A joint multiple discrete continuous extreme value (MDCEV) model and multinomial logit model (MNL) for examining vehicle type/vintage, make/model and usage decisions of the household
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In this dissertation, we seek to contribute to the area of automobile demand modeling by developing a comprehensive econometric model to examine several dimensions of household vehicle holdings and usage decisions. In particular, we model number of vehicles owned as well as the following attributes for each of the vehicles owned: (1) vehicle body type, (2) vehicle age (i.e., vintage), (3) vehicle make and model, and (4) vehicle usage. We develop a comprehensive conceptual framework for modeling the choice situation of households characterized by the simultaneous choice of multiple vehicle types/vintages and usage decisions as well as the choice of a single make and model within each vehicle type/vintage chosen. We translate this conceptual framework into a utility-theoretic formulation to analyze the many dimensions of vehicle holdings vii and use. Specifically, we formulate a nested model structure that includes a multiple discrete-continuous extreme value (MDCEV) component to analyze the choice of vehicle type/vintage and usage in the upper level and a multinomial logit (MNL) component to analyze the choice of vehicle make/model in the lower nest. The model is estimated using data from the 2000 San Francisco Bay Area Travel Survey. The model results indicate the important effects of household demographics, household location characteristics, built environment attributes, household head characteristics, and vehicle attributes on household vehicle holdings and use. Finally, the model developed in the dissertation is applied to predict the impact of land use and fuel cost changes on vehicle holdings and usage of the households. Such predictions can inform the design of proactive land-use, economic, and transportation policies to influence household vehicle holdings and usage in a way that reduces the negative impacts of automobile dependency such as traffic congestion, fuel consumption and air pollution.