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A vehicle ownership and utilization choice model with endogenous residential density

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This paper explores the impact of residential density on households’ vehicle type and usage choices using the 2001 National Household Travel Survey (NHTS). Attempts to quantify the effect of urban form on households’ vehicle choice and utilization often encounter the problem of sample selectivity. Household characteristics that are unobservable to the researchers might determine simultaneously where to live, what vehicles to choose, and how much to drive them. Unless this simultaneity is modeled, any relationship between residential density and vehicle choice may be biased. This paper extends the Bayesian multivariate ordered probit and tobit model developed in Fang (2008) to treat local residential density as endogenous. The model includes equations for vehicle ownership and usage in terms of number of cars, number of trucks (vans, sports utility vehicles, and pickup trucks), miles traveled by cars, and miles traveled by trucks. We carry out policy simulations that show that an increase in residential density has a negligible effect on car choice and utilization, but slightly reduces truck choice and utilization. The largest impact we find is a -.4 arc elasticity of truck fuel use with respect to density. We also perform an out-of-sample forecast using a holdout sample to test the robustness of the model.

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  • Brownstone, David & Fang, Hao (Audrey), 2014. "A vehicle ownership and utilization choice model with endogenous residential density," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(2), pages 135-151.
  • Handle: RePEc:ris:jtralu:0133
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    1. Rith, Monorom & Fillone, Alexis & Biona, Jose Bienvenido M., 2019. "The impact of socioeconomic characteristics and land use patterns on household vehicle ownership and energy consumption in an urban area with insufficient public transport service – A case study of me," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    2. Zhang, Zhao & Jin, Wen & Jiang, Hai & Xie, Qianyan & Shen, Wei & Han, Weijian, 2017. "Modeling heterogeneous vehicle ownership in China: A case study based on the Chinese national survey," Transport Policy, Elsevier, vol. 54(C), pages 11-20.
    3. De Borger, Bruno & Mulalic, Ismir & Rouwendal, Jan, 2016. "Substitution between cars within the household," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 135-156.
    4. Leung, Kevin Y.K. & Astroza, Sebastian & Loo, Becky P.Y. & Bhat, Chandra R., 2019. "An environment-people interactions framework for analysing children's extra-curricular activities and active transport," Journal of Transport Geography, Elsevier, vol. 74(C), pages 341-358.
    5. Watanabe, Hajime & Maruyama, Takuya, 2024. "A Bayesian sample selection model with a binary outcome for handling residential self-selection in individual car ownership," Journal of choice modelling, Elsevier, vol. 51(C).
    6. Winston, Clifford & Yan, Jia, 2021. "Vehicle size choice and automobile externalities: A dynamic analysis," Journal of Econometrics, Elsevier, vol. 222(1), pages 196-218.
    7. Schmidt, Peter, 2020. "The effect of car sharing on car sales," International Journal of Industrial Organization, Elsevier, vol. 71(C).
    8. Bhat, Chandra R., 2015. "A comprehensive dwelling unit choice model accommodating psychological constructs within a search strategy for consideration set formation," Transportation Research Part B: Methodological, Elsevier, vol. 79(C), pages 161-188.
    9. Watanabe, Hajime & Maruyama, Takuya, 2023. "A Bayesian instrumental variable model for multinomial choice with correlated alternatives," Journal of choice modelling, Elsevier, vol. 46(C).
    10. Toshiyuki Yamamoto & Jean-Loup Madre & Matthieu Lapparent & Roger Collet, 2020. "A random heaping model of annual vehicle kilometres travelled considering heterogeneous approximation in reporting," Transportation, Springer, vol. 47(3), pages 1027-1045, June.
    11. Bhat, Chandra R. & Pinjari, Abdul R. & Dubey, Subodh K. & Hamdi, Amin S., 2016. "On accommodating spatial interactions in a Generalized Heterogeneous Data Model (GHDM) of mixed types of dependent variables," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 240-263.
    12. Bhat, Chandra R. & Astroza, Sebastian & Bhat, Aarti C. & Nagel, Kai, 2016. "Incorporating a multiple discrete-continuous outcome in the generalized heterogeneous data model: Application to residential self-selection effects analysis in an activity time-use behavior model," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 52-76.
    13. Manish Shirgaokar, 2016. "Expanding cities and vehicle use in India: Differing impacts of built environment factors on scooter and car use in Mumbai," Urban Studies, Urban Studies Journal Limited, vol. 53(15), pages 3296-3316, November.

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    More about this item

    Keywords

    vehicle type choice and utilization; endogenous density; ordered probit and tobit models.;
    All these keywords.

    JEL classification:

    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General

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