Theses and Dissertations from UMD
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Item 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.Item 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.Item THE INTERACTION BETWEEN DISTANCE TO WORK AND VEHICLE MILES TRAVELED(2008-01-31) Gonzalez, Hernan Mauricio; Horowitz, John K.; Agricultural and Resource Economics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Economists have long been concerned with the externalities generated by automobiles, such as traffic congestion and air pollution. Since many of these externalities are closely bound up with the number of miles being driven, economists have been much interested in the behavior of what is known as vehicle miles traveled (VMT). Planners believe that land use can be manipulated to serve congestion management, air quality or related transport planning goals. The underlying idea is that household location may have a big impact on its transportation demand, including car ownership. In this context, I focus on distance to work (DTW) as the measure of household location. I chose a continuous measure of household location instead of a discrete one because, besides being easily measured, it matches better the data available for this study and it has a very straightforward interpretation--it allows me to calculate the contribution of commuting miles to total miles driven. Despite the clear conceptual connection between DTW and VMT, and the constraining nature of household location, little is known about their joint behavior. City and household level attributes that may lead households to live close or far from their work may also lead them to drive few or many miles for non-commuting purposes. This effect must be accounted for when measuring the behavior of VMT conditional on DTW. I develop two models to analyze: (i) the role of city characteristics in explaining households' distance to work, (ii) the effect of distance to work on VMT and car ownership, (iii) the effect of city level attributes on VMT, conditional on DTW, (iv) the unobserved taste for driving, (v) differences between workers and non-workers. I find that: (i) City characteristics expected to affect commutes have a small effect on households' DTW, (ii) DTW provides an important effect on car ownership levels and VMT, (iii) City characteristics expected to influence non-commute miles have a small impact on VMT, (iv) taste for driving has a small but significant effect on VMT, and (v) non-workers are much less responsive to gas prices than workers.