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Modeling individuals’ preferences towards different levels of vehicle autonomy: A random parameter rank-ordered logit model

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  • Shahadat Hossain, Md
  • Rahman Fatmi, Mahmudur

Abstract

Vehicular automation is changing the conventional way of driving – e.g., the role of a driver is changing into an observer or a passenger due to the higher levels of automation. However, the success of this vehicle automation potentially leading to fully autonomous vehicles largely depends on the interest and comfort of consumers. Adoption of such disruptive technologies might have a retrospective aspect related to individuals’ experiences, such as exposure to technologies over their life course. This study adopts a life history-oriented approach to investigate individuals' preferences towards different levels of vehicle autonomy. The effects of historical experiences in different life domains such as technology usage in daily life and availability of vehicle technology, and the evolution of demographics such as historically living in an owned dwelling are explored. Data comes from a retrospective survey conducted in the Okanagan region of Canada. A random parameter rank-ordered logit model is developed to accommodate the relative ranked preferences of the alternatives and to capture unobserved heterogeneity. The model results suggest that individuals with historical exposure to vehicle technology have a higher likelihood of adopting higher levels of vehicle automation, whereas individuals having advanced technology only in the current vehicle reveal a lower interest towards higher vehicle autonomy. Individuals without a driver’s license show a higher interest in full automation, whereas they are less interested in no automation. The model confirms the existence of heterogeneity. For instance, pro-urban individuals prefer no automation, whereas pro-suburban individuals are inclined towards full automation. Both variables show significant heterogeneity with a large standard deviation. The findings of this study demonstrate the need to accommodate the effects of historical experiences while analyzing the preferences towards different levels of automation. Such inclusion of life history-oriented attributes within the travel demand forecasting models is expected to improve the demand and usage prediction accuracy of different levels of vehicle automation.

Suggested Citation

  • Shahadat Hossain, Md & Rahman Fatmi, Mahmudur, 2022. "Modeling individuals’ preferences towards different levels of vehicle autonomy: A random parameter rank-ordered logit model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 88-99.
    • Handle: RePEc:eee:transa:v:163:y:2022:i:c:p:88-99
    DOI: 10.1016/j.tra.2022.06.010
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    1. Macfarlane, Gregory S. & Garrow, Laurie A. & Mokhtarian, Patricia L., 2015. "The influences of past and present residential locations on vehicle ownership decisions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 74(C), pages 186-200.
    2. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    3. Fatmi, Mahmudur Rahman & Chowdhury, Subeh & Habib, Muhammad Ahsanul, 2017. "Life history-oriented residential location choice model: A stress-based two-tier panel modeling approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 104(C), pages 293-307.
    4. David Hensher & William Greene, 2003. "The Mixed Logit model: The state of practice," Transportation, Springer, vol. 30(2), pages 133-176, May.
    5. Abu Oakil & Dick Ettema & Theo Arentze & Harry Timmermans, 2014. "Changing household car ownership level and life cycle events: an action in anticipation or an action on occurrence," Transportation, Springer, vol. 41(4), pages 889-904, July.
    6. 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.
    7. Gopindra Sivakumar Nair & Sebastian Astroza & Chandra R. Bhat & Sara Khoeini & Ram M. Pendyala, 2018. "An application of a rank ordered probit modeling approach to understanding level of interest in autonomous vehicles," Transportation, Springer, vol. 45(6), pages 1623-1637, November.
    8. Layton, David F., 2000. "Random Coefficient Models for Stated Preference Surveys," Journal of Environmental Economics and Management, Elsevier, vol. 40(1), pages 21-36, July.
    9. Kim, Sung Hoo & Circella, Giovanni & Mokhtarian, Patricia L., 2019. "Identifying latent mode-use propensity segments in an all-AV era," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 192-207.
    10. Delbosc, Alexa & Nakanishi, Hitomi, 2017. "A life course perspective on the travel of Australian millennials," Transportation Research Part A: Policy and Practice, Elsevier, vol. 104(C), pages 319-336.
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