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An application of a rank ordered probit modeling approach to understanding level of interest in autonomous vehicles

Author

Listed:
  • Gopindra Sivakumar Nair

    (The University of Texas at Austin)

  • Sebastian Astroza

    (The University of Texas at Austin
    Universidad de Concepción)

  • Chandra R. Bhat

    (The University of Texas at Austin
    The Hong Kong Polytechnic University)

  • Sara Khoeini

    (Arizona State University)

  • Ram M. Pendyala

    (Arizona State University)

Abstract

Surveys of behavior could benefit from information about people’s relative ranking of choice alternatives. Rank ordered data are often collected in stated preference surveys where respondents are asked to rank hypothetical alternatives (rather than choose a single alternative) to better understand their relative preferences. Despite the widespread interest in collecting data on and modeling people’s preferences for choice alternatives, rank-ordered data are rarely collected in travel surveys and very little progress has been made in the ability to rigorously model such data and obtain reliable parameter estimates. This paper presents a rank ordered probit modeling approach that overcomes limitations associated with prior approaches in analyzing rank ordered data. The efficacy of the rank ordered probit modeling methodology is demonstrated through an application of the model to understand preferences for alternative configurations of autonomous vehicles (AV) using the 2015 Puget Sound Regional Travel Study survey data set. The methodology offers behaviorally intuitive model results with a variety of socio-economic and demographic characteristics, including age, gender, household income, education, employment and household structure, significantly influencing preference for alternative configurations of AV adoption, ownership, and shared usage. The ability to estimate rank ordered probit models offers a pathway for better utilizing rank ordered data to understand preferences and recognize that choices may not be absolute in many instances.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:transp:v:45:y:2018:i:6:d:10.1007_s11116-018-9945-9
    DOI: 10.1007/s11116-018-9945-9
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    Cited by:

    1. Delle Site, Paolo & Kilani, Karim & Gatta, Valerio & Marcucci, Edoardo & de Palma, André, 2019. "Estimation of consistent Logit and Probit models using best, worst and best–worst choices," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 87-106.
    2. Mondal, Aupal & Bhat, Chandra R., 2022. "A spatial rank-ordered probit model with an application to travel mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 374-393.
    3. 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.
    4. Kailai Wang & Gulsah Akar, 2019. "Effects of neighborhood environments on perceived risk of self-driving: evidence from the 2015 and 2017 Puget Sound Travel Surveys," Transportation, Springer, vol. 46(6), pages 2117-2136, December.
    5. Asmussen, Katherine E. & Mondal, Aupal & Bhat, Chandra R., 2022. "Adoption of partially automated vehicle technology features and impacts on vehicle miles of travel (VMT)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 156-179.
    6. Saeed, Tariq Usman & Burris, Mark W. & Labi, Samuel & Sinha, Kumares C., 2020. "An empirical discourse on forecasting the use of autonomous vehicles using consumers’ preferences," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    7. Barbour, Natalia & Menon, Nikhil & Zhang, Yu & Mannering, Fred, 2019. "Shared automated vehicles: A statistical analysis of consumer use likelihoods and concerns," Transport Policy, Elsevier, vol. 80(C), pages 86-93.
    8. Kassens-Noor, Eva & Dake, Dana & Decaminada, Travis & Kotval-K, Zeenat & Qu, Teresa & Wilson, Mark & Pentland, Brian, 2020. "Sociomobility of the 21st century: Autonomous vehicles, planning, and the future city," Transport Policy, Elsevier, vol. 99(C), pages 329-335.
    9. Ishant Sharma & Sabyasachee Mishra, 2023. "Ranking preferences towards adopting autonomous vehicles based on peer inputs and advertisements," Transportation, Springer, vol. 50(6), pages 2139-2192, December.
    10. Rosell, Jordi & Allen, Jaime, 2020. "Test-riding the driverless bus: Determinants of satisfaction and reuse intention in eight test-track locations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 166-189.
    11. Paolo Delle Site & Karim Kilani & Valerio Gatta & Edoardo Marcucci & André de Palma, 2018. "Estimation of Logit and Probit models using best, worst and best-worst choices," Working Papers hal-01953581, HAL.
    12. Behnood, Ali & Haghani, Milad & Golafshani, Emadaldin Mohammadi, 2022. "Determinants of purchase likelihood for partially and fully automated vehicles: Insights from mixed logit model with heterogeneity in means and variances," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 119-139.
    13. Cynthia J. Mkong & Tahirou Abdoulaye & Paul Martin Dontsop-Nguezet & Zoumana Bamba & Victor Manyong & Godlove Shu, 2021. "Determinant of University Students’ Choices and Preferences of Agricultural Sub-Sector Engagement in Cameroon," Sustainability, MDPI, vol. 13(12), pages 1-18, June.
    14. Yue Ding & Ruimin Li & Xiaokun Wang & Joshua Schmid, 2022. "Heterogeneity of autonomous vehicle adoption behavior due to peer effects and prior-AV knowledge," Transportation, Springer, vol. 49(6), pages 1837-1860, December.

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