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A Vehicle Ownership and Utilization Choice Model with Edogenous Residential Density

  • Brownstone, David
  • Fang, Hao Audrey

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 which show that an increase in residential density has a negligible effect on car choice and utilization, but slightly reduces truck choice and utilization. We also perform an out-of-sample forecast using a holdout sample to test the robustness of the model.

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Paper provided by University of California Transportation Center in its series University of California Transportation Center, Working Papers with number qt2hc4h6h5.

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Date of creation: 01 Mar 2010
Date of revision:
Handle: RePEc:cdl:uctcwp:qt2hc4h6h5
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  1. Evans, William N & Oates, Wallace E & Schwab, Robert M, 1992. "Measuring Peer Group Effects: A Study of Teenage Behavior," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 966-91, October.
  2. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, September.
  3. Webb, Emily L. & Forster, Jonathan J., 2008. "Bayesian model determination for multivariate ordinal and binary data," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2632-2649, January.
  4. Brueckner, Jan K. & Largey, Ann G., 2008. "Social interaction and urban sprawl," Journal of Urban Economics, Elsevier, vol. 64(1), pages 18-34, July.
  5. Brownstone, David & Golob, Thomas F., 2009. "The impact of residential density on vehicle usage and energy consumption," Journal of Urban Economics, Elsevier, vol. 65(1), pages 91-98, January.
  6. Lave, Charles, 1994. "State and National VMT Estimates: It Ain't Necessarily So," University of California Transportation Center, Working Papers qt5527j8dj, University of California Transportation Center.
  7. Antonio M. Bento & Maureen L. Cropper & Ahmed Mushfiq Mobarak & Katja Vinha, 2005. "The Effects of Urban Spatial Structure on Travel Demand in the United States," The Review of Economics and Statistics, MIT Press, vol. 87(3), pages 466-478, August.
  8. Kai, Li, 1998. "Bayesian inference in a simultaneous equation model with limited dependent variables," Journal of Econometrics, Elsevier, vol. 85(2), pages 387-400, August.
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