Nanotechnology is one of the key innovative technologies in the present century. The food industry has applied this technology in each of its sectors. Nanotechnology has tremendous potential in food and agriculture, including advancing agricultural cultivation and food production, enhancing food nutrition and flavor, and improving food packaging and preservation. However, the novel properties of nanoscale materials that allow beneficial applications are also accompanied with uncertainties, even unknown risks. A number of studies have examined public understanding as well as acceptance of nanotechnology via surveys in both the US and Europe. However, most of these studies concentrated on public attitudes in general. Few works focused on specific products, let alone food or food related products. This project will contribute to the literature by calculating monetary valuations (i.e., willingness-to-pay) for canola oil where new techniques are utilized. Using choice experiment survey data, consumers’ valuations for nano attributes were estimated with choice models. As implied, consumers were willing to pay $0.95 less for a typical bottle (48 fl. oz.) of canola oil if it was produced from nanoscale-modified seed; $0.51 less if the final products were packed with nanotechnology-enhanced packaging technique; and no significant difference was found for oil that was designed with health enhancing nano-engineered oil drops, which would require interaction with the human digestive system. Additionally, the results revealed unobserved heterogeneities among respondents in their willingness-to-pay for canola oil attributes. Aligned with descriptive results, 46.7% of the respondents reported that they were optimistic about new technology applied to food products. While a significant portion of the respondents (42.8%) indicated that they might gain benefits at the same level as risks, there were a slightly larger proportion of the respondents who feared they might be exposed to more risks than benefits through nanofoods. Further analysis included respondents’ attitudes and opinions as well as their demographic and socioeconomic characteristics toward the goal of understanding the underlying behavior difference. Findings from this study will help bridge the gap between scientific innovation and public policy and social-economic concerns. Implications for government policy that can be efficiently used to monitor and regulate these technologies were also investigated.

Mobility is an undeniable issue for current and future elderly populations. The increasing popularity for retirees to live in rural communities makes this a particularly important issue in rural towns. When an elderly individual living in a rural community is no longer able to drive, issues that come with living in a rural area may be exacerbated, and the individual may experience a decrease in their quality of life. Although individuals may be able to use public transportation most existing options do not promote an independent lifestyle. Any updated rural transportation system benefiting the elderly would be funded by taxpayers. An understanding of the taxpayers' preferences and willingness-to-pay (WTP) for transportation options, therefore, is essential. Few, if any economic studies have addressed this issue. The objectives of this research are to: (1) estimate economic willingness-to-pay (WTP) for public transportation options by using choice modeling techniques; and (2) better understand opinions related to public transportation for the elderly held by the general population as a whole and within different demographics. To complete these objectives, a choice survey was distributed to samples of three populations: residents of Atascosa County (located in south Texas); residents of Polk County (located in east Texas); and students at Texas A&M University. Respondents were presented with transportation options made of five attributes: addition to annual vehicle registration fee, days of operation, hours of operation, type of route, and senior citizen transportation fare discount. Results show both students and the general public value public transportation options and are willing to pay for specific transportation attributes. Respondents tended to prefer options which are more flexible than the less flexible attribute presented to them; however, respondents did not necessarily prefer the most flexible options. Students, generally, are willing to pay more for transportation attributes than county residents. Overall, both Atascosa and Polk County residents have similar WTP's, indicating both populations value rural public transportation similarly. The effects of socio-demographic variables on residents' decision to choose a transportation option appear to differ between the counties. These findings imply that while the influence of transportation attribute levels are consistent across counties, local input is important in customizing transportation systems to meet local expectations.

Understanding joint and causal relationships among multiple endogenous variables has been of much interest to researchers in the field of activity and travel behavior modeling. Structural equation models have been widely developed for modeling and analyzing the causal relationships among travel time, activity duration, car ownership, trip frequency and activity frequency. In the model, travel time and activity duration are treated as continuous variables, while car ownership, trip frequency and activity frequency as ordered discrete variables. However, many endogenous variables of interest in travel behavior are not continuous or ordered discrete but unordered discrete in nature, such as mode choice, destination choice, trip chaining pattern and time-of-day choice (it can be classified into a few categories such as AM peak, midday, PM peak and off-peak). A modeling methodology with involvement of unordered discrete variables is highly desired for better understanding the causal relationships among these variables. Under this background, the proposed dissertation study will be dedicated into seeking an appropriate modeling methodology which aids in identifying the causal relationships among activity and travel variables including unordered discrete variables. In this dissertation, the proposed modeling methodologies are applied for modeling the causal relationship between three pairs of endogenous variables: trip chaining pattern vs. mode choice, activity timing vs. duration and trip departure time vs.mode choice. The data used for modeling analysis is extracted from Swiss Travel Microcensus 2000. Such models provide us with rigorous criteria in selecting a reasonable application sequence of sub-models in the activity-based travel demand model system.

In this thesis an algorithm (MLOPT) for mixed logit models is proposed. Mixed logit models are flexible discrete choice models, but their estimation with large datasets involves the solution of a nonlinear optimization problem with a high dimensional integral in the objective function, which is the log-likelihood function. This complex structure is a general problem that occurs in statistics and optimization. MLOPT uses sampling from the dataset of individuals to generate a data sample. In addition to this, Monte Carlo samples are used to generate an integration sample to estimate the choice probabilities. MLOPT estimates the log-likelihood function values for each individual in the dataset by controlling and adaptively changing the data sample and the size of the integration sample at each iteration. Furthermore, MLOPT incorporates statistical testing for the quality of the solution obtained within the optimization problem. MLOPT is tested with a benchmark study from the literature (AMLET) and further applied to real-life applications in the automotive industry by predicting market shares in the Low Segment of the new car market. The automotive industry is particularly interesting in that understanding the behavior of buyers and how rebates affect their preferences is very important for revenue management. Real transaction data is used to generate and test the mixed logit models developed in this study. Another new aspect of this study is that the sales transactions are differentiated with respect to the transaction type of the purchases made. These mixed logit models are used to estimate demand and analyze market share changes under different what-if scenarios. An analysis and discussion of the results obtained are also presented.

Made available in DSpace on 2014-06-12T17:16:13Z (GMT). No. of bitstreams: 2 arquivo3661_1.pdf: 1943888 bytes, checksum: f8b9f3aa9e4ecea25a4ecef423358016 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2008 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / A tese tem como objetivo principal obter evidências das preferências dos trabalhadores residentes das Regiões Metropolitanas (RMs) do Brasil pelo consumo dos atributos locais, em especial, os atributos climáticos. Desta forma, foi aplicado o critério da propensão marginal a pagar (PMg), o qual indica o quanto os trabalhadores estão dispostos a pagar pelo consumo dos atributos locais. Em adição, com base na estimação do valor atribuído pelo consumo das amenidades foi possível ordenar as RMs com respeito à qualidade das amenidades. Três abordagens para a valoração das amenidades foram adotadas, o que se configurou em três ensaios que integram a tese: i) a abordagem hedônica; ii) a abordagem de Kahn (1995); e iii) a abordagem da escolha discreta (McFadden, 1974; Train, 2003). A análise é realizada para nove principais Regiões RMs Brasileiras: Belém; Fortaleza, Recife; Salvador; Belo Horizonte; Rio de Janeiro; São de Paulo; Curitiba; e Porto Alegre. Três principais bases de dados foram utilizadas na pesquisa: a Pesquisa Anual de Amostra e Domicílio (PNAD-2006); os dados de clima do Instituto Nacional de Meteorologia (INMET-1990 a 2006); e os dados de poluição do Centro de Tempo e Estudos Climáticos (CPTEC 2004 e 2005). Em resumo, considerando as duas abordagens que utilizam a PMg pelas amenidades no calculo do Índice de Qualidade das Amenidade (IQA) a abordagem hedônica e a abordagem discreta a abordagem hedônica apresentou alguns resultados não esperados. Mas as estimações a partir da abordagem discreta corroboraram com as expectativas realizadas a respeito da influência das amenidades para o bem-estar dos trabalhadores: a proximidade para o mar, a média da temperatura do mês de Julho, e a média da precipitação do mês de Setembro atuando como amenidades; e a poluição do ar, a umidade relativa do ar, a média da precipitação anual, e a temperatura média do mês de Dezembro atuando como amenidades negativas . Também há evidências, a partir do modelo discreto, de que os trabalhadores têm preferências heterogêneas pelas amenidades

Product labeling is increasingly used as a tool to differentiate products with public and private benefits that cannot be readily evaluated by a consumer at the time of purchase. Our research investigates how a labeling program may be applied in the sale of ornamental plants to address two key issues; plant disease and irrigation water use. A choice modeling survey was utilized to estimate consumers' willingness to pay a premium for six ornamental plants with disease-free and/or water conservation certification labels. The results of the mixed logit models show consumers are willing to pay a premium for plants certified as disease-free and/or produced with water conservation practices. The results strongly suggest producers can recoup some of the costs of implementing water conservation measures such as water recycling and disease control measures amid regulatory and drought concerns. Our research also investigated consumers' preferences for multiple third party certifying authorities and whether preferences for the labels varied among consumers. The results showed ornamental plant consumers did not reveal a preference for a particular certifying authority. The results are mixed as to whether willingness to pay for the labels varies among respondents. We show willingness to pay does vary among respondents for three of the plant models indicating preference heterogeneity. / Master of Science

<p dir="ltr">The emergence of time-dependent data provides researchers with unparalleled opportunities to investigate disaggregated levels of safety performance on roadway infrastructures. A disaggregated crash risk analysis uses both time-dependent data (e.g., hourly traffic, speed, weather conditions and signal controls) and fixed data (e.g., geometry) to estimate hourly crash probability. Despite abundant research on crash risk analysis, coordinated signalized intersections continue to require further investigation due to both the complexity of the safety problem and the relatively small number of past studies that investigated the risk factors of coordinated signalized intersections. This dissertation aimed to develop robust crash risk prediction models to better understand the risk factors of coordinated signalized intersections and to identify practical safety countermeasures. The crashes first were categorized into three types (same-direction, opposite-direction, and right-angle) within several crash-generating scenarios. The data needed were organized in hourly observations and included the following factors: road geometric features, traffic movement volumes, speeds, weather precipitation and temperature, and signal control settings. Assembling hourly observations for modeling crash risk was achieved by synchronizing and linking data sources organized at different time resolutions. Three different non-crash sampling strategies were applied to the following three statistical models (Conditional Logit, Firth Logit, and Mixed Logit) and two machine learning models (Random Forest and Penalized Support Vector Machine). Important risk factors, such as the presence of light rain, traffic volume, speed variability, and vehicle arrival pattern of downstream, were identified. The Firth Logit model was selected for implementation to signal coordination practice. This model turned out to be most robust based on its out-of-sample prediction performance and its inclusion of important risk factors. The implementation examples of the recommended crash risk model to building daily risk profiles and to estimating the safety benefits of improved coordination plans demonstrated the model’s practicality and usefulness in improving safety at coordinated signals by practicing engineers.</p>

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The global decline of pollinators, insects and animals that assist in pollination and fertilizing plants, poses a great risk to global food supply. This study investigates if people have preferences for a program to abate decline in a region along the East Coast of the United States. A survey is used to investigate these preferences and connections to the region to understand if individual connections can explain preferences. The majority of people were found to not have preferences to help increase pollinator abundance. Additionally, measures of connection to the region were found to be insignificant in explain individual preferences. / Master of Science / The global decline of pollinators, insects and animals that assist in pollination and fertilizing plants, poses a great risk to global food supply. This study investigates if people have preferences for a program to abate decline in a region along the East Coast of the United States. A survey is used to investigate these preferences and connections to the region to understand if individual connections can explain preferences. The majority of people were found to not have preferences to help increase pollinator abundance. Additionally, measures of connection to the region were found to be insignificant in explain individual preferences.

Generalised linear models (GLMs) provide a flexible and sound basis for calibrating gravity models for trip distribution, for a wide range of deterrence functions (from steps to splines), with K factors and geographic segmentation. The Tanner function fitted Wellington Transport Strategy Model data as well as more complex functions and was insensitive to the formulation of intrazonal and external costs. Weighting from variable expansion factors and interpretation of the deviance under sparsity are addressed. An observed trip matrix is disaggregated and fitted at the household, person and trip levels with consistent results. Hierarchical GLMs (HGLMs) are formulated to fit mixed logit models, but were unable to reproduce the coefficients of simple nested logit models. Geospatial analysis by HGLM showed no evidence of spatial error patterns, either as random K factors or as correlations between them. Equivalence with hierarchical mode choice, duality with trip distribution, regularisation, lorelograms, and the modifiable areal unit problem are considered. Trip distribution is calibrated from aggregate data by the MVESTM matrix estimation package, incorporating period and direction factors in the intercepts. Counts across four screenlines showed a significance similar to a thousand-household travel survey. Calibration was possible only in conjuction with trip end data. Criteria for validation against screenline counts were met, but only if allowance was made for error in the trip end data.