A discrete–continuous model of households’ vehicle choice and usage, with an application to the effects of residential density

Abstract This paper develops a new method to solve multivariate discrete–continuous problems and applies the model to measure the influence of residential density on households’ vehicle fuel efficiency and usage choices. Traditional discrete–continuous modelling of vehicle holding choice and vehicle usage becomes unwieldy with large numbers of vehicles and vehicle categories. I propose a more flexible method of modelling vehicle holdings in terms of number of vehicles in each category, using a Bayesian multivariate ordinal response system. I also combine the multivariate ordered equations with Tobit equations to jointly estimate vehicle type/usage demand in a reduced form, offering a simpler alternative to the traditional discrete/continuous analysis. Using the 2001 National Household Travel Survey data, I find that increasing residential density reduces households’ truck holdings and utilization in a statistically significant but economically insignificant way. The results are broadly consistent with those from a model derived from random utility maximization. The method developed above can be applied to other discrete–continuous problems.

[1]  S. Chib,et al.  Bayesian analysis of binary and polychotomous response data , 1993 .

[2]  J. Tobin.  Estimation of Relationships for Limited Dependent Variables , 1958 .

[3]  Jonathan J. Forster,et al.  Bayesian model determination for multivariate ordinal and binary data , 2008, Comput. Stat. Data Anal..

[4]  Peter E. Rossi,et al.  Bayesian Statistics and Marketing , 2005 .

[5]  Chandra R. Bhat,et al.  A multiple discrete–continuous extreme value model: formulation and application to discretionary time-use decisions , 2005 .

[6]  Dale J. Poirier,et al.  Intermediate Statistics and Econometrics: A Comparative Approach , 1995 .

[7]  Ye Feng,et al.  Vehicle choices, miles driven, and pollution policies , 2005 .

[8]  M. Cropper,et al.  The Effects of Urban Spatial Structure on Travel Demand in the United States , 2005, Review of Economics and Statistics.

[9]  Chandra R. Bhat,et al.  The Impact of Demographics, Built Environment Attributes, Vehicle Characteristics, and Gasoline Prices on Household Vehicle Holdings and Use , 2009 .

[10]  John Geweke,et al.  Efficient Simulation from the Multivariate Normal and Student-t Distributions Subject to Linear Constraints and the Evaluation of Constraint Probabilities , 1991 .

[11]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Peter E. Rossi,et al.  An exact likelihood analysis of the multinomial probit model , 1994 .

[13]  D. Brownstone,et al.  The Impact of Residential Density on Vehicle Usage and Energy Consumption , 2005 .

[14]  K. Small,et al.  Does Britain or the United States Have the Right Gasoline Tax , 2005 .

[15]  Adrian F. M. Smith,et al.  Sampling-Based Approaches to Calculating Marginal Densities , 1990 .

[16]  J. Brueckner.  Urban Sprawl: Lessons from Urban Economics , 2001 .

[17]  R. Cervero,et al.  TRAVEL DEMAND AND THE 3DS: DENSITY, DIVERSITY, AND DESIGN , 1997 .

[18]  Ming-Hui Chen,et al.  Reparameterizing the generalized linear model to accelerate gibbs sampler convergence , 1996 .

[19]  C. Bhat,et al.  Household Vehicle Type Holdings and Usage: An Application of the Multiple Discrete-Continuous Extreme Value (MDCEV) Model , 2006 .

[20]  S. Chib,et al.  Analysis of multivariate probit models , 1998 .

[21]  W. Hanemann.  Discrete-Continuous Models of Consumer Demand , 1984 .

[22]  B. Carlin,et al.  Bayesian Tobit Modeling of Longitudinal Ordinal Clinical Trial Compliance Data with Nonignorable Missingness , 1996 .

[23]  T. Amemiya.  Tobit models: A survey , 1984 .

[24]  F. Mannering,et al.  A DYNAMIC EMPIRICAL ANALYSIS OF HOUSEHOLD VEHICLE OWNERSHIP AND UTILIZATION , 1985 .

[25]  P. Goldberg,et al.  The Effects of the Corporate Average Fuel Efficiency Standards , 1996 .

[26]  M. Cropper,et al.  The Impact of Urban Spatial Structure on Travel Demand in the United States , 2003 .

[27]  D. Brownstone,et al.  The impact of residential density on vehicle usage and fuel consumption , 2010 .

[28]  Kenneth A. Small,et al.  EFFICIENT ESTIMATION OF NESTED LOGIT MODELS , 1985 .

[29]  C. Bhat.  The multiple discrete-continuous extreme value (MDCEV) model : Role of utility function parameters, identification considerations, and model extensions , 2008 .

[30]  Sivaramakrishnan Srinivasan,et al.  A multiple discrete-continuous model for independent- and joint-discretionary-activity participation decisions , 2006 .

[31]  Jeffrey A. Dubin,et al.  An Econometric Analysis of Residential Electric Appliance Holdings and Consumption , 1984 .

[32]  K. Train.  Qualitative Choice Analysis: Theory, Econometrics, and an Application to Automobile Demand , 1985 .

[33]  J. Brueckner,et al.  Social Interaction and Urban Sprawl , 2006, SSRN Electronic Journal.

[34]  R. Dunphy,et al.  TRANSPORTATION, CONGESTION, AND DENSITY: NEW INSIGHTS , 1996 .

[35]  John Rust,et al.  A nested logit model of automobile holdings for one vehicle households , 1985 .

[36]  Sarah E. West.  Distributional Effects of Alternative Vehicle Pollution Control Policies , 2004 .

[37]  Mike Wright,et al.  Qualitative Choice Analysis-Theory, Econometrics and an Application to Automobile Demand , 1987 .