerra, 2019. "Transit user perceptions of driverless buses," Transportation, Springer, vol. 46(1), pages 35-50, February.
    7. Kolarova, Viktoriya & Steck, Felix & Bahamonde-Birke, Francisco J., 2019. "Assessing the effect of autonomous driving on value of travel time savings: A comparison between current and future preferences," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 155-169.
    8. Aizaki, Hideo, 2012. "Basic Functions for Supporting an Implementation of Choice Experiments in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(c02).
    9. Salonen, Arto O., 2018. "Passenger's subjective traffic safety, in-vehicle security and emergency management in the driverless shuttle bus in Finland," Transport Policy, Elsevier, vol. 61(C), pages 106-110.
    10. Abe, Ryosuke, 2019. "Introducing autonomous buses and taxis: Quantifying the potential benefits in Japanese transportation systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 94-113.
    Full references (including those not matched with items on IDEAS)

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