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Risk preference and adoption of autonomous vehicles

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  • Wang, Shenhao
  • Zhao, Jinhua

Abstract

Despite an increasingly large body of research that focuses on the potential demand for autonomous vehicles (AVs), risk preference is an understudied factor. Given that AV technology and how it will interact with the evolving mobility system are highly risky, this lack of research on risk preference is a critical gap in current understanding. By using a stated preference survey of 1142 individuals from Singapore, this study achieves three objectives. First, it develops one measure of psychometric risk preference and operationalizes prospect theory to create two economic risk preference parameters. Second, it examines how these psychometric and economic risk preferences are associated with socioeconomic variables. Third, it analyzes how risk preference influences the mode choice of AVs. The study finds that risk preference parameters are significantly associated with socioeconomic variables: the elderly, poor, females, and unemployed Singaporeans appear more risk-averse and tend to overestimate small probabilities of losses. Furthermore, all three risk preference parameters contribute to the prediction of AV adoption. These modeling results have policy implications at both the aggregate and disaggregate levels. At the aggregate level, people misperceive probabilities, are overall risk-averse, and hence under-consume AVs relative to the social optimum. At the disaggregate level, the elderly, poor, female, and unemployed are more risk-averse and thus are less likely to adopt AVs. These results suggest that it might be valuable for governments to implement policies to encourage technology adoption, particularly for disadvantaged social groups, although caution remains due to uncertainty in the long-term effects of AVs. Individualized risk preference parameters could also inform how to design regulations, safety standards, and liability allocations of AVs since many regulations are essentially mechanisms for risk allocation. One limitation of the paper is that risk preference is measured and modeled only as individual-specific but not alternative-specific variables. Future studies should examine the relationship between the multiple components of risk preference and the multiple risky aspects of AVs.

Suggested Citation

  • Wang, Shenhao & Zhao, Jinhua, 2019. "Risk preference and adoption of autonomous vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 215-229.
  • Handle: RePEc:eee:transa:v:126:y:2019:i:c:p:215-229
    DOI: 10.1016/j.tra.2019.06.007
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    as
    1. Lamotte, Raphaël & de Palma, André & Geroliminis, Nikolas, 2017. "On the use of reservation-based autonomous vehicles for demand management," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 205-227.
    2. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    3. Romani de Oliveira, Ítalo, 2017. "Analyzing the performance of distributed conflict resolution among autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 96(C), pages 92-112.
    4. Helga Fehr-Duda & Thomas Epper, 2012. "Probability and Risk: Foundations and Economic Implications of Probability-Dependent Risk Preferences," Annual Review of Economics, Annual Reviews, vol. 4(1), pages 567-593, July.
    5. Brigitte C. Madrian, 2014. "Applying Insights from Behavioral Economics to Policy Design," Annual Review of Economics, Annual Reviews, vol. 6(1), pages 663-688, August.
    6. Dhami, Sanjit, 2016. "The Foundations of Behavioral Economic Analysis," OUP Catalogue, Oxford University Press, number 9780198715535.
    7. Quang Nguyen & Colin Camerer & Tomomi Tanaka, 2010. "Risk and Time Preferences Linking Experimental and Household Data from Vietnam," Post-Print halshs-00547090, HAL.
    8. Cindy D. Kam, 2012. "Risk Attitudes and Political Participation," American Journal of Political Science, John Wiley & Sons, vol. 56(4), pages 817-836, October.
    9. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    10. Bansal, Prateek & Kockelman, Kara M., 2017. "Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 49-63.
    11. 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.
    12. Pascaline Dupas, 2014. "Short‐Run Subsidies and Long‐Run Adoption of New Health Products: Evidence From a Field Experiment," Econometrica, Econometric Society, vol. 82(1), pages 197-228, January.
    13. Wu, Xing & (Marco) Nie, Yu, 2011. "Modeling heterogeneous risk-taking behavior in route choice: A stochastic dominance approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(9), pages 896-915, November.
    14. Correia, Gonçalo Homem de Almeida & van Arem, Bart, 2016. "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, Elsevier, vol. 87(C), pages 64-88.
    15. Fagnant, Daniel J. & Kockelman, Kara, 2015. "Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 167-181.
    16. Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D. With contributions by-Name:Adamowicz,Wiktor, 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304, January.
    17. Elaine M. Liu, 2013. "Time to Change What to Sow: Risk Preferences and Technology Adoption Decisions of Cotton Farmers in China," The Review of Economics and Statistics, MIT Press, vol. 95(4), pages 1386-1403, October.
    18. Tomomi Tanaka & Colin F. Camerer & Quang Nguyen, 2010. "Risk and Time Preferences: Linking Experimental and Household Survey Data from Vietnam," American Economic Review, American Economic Association, vol. 100(1), pages 557-571, March.
    19. Andre Palma & Moshe Ben-Akiva & David Brownstone & Charles Holt & Thierry Magnac & Daniel McFadden & Peter Moffatt & Nathalie Picard & Kenneth Train & Peter Wakker & Joan Walker, 2008. "Risk, uncertainty and discrete choice models," Marketing Letters, Springer, vol. 19(3), pages 269-285, December.
      • André de Palma & Moshe Ben-Akiva & David Brownstone & Charles Holt & Thierry Magnac & Daniel McFadden & Peter Moffatt & Nathalie Picard & Kenneth Train & Peter Wakker & Joan Walker, 2008. "Risk, Uncertainty and Discrete Choice Models," THEMA Working Papers 2008-02, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    20. Chen, Zhibin & He, Fang & Yin, Yafeng & Du, Yuchuan, 2017. "Optimal design of autonomous vehicle zones in transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 44-61.
    21. 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.
    22. Drazen Prelec, 1998. "The Probability Weighting Function," Econometrica, Econometric Society, vol. 66(3), pages 497-528, May.
    23. Chen, Danjue & Ahn, Soyoung & Chitturi, Madhav & Noyce, David A., 2017. "Towards vehicle automation: Roadway capacity formulation for traffic mixed with regular and automated vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 100(C), pages 196-221.
    24. Colin F. Camerer & Howard Kunreuther, 1989. "Decision processes for low probability events: Policy implications," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 8(4), pages 565-592.
    25. Sunstein, Cass R, 2003. "Terrorism and Probability Neglect," Journal of Risk and Uncertainty, Springer, vol. 26(2-3), pages 121-136, March-May.
    26. Yonah Freemark & Anne Hudson & Jinhua Zhao, 2019. "Are Cities Prepared for Autonomous Vehicles?," Journal of the American Planning Association, Taylor & Francis Journals, vol. 85(2), pages 133-151, April.
    27. Hensher, David A. & Ho, Chinh & Knowles, Louise, 2016. "Efficient contracting and incentive agreements between regulators and bus operators: The influence of risk preferences of contracting agents on contract choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 87(C), pages 22-40.
    28. Elias, Wafa & Shiftan, Yoram, 2012. "The influence of individual’s risk perception and attitudes on travel behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(8), pages 1241-1251.
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