17 research outputs found
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Hazard Models of Changing Household Demographics
In this paper, I develop demographic models which can be used to simulation household changes resulting from marriage, divorce or separation, childbirth, children leaving home, cohabitation, extended families living together, death, and so forth. They are dynamic in nature, and are meant to be used within a larger microsimulation system. In fact, they can be used by any microsimulation system that models decision-making at the household level. They extend previous work in three ways: 1) by using continuos time hazard models, 2) by allowing for inter-dependencies across the various type of changes that a household may undergo, and 3) by including several important covariates. These covariates include age, gender, race, education, income, employment status, and indicators for previous demographic events (e.g. birth of a child out-of-wedlock and previous marriages). They provide insight into the demographic patterns across different socioeconomic groups
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On the Costs of Air Pollution from Motor Vehicles
Air pollution is frequently the stated reason for special measures aimed at controlling motor vehicles. In the United States, motor vehicle emission standards are set explicitly in clean air legislation, while policies at several levels of government are designed to reduce the use of cars for particular purposes like commuting. In Europe, high fuel taxes and subsidies to urban mass transit and intercity rail travel in large part aim to reduce car use
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On the Costs of Air Pollution from Motor Vehicles
Air pollution is frequently the stated reason for special measures aimed at controlling motor vehicles. In the United States, motor vehicle emission standards are set explicitly in clean air legislation, while policies at several levels of government are designed to reduce the use of cars for particular purposes like commuting. In Europe, high fuel taxes and subsidies to urban mass transit and intercity rail travel in large part aim to reduce car use
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Modeling Non-Ignorable Attrition and Measurement Error in Panel Surveys: An Application to Travel Demand Modeling
Modern panel surveys frequently suffer from high and non-ignorable attrition, and transportation surveys suffer from poor travel time estimates. The initial sampling process for most transportation surveys is also non-ignorable since rare travel modes are oversampled (and mode choice is the key dependent variable). This paper examines new multiple imputation methods for adjusting forecasts and model estimates to account for these problems in a new panel survey of 1500 commuters in San Diego, California. These data are collected to evaluate charging solo commuters to use an existing 8-mile underutilized freeway carpool lane. We illustrate the impact of attrition and measurement error on a standard conditional logit model of commuters' mode choice (solo drive in free lanes, pay to solo drive in the carpool lanes, or carpool for free in carpool lanes). Although the attrition rate between waves is 40% and non-ignorable, the quantitative impact on the results is negligible. However, measurement error in travel time does have an important impact on the key results from our model. Finally, failure to account for the measurement error process using multiple imputations yields a downward bias of at least 50% in the standard errors of the logit coefficient estimates
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Modeling non-ignorable attrition and measurement error in panel surveys: an application to travel demand modeling
Modern panel surveys frequently suffer from high and likely non-ignorable attrition, and transportation surveys suffer from poor travel time estimates. This paper examines new methods for adjusting forecasts and model estimates to account for these problems. The methods we describe are illustrated using a new panel survey of 1500 commuters in San Diego, California. These data are being collected to evaluate a federally-funded “Congestion Pricing” experiment investigating the impacts of allowing solo drivers to pay to use freeway carpool lanes. The panel survey, begun in Fall 1997, collects data on travel behavior and attitudes at six-month intervals through telephone interviews. The panel sample is refreshed with new respondents at each wave to counteract the attrition between waves. Both the original and refreshment samples are stratified on commuters’ mode choice (solo drive in free lanes, pay to solo drive in the carpool lanes, or carpool for free in carpool lanes) to insure sufficient sample size for estimating our models