Browsing by Subject "principal component analysis"
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Item Developing a methodology to account for commercial motor vehicles using microscopic traffic simulation models(Texas A&M University, 2004-09-30) Schultz, Grant GeorgeThe collection and interpretation of data is a critical component of traffic and transportation engineering used to establish baseline performance measures and to forecast future conditions. One important source of traffic data is commercial motor vehicle (CMV) weight and classification data used as input to critical tasks in transportation design, operations, and planning. The evolution of Intelligent Transportation System (ITS) technologies has been providing transportation engineers and planners with an increased availability of CMV data. The primary sources of these data are automatic vehicle classification (AVC) and weigh-in-motion (WIM). Microscopic traffic simulation models have been used extensively to model the dynamic and stochastic nature of transportation systems including vehicle composition. One aspect of effective microscopic traffic simulation models that has received increased attention in recent years is the calibration of these models, which has traditionally been concerned with identifying the "best" parameter set from a range of acceptable values. Recent research has begun the process of automating the calibration process in an effort to accurately reflect the components of the transportation system being analyzed. The objective of this research is to develop a methodology in which the effects of CMVs can be included in the calibration of microscopic traffic simulation models. The research examines the ITS data available on weight and operating characteristics of CMVs and incorporates this data in the calibration of microscopic traffic simulation models. The research develops a methodology to model CMVs using microscopic traffic simulation models and then utilizes the output of these models to generate the data necessary to quantify the impacts of CMVs on infrastructure, travel time, and emissions. The research uses advanced statistical tools including principal component analysis (PCA) and recursive partitioning to identify relationships between data collection sites (i.e., WIM, AVC) such that the data collected at WIM sites can be utilized to estimate weight and length distributions at AVC sites. The research also examines methodologies to include the distribution or measures of central tendency and dispersion (i.e., mean, variance) into the calibration process. The approach is applied using the CORSIM model and calibrated utilizing an automated genetic algorithm methodology.Item Protection Motivation Theory and Consumer Willingness-to-Pay, in the Case of Post-Harvest Processed Gulf Oysters(2012-10-19) Blunt, Emily AnnGulf oysters are harvested and consumed year-round, with more than 90% consumed in a raw, unprocessed state. A chief concern of policymakers in recent years is the incidence of Vibrio vulnificus infection following raw seafood consumption. V.vulnificus refers to a halophilic bacterium naturally occurring in brackish coastal waters, which concentrates in filter-feeding oysters. Proposed FDA legislation requiring processing of all raw Gulf oysters sold during warmer summer months threatens the Gulf oyster industry, as little to no research regarding demand for post-harvest processing (PHP) has preceded the potential mandate. This research endeavors to examine the relationship between oyster consumers' fears of V.vulnificus infection and their willingness-to-pay (WTP) for processing of an oyster meal. The psychological model of Protection Motivation Theory (PMT) is employed alongside the economic framework of contingent valuation (CV) to result in an analysis of oyster processing demand with respect to threats and efficacy. A survey administered to 2,172 oyster consumers in six oyster producing states elicits projected consumption and PMT data. Principal Component Analysis is used to reduce the number of PMT variables to a smaller size, resulting in five individual principal components representing the PMT elements of source information, threat appraisal, coping appraisal, maladaptive coping, and protection motivation. Using survey data, the marginal willingness-to-pay (MWTP) for PHP per oyster meal is also calculated, and the five created PMT variables are regressed on this calculation using four separate OLS models. Results indicate significant correlation for four of the five created PMT variables. In addition, a mean MWTP for PHP of $0.31 per oyster meal is determined, contributing to the demand analysis for processing of Gulf oysters. The findings suggest a strong relationship between the fear elements and the demand for processing, and support arguments in favor of further research on specific PHP treatments and the necessity for a valid PMT survey instrument.Item The Diversity of Variations in the Spectra of Type Ia Supernovae(2012-10-19) Wagers, Andrew JamesType Ia supernovae (SNe Ia) are currently the best probe of the expansion history of the universe. Their usefulness is due chiefly to their uniformity between supernovae (SNe). However, there are some slight variations amongst SNe that have yet to be understood and accounted for. The goal of this work is to uncover relationships between the spectral features and the light curve decline rate, [delta]m??. Wavelet decomposition has been used to develop a new spectral index to measure spectral line strengths independent of the continuum and easily corrected for noise. This new method yields consistent results without the arbitrary uncertainties introduced by current methods and is particularly useful for spectra which do not have a clearly defined continuum. These techniques are applied to SN Ia spectra and correlations are found between the spectral features and light curve decline rate. The wavelet spectral indexes are used to measure the evolution of spectral features which are characterized by 3 or 4 parameters for the most complicated evolution. The three absorption features studied here are associated with sulfur and silicon and all show a transition in strength between 1 to 2 weeks after B-band maximum. Pearson correlation coefficients between spectral features and [delta]m?? are found to be significant within a week of maximum brightness and 3 to 4 weeks post-maximum. These correlations are used to determine the principal components at each epoch among the set of SN spectra in this work. The variation contained in the first principal component (PC1) is found to be greater than 60% to 70% for most epochs and reaching as high as 80% to 90% for epochs with the highest correlations. The same first principal component can be used to relate spectral feature strengths to the decline rate. These relations were used to estimate a SN light curve decline rate from a set of spectra taken over the course of the explosion, from a single spectrum, or from even a single spectral feature. These relationships could be used for future surveys to estimate spectral characteristics from light curve data, such as photometric redshift.