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Transportation

, Volume 45, Issue 6, pp 1623–1637 | Cite as

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

  • Gopindra Sivakumar Nair
  • Sebastian Astroza
  • Chandra R. Bhat
  • Sara Khoeini
  • Ram M. Pendyala
Article
  • 77 Downloads

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.

Keywords

Rank ordered probit model Rank ordered data Travel demand modeling Autonomous vehicle adoption and usage 

Notes

Acknowledgements

This research was partially supported by the Center for Teaching Old Models New Tricks (TOMNET) (Grant No. 69A3551747116) as well as the Data-Supported Transportation Operations and Planning (D-STOP) Center (Grant No. DTRT13GUTC58), both of which are Tier 1 University Transportation Centers sponsored by the US Department of Transportation. The authors are grateful to Lisa Macias for her help in formatting this document. The authors thank four anonymous reviewers for their valuable comments and input that greatly improved the paper.

Author’s contributions

GSN: Literature review, model specification and estimation, coding. SA: Model specification and estimation, coding, manuscript editing. CRB: Conceptual development, methodology development, manuscript writing. SK: Manuscript review and editing, model interpretation. RMP: Manuscript writing, model specification development.

Compliance with ethical standards

Conflict of interest statement

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Gopindra Sivakumar Nair
    • 1
  • Sebastian Astroza
    • 1
    • 2
  • Chandra R. Bhat
    • 1
    • 3
  • Sara Khoeini
    • 4
  • Ram M. Pendyala
    • 4
  1. 1.Department of Civil, Architectural and Environmental EngineeringThe University of Texas at AustinAustinUSA
  2. 2.Departamento de Ingeniería IndustrialUniversidad de ConcepciónConcepciónChile
  3. 3.The Hong Kong Polytechnic UniversityKowloonHong Kong
  4. 4.School of Sustainable Engineering and the Built EnvironmentArizona State UniversityTempeUSA

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