Estimation of annual mileage budgets for a multiple discrete-continuous choice model of household vehicle ownership and utilization

This paper presents an empirical comparison of the following approaches to estimate annual mileage budgets for multiple discrete-continuous extreme value (MDCEV) models of household vehicle ownership and utilization: (a) a log-linear regression approach to model observed total annual household vehicle miles traveled (AH-VMT), (b) a stochastic frontier regression approach to model latent annual vehicle mileage frontier, and (c) other approaches used in the literature to assume annual household vehicle mileage budgets. For the stochastic regression approach, MDCEV and multiple discrete-continuous heteroscedastic extreme value (MDCHEV) models were estimated and examined. When model predictions were compared with observed distributions of vehicle ownership and utilization in a validation data sample, the log-linear regression approach performed better than other approaches. However, policy simulations demonstrate that the log-linear regression approach does not allow for AH-VMT to increase or decrease as a result of changes in vehicle-specific attributes, such as changes in fuel economy. The stochastic frontier approach overcomes that limitation. Policy simulation results with the stochastic frontier approach suggest that increasing the fuel economy of a category of vehicles increases the ownership and use of those vehicles. But this does not necessarily translate into an equal decrease in the use of other household vehicles, confirming previous findings in the literature that improvements in fuel economy tend to induce additional travel. In view of policy responsiveness and prediction accuracy, using the stochastic frontier regression (for estimating mileage budgets) in conjunction with the MDCHEV model for discrete-continuous choice analysis of household vehicle ownership and utilization is recommended.