UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
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Item STATISTICAL METHODS FOR ADVANCED ECONOMETRIC MODELS WITH APPLICATIONS TO VEHICLE HOLDING AND SPEED QUANTILES DISTRIBUTION(2016) Tremblay, Jean-Michel; Cirillo, Cinzia; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation proposes statistical methods to formulate, estimate and apply complex transportation models. Two main problems are part of the analyses conducted and presented in this dissertation. The first method solves an econometric problem and is concerned with the joint estimation of models that contain both discrete and continuous decision variables. The use of ordered models along with a regression is proposed and their effectiveness is evaluated with respect to unordered models. Procedure to calculate and optimize the log-likelihood functions of both discrete-continuous approaches are derived, and difficulties associated with the estimation of unordered models explained. Numerical approximation methods based on the Genz algortithm are implemented in order to solve the multidimensional integral associated with the unordered modeling structure. The problems deriving from the lack of smoothness of the probit model around the maximum of the log-likelihood function, which makes the optimization and the calculation of standard deviations very difficult, are carefully analyzed. A methodology to perform out-of-sample validation in the context of a joint model is proposed. Comprehensive numerical experiments have been conducted on both simulated and real data. In particular, the discrete-continuous models are estimated and applied to vehicle ownership and use models on data extracted from the 2009 National Household Travel Survey. The second part of this work offers a comprehensive statistical analysis of free-flow speed distribution; the method is applied to data collected on a sample of roads in Italy. A linear mixed model that includes speed quantiles in its predictors is estimated. Results show that there is no road effect in the analysis of free-flow speeds, which is particularly important for model transferability. A very general framework to predict random effects with few observations and incomplete access to model covariates is formulated and applied to predict the distribution of free-flow speed quantiles. The speed distribution of most road sections is successfully predicted; jack-knife estimates are calculated and used to explain why some sections are poorly predicted. Eventually, this work contributes to the literature in transportation modeling by proposing econometric model formulations for discrete-continuous variables, more efficient methods for the calculation of multivariate normal probabilities, and random effects models for free-flow speed estimation that takes into account the survey design. All methods are rigorously validated on both real and simulated data.Item A discrete-continuous modeling approach with applications to vehicle holding and use(2012) Tremblay, Jean-Michel; Cirillo, Cinzia; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Transportation and automobile use is a major concern today in the United- States. The use of automobile has impacts on congestion, urban dynamics, environ- ment and on the economy in general. Good indicators of transportation demand are the number of vehicles owned by a household and the total number of miles traveled. This thesis aims at building a model that can predict the total vehicle miles traveled and number of cars owned by households, simultaneously. The discrete- continuous model that we present correlates the error terms of a utility-based probit with the error term of an ordinary regression. The objective is to capture the relationship between preferred ownership alternatives and miles traveled. We successfully show that households with high utility for owning a lot of cars also drive more and that households with high utility for owning few cars drive less. The correlation is between utilities and miles traveled. It also correlates the two transportation demand indicators without assuming that one precedes the other and, thus, does not suffer from circular variable inclusions. The thesis ends by incorporating sampling weights into the model before pa- rameters are estimated. We find slight changes in parameters' values calculated with weights. The difference however, is more quantitative than qualitative since the general analysis we make with the weighted coefficients remains the same, only the magnitude of the effects change