A. James Clark School of Engineering

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    DYNAMIC DISCRETE CHOICE MODELS FOR CAR OWNERSHIP MODELING
    (2011) XU, RENTING; CIRILLO, CINZIA; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    With the continuous and rapid changes in modern societies, such as the introduction of advanced technologies, aggressive marketing strategies and innovative policies, it is more and more recognized by researchers in various disciplines from social science to economics that choice situations take place in a dynamic environment and that strong interdependencies exist among decisions made at different points in time. The increasing concerns about climate change, the development of high-tech vehicles, and the extensive applications of demand models in economics and transportation areas motivate this research on vehicle ownership based on disaggregate discrete choices. Over the next five to ten years, dramatic changes in the automotive marketplace are expected to occur and new opportunities might arise. Therefore, a methodology to model dynamic vehicle ownership choices is formulated and implemented in this dissertation for short and medium-term planning. In the proposed dynamic model framework, the car ownership problem is described as a regenerative optimal stopping problem; when a purchase is made, the current vehicle state (vehicle age, mileage driven, etc.) is regenerated. The model allows the estimation of the probability of buying a new vehicle or postponing this decision; if the decision to buy is made, the model further investigates the vehicle type choices. Dynamic models explicitly account for consumers' expectations of future vehicle quality or market evolution, arising endogenously from their purchase decisions. Both static and dynamic formulations are applied first to simulated data in order to test the ability to recover the true underlying parameters of the synthetic population. Results obtained attest that the dynamic model outperforms the static MNL in terms of goodness of fit, parameters bias and predictive power. In particular, it is found that MNL captures the general trends in choice probabilities, but fails to recover peaks in demand and behavioral changes due to rapidly evolving external conditions. The extension to a real case study required a data collection effort. A preliminary pilot survey was designed and executed in the State of Maryland in fall 2010; the survey was self-administrated and web-based. Choices were made under the hypothesis that an interval time period of six months passed from a decision to the successive decision and choices over a hypothetical time period of six years were recorded. Finally, the application of dynamic discrete choice models to vehicle ownership decisions in the context of the introduction of new technology is proposed. Results from the real case study confirm our initial expectations, as the model fit is significantly superior to the fit of the static model.
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    MODELING CAR OWNERSHIP, TYPE AND USAGE FOR THE STATE OF MARYLAND
    (2010) Liu, Yangwen; Cirillo, Cinzia; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Over the last few decades there has been a great increase in the number of cars in the United States. Given the importance of vehicle ownership on both transport and land-use planning and its relationship with energy consumption, the environment and health, the growth in the number of vehicles and their use has been one of the most intensely researched transport topics over many years. This thesis presents a car ownership model framework for the State of Maryland. The model has been calibrated on publicly available data (2001 and 2009 National Household Travel Survey) without the burden and the consequent cost of collecting additional data. The sample has been sufficient to correctly estimate a number of relevant socio demographic and land use variables. The model has then been applied, for demonstration purposes, to test a number of sensitivity analysis concerning changes in housing density, income, urbanization, unemployment rates and fuel price.