This paper investigates the impact of residential density on vehicle usage and fuel consumption. The empirical model accounts for both residential self-selection effects and non-random missing data problems. While most previous studies focus on a specific region, this paper analyzes national...

This research describes a new model of household vehicle use behavior by type of vehicle. Forecasts of future vehicle emissions, including potential gains that might be attributed to introductions of alternative-fuel (clean-fuel) vehicles, critically depend upon the ability to forecast...

Modern travel-behavior surveys have become quite complex; they frequently include multiple telephone contacts, travel diaries, and customized stated preference experiments. The complexity and length of these surveys lead to pervasive problems with missing data and non-random response biases....

This paper studies the effects of certain incentives designed to promote ridesharing on work trips to reduce congestion and air pollution. Ordered probit discrete choice models of commuters' mode choices (always rideshare, sometimes rideshare, and always drive alone) are estimated using a new...

This research describes a new model of household vehicle use behavior by type of vehicle. Forecasts of future vehicle emissions, including potential gains that might be attributed to introductions of alternative-fuel (clean-fuel) vehicles, critically depend upon the ability to forecast...

Rubin (1987) has proposed multiple imputations as a general method for estimation ion the presence of missing data. Rubin's results only strictly apply to Bayesian models, but Schenker and Welsh (1988) directly prove the consistency of multiple imputations inferences when there are missing...

The vehicle choice model developed here is one component in a micro-simulation demand forecasting system being designed to produce annual forecasts of new and used vehicle demand by vehicle type and geographic area in California. The system will also forecast annual vehicle miles traveled for...

This chapter demonstrates a new methodology for correcting panel data models for attrition bias. The method combines Rubin's Multiple Imputations technique with Manski and Lerman's Weighted Exogenous Sample Maximum Likelihood Estimator (WESMLE). Simple Hausman tests for the presence of attrition...

Recent survey validation studies suggest that measurement error in earnings data is pervasive and violates classical measurement error assumptions, and therefore may bias estimation of cross-section and longitudinal earnings models. We model the structure of earnins measurements error using data...

Recent survey validation studies suggest that measurement error in earnings data is pervasive and violates classical measurement error assumptions, and therefore may bias estimation of cross-section and longitudinal earnings models. We model the structure of earnings measurement error using data...