A vehicle ownership and utilization choice model with endogenous residential density

David Brownstone, Hao (Audrey) Fang

Abstract


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.

Keywords


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

Full Text:

PDF

References


Albert, J., Chib, S., 1993. Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association 88, 669–679.

Bento, A.M., Cropper, M.L., Mobarak, A.M., Vinha, K., 2005. The effect of urban spatial structure on travel demand in the United States. Review of Economics and Statistics 87(3), 466-478.

Bento, Antonio M., Lawrence H. Goulder, Mark R. Jacobsen, and Roger H. von Haefen. 2009. “Distributional and Efficiency Impacts of Increased US Gasoline Taxes.” American Economic Review 99 (3): 667–99.

Brownstone, D., Golob, T.F., 2009. The impact of residential density on vehicle usage and energy consumption. Journal of Urban Economics 65(1), 91-98.

Brueckner, J., Largey, A., 2008. Social interaction and urban sprawl. Journal of Urban Economics 64(1), 18-34.

Cervero, R., Kockelman, K., 1997. Travel demand and the 3Ds: density, diversity and design. Transportation Research Part D 3, 199-219.

Daly, A., Hess S. and de Jong, G., 2012. Calculating errors for measures derived from choice modelling estimates, Transportation Research Part B: Methodological, 46, 333-341.

Downs, A. 2004. Still stuck in traffic: coping with peak-hour traffic congestion, The Brookings Institution, Washington, D.C.

Dunphy, R., Fisher, K., 1996. Transportation, congestion, and density: new insights. Transportation Research Record 1552, 89-96.

Evans, W., Oates, W., Schwab, R., 1992. Measuring peer group effects: A study of teenage behavior. Journal of Political Economy 100, 966¨C991.

Ewing, R., Cervero, R., 2001. Travel and the built environment. Transportation Research Record, 1780, 87-114.

Fang, A., 2008. A discrete-continuous model of households' vehicle choice and usage, with an application to the effects of residential density. Transportation Research B, 42, 736-758

.

Koop, Gary, 2003. Bayesian Econometrics. John Wiley & Sons.

Lave, Charles, 1994. State and National VMT Estimates: It Ain't Necessarily So. University of California Transportation Working Paper accessed at http://escholarship.org/uc/item/5527j8dj.

Li, Kai, 1998. Bayesian inference in a simultaneous equation model with limited dependent variables. Journal of Econometrics 85, 387-400.

Nandram, B., Chen, M., 1996. Reparameterizing the generalized linear model to accelerate gibbs sampler convergence. Journal of Statistical Computation and Simulation 54, 129-144.

Train, K.E., 2003. Discrete choice methods with simulation. Cambridge, UK: Cambridge University Press.

Webb, E.L., Forster, J.J., 2008. Bayesian model determination for multivariate ordinal and binary data. Computational Statistics and Data Analysis, 52(5), 2632-2649.




DOI: http://dx.doi.org/10.5198/jtlu.v7i2.468


Copyright (c) 2014 Journal of Transport and Land Use