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The built environment and vehicle ownership modeling: Evidence from 32 diverse regions in the U.S

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  • Sabouri, Sadegh
  • Tian, Guang
  • Ewing, Reid
  • Park, Keunhyun
  • Greene, William

Abstract

•Four-step modeling is still the state-of-the-practice.•More than half of the surveyed MPOs do not model vehicle ownership.•Vehicle ownership increases with household size, income, and number of workers.•Vehicle ownership decreases with almost all of the built environmental variables.•Count regression models have better predictive accuracy than discrete choice models.

Suggested Citation

  • Sabouri, Sadegh & Tian, Guang & Ewing, Reid & Park, Keunhyun & Greene, William, 2021. "The built environment and vehicle ownership modeling: Evidence from 32 diverse regions in the U.S," Journal of Transport Geography, Elsevier, vol. 93(C).
    • Handle: RePEc:eee:jotrge:v:93:y:2021:i:c:s0966692321001265
    DOI: 10.1016/j.jtrangeo.2021.103073
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    References listed on IDEAS

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    3. Seung Jun Choi & Junfeng Jiao, 2024. "Uncovering electric vehicle ownership disparities using K-means clustering analysis: A case study of Austin, Texas," Journal of Computational Social Science, Springer, vol. 7(3), pages 2403-2456, December.
    4. Sangwan Lee, 2022. "Exploring Associations between Multimodality and Built Environment Characteristics in the U.S," Sustainability, MDPI, vol. 14(11), pages 1-16, May.
    5. Te Ma & Mahdi Aghaabbasi & Mujahid Ali & Rosilawati Zainol & Amin Jan & Abdeliazim Mustafa Mohamed & Abdullah Mohamed, 2022. "Nonlinear Relationships between Vehicle Ownership and Household Travel Characteristics and Built Environment Attributes in the US Using the XGBT Algorithm," Sustainability, MDPI, vol. 14(6), pages 1-18, March.
    6. Obregón-Biosca, Saúl A., 2022. "Choice of transport in urban and periurban zones in metropolitan area," Journal of Transport Geography, Elsevier, vol. 100(C).

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