This thesis develops a conceptual framework to determine the probability of adopting HT canola when producers are assumed heterogeneous. The model is based on the framework developed by Fulton and Keyowski (1999), but is modified from a deterministic model to a probabilistic model. The study also considers the gross returns from adopting HT canola. Canola production in Manitoba, Canada is chosen as the region of analysis for the empirical component of the study. In 2002, 74 per cent of total canola acres in Manitoba were devoted to HT canola production. Factors such as soil type, producer risk profile, experience, productivity, and management ability are considered as potential determining factors which distinguish adopters of HT technology from non-adopters. Based on an initial assessment of Manitoba canola data, which shows the incomplete adoption of HT technology in Manitoba, a model is developed which considers adoption of a new technology as a function of the characteristics of the adopters. The conceptual model is tested empirically in two-stages. The first stage employs Ordinary Least Squares analysis to estimate the expected yield of different canola varieties to determine whether producers realize a benefit from the adoption of HT varieties. A logit analysis is conducted in the second stage, and considers different attributes of producers such as risk aversion, management ability, productivity and expected yields to determine the probability of producers adopting HT technology. The results show two primary findings. First, certain HT varieties can be shown to give producers higher returns. Second, differentiating characteristics of producers are key in determining the likely adoption of HT canola.

This thesis develops a conceptual framework to determine the probability of adopting HT canola when producers are assumed heterogeneous. The model is based on the framework developed by Fulton and Keyowski (1999), but is modified from a deterministic model to a probabilistic model. The study also considers the gross returns from adopting HT canola. Canola production in Manitoba, Canada is chosen as the region of analysis for the empirical component of the study. In 2002, 74 per cent of total canola acres in Manitoba were devoted to HT canola production. Factors such as soil type, producer risk profile, experience, productivity, and management ability are considered as potential determining factors which distinguish adopters of HT technology from non-adopters. Based on an initial assessment of Manitoba canola data, which shows the incomplete adoption of HT technology in Manitoba, a model is developed which considers adoption of a new technology as a function of the characteristics of the adopters. The conceptual model is tested empirically in two-stages. The first stage employs Ordinary Least Squares analysis to estimate the expected yield of different canola varieties to determine whether producers realize a benefit from the adoption of HT varieties. A logit analysis is conducted in the second stage, and considers different attributes of producers such as risk aversion, management ability, productivity and expected yields to determine the probability of producers adopting HT technology. The results show two primary findings. First, certain HT varieties can be shown to give producers higher returns. Second, differentiating characteristics of producers are key in determining the likely adoption of HT canola.

This dissertation has four interrelated studies, namely (1) the characterization of milk purchase choices which included the purchase of organic milk, both organic and conventional milk and conventional milk only; (2) the estimation of a single-equation household demand function for organic and conventional milk; (3) the assessment of binary choice models for organic milk using the Brier Probability score and Yates partition, and (4) the estimation of demand systems that addresses the censoring issue through the use of econometric techniques. In the first paper, the study utilized the estimation of both multinomial logit and probit models in examining a set of causal socio-demographic variables in explaining the purchase of three outcome milk choices namely organic milk, organic and conventional milk and conventional milk only. These crucial variables include income, household size, education level and employment of household head, race, ethnicity and region. Using the 2004 Nielsen Homescan Panel, the second study used the Heckman two-step procedure in calculating the own-price, cross-price, and income elasticities by estimating the demand relationships for both organic and conventional milk. Results indicated that organic and conventional milk are substitutes. Also, an asymmetric pattern existed with regard to the substitution patterns of the respective milk types. Likewise, the third study showed that predictive outcomes from binary choice models associated with organic milk can be enhanced with the use of the Brier score method. In this case, specifications omitting important socio-demographic variables reduced the variability of predicted probabilities and therefore limited its sorting ability. The last study estimated both censored Almost Ideal Demand Systems (AIDS) and Quadratic Almost Ideal Demand System (QUAIDS) specifications in modeling nonalcoholic beverages. In this research, five estimation techniques were used which included the usage of Iterated Seemingly Unrelated Regression (ITSUR), two stage methods such as the Heien and Wessells (1990) and the Shonkwiler and Yen (1999) approaches, Generalized Maximum Entropy and the Dong, Gould and Kaiser (2004a) methods. The findings of the study showed that at various censoring techniques, price elasticity estimates were observed to have greater variability in highly censored nonalcoholic beverage items such as tea, coffee and bottled water.

Commercial vehicle movement within an urban area is an integral part of a region’s economic growth and has significant impact on the quality of life. Commercial traffic grows with economic activity and population growth. However, in regional models commercial traffic is not described as well as person travel. Modeling commercial vehicles is complex because of the involvement of multiple decision agents including shippers, carriers, and receivers and their interactions. The proprietary nature of truck data often limits development of behavioral econometric models that have superior predictive and policy analysis abilities. The efficient movement of goods is a very important component to urban civilization and economic development and therefore, understanding truck movement behavior is an important area of interest for transportation policy planning. The objective of this dissertation is to contribute to apply advanced choice modeling methods to analyze commercial vehicle travel behavior within an urban area. This research collects disaggregate level truck generation data from the business establishments located in a sample urban region and uses the collected data to evaluate factors that affect truck trip generation patterns using linear regression and ordered logit model structures. The results of the study show that employment size, business industrial class, truck ownership, land-use class, and land-value affect trip generation behavior. This research also analyzed three different multiple discrete-continuous (MDC) choice situations encountered by commercial vehicles on a daily basis. These choices are 1) the choice of tour chain(s) and the number of trips in each tour chain, 2) the time (s) of day choice to perform daily activities and the corresponding vehicle-miles traveled; and 3) the choice of destination location(s) among alternative destination zones and the number of stops at each destination zone. The study find that commercial vehicle characteristics, shipment characteristics, transportation network attributes, base location and intermediate stop location features affect the first two choice situations while the level of service and zonal attributes affect the destination choice behavior of commercial vehicle daily travel.

Discrete choice models (DCMs) are used to forecast demand in a variety of engineering, marketing, and policy contexts, and understanding the uncertainty associated with model forecasts is crucial to inform decision-making. This thesis evaluates the suitability of DCMs for forecasting automotive demand. The entire scope of this investigation is too broad to be covered here, but I explore several elements with a focus on three themes: defining how to measure forecast accuracy, comparing model specifications and forecasting methods in terms of prediction accuracy, and comparing the implications of model specifications and forecasting methods on vehicle design. Specifically I address several questions regarding the accuracy and uncertainty of market share predictions resulting from choice of utility function and structural specification, estimation method, and data structure assumptions. I1 compare more than 9,000 models based on those used in peer-reviewed literature and academic and government studies. Firstly, I find that including more model covariates generally improves predictive accuracy, but that the form those covariates take in the utility function is less important. Secondly, better model fit correlates well with better predictive accuracy; however, the models I construct— representative of those in extant literature— exhibit substantial prediction error stemming largely from limited model fit due to unobserved attributes. Lastly, accuracy of predictions in existing markets is neither a necessary nor sufficient condition for use in design. Much of the econometrics literature on vehicle market modeling has presumed that biased coefficients make for bad models. For purely predictive purposes, the drawbacks of potentially mitigating bias using generalized method of moments estimation coupled with instrumental variables outweigh the expected benefits in the experiments conducted in this dissertation. The risk of specifying invalid instruments is high, and my results suggest that the instruments frequently used in the automotive demand literature are likely invalid. Furthermore, biased coefficients are not necessarily bad for maximizing the predictive power of the model. Bias can even aid predictions by implicitly capturing persistent unobserved effects in some circumstances. Including alternative specific constants (ASCs) in DCM utility functions improves model fit but not necessarily forecast accuracy. For frequentist estimated models all tested methods of forecasting ASCs improved share predictions of the whole midsize sedan market over excluding ASC in predictions, but only one method results in improved long term new vehicle, or entrant, forecasts. As seen in a synthetic data study, assuming an incorrect relationship between observed attributes and the ASC for forecasting risks making worse forecasts than would be made by a model that excludes ASCs entirely. Treating the ASCs as model parameters with full distributions of uncertainty via Bayesian estimation is more robust to selection of ASC forecasting method and less reliant on persistent market structures, however it comes at increased computational cost. Additionally, the best long term forecasts are made by the frequentist model that treats ASCs as calibration constants fit to the model post estimation of other parameters.

L’objectif de cette thèse est de proposer des solutions efficaces à des problèmes de décision qui ont un impact sur la vie des citoyens, et qui reposent sur des données incertaines. Au niveau des applications, nous nous intéressons à deux problèmes de localisation qui ont un impact sur l’espace public, notamment la localisation de nouveaux logements, et la localisation de vendeurs mobiles dans l’espace urbain. Les problèmes de localisation ne sont pas un sujet récent dans la littérature, toutefois, pour ces deux problèmes qui reposent sur des modèles de choix pour le comportement d’achat des consommateurs, l’incertitude dans le modèle génère un cas spécial qui permet d’étendre la littérature sur l’Optimisation Robuste. Les contributions de cette thèse peuvent s’appliquer à divers problèmes génériques d’optimisation. / We address decision problems under uncertain information with non-linear structures of parameter variation, and devise solution methods in the spirit of Bertsimas and Sim’s Γ-Robustness approach. Furthermore, although the non-linear impact of uncertainty often introduces discrete structures to the problem, for tractability, we provide the conditions under which the complexity class of the nominal model is preserved for the robust counterpart. We extend the Γ-Robustness approach in three avenues. First, we propose a generic case of non-linear impact of parameter variation, and model it with a piecewise linear approximation of the impact function. We show that the subproblem of determining the worst-case variation can be dualized despite the discrete structure of the piece-wise function. Next, we built a robust model for the location of new housing where the non-linearity is introduced by a choice model, and propose a solution combining Γ-Robustness with a scenario-based approach. We show that the subproblem is tractable and leads to a linear formulation of the robust problem. Finally, we model the demand in a Location Problem through a Poisson Process inducing, when demands are uncertain, non-linear structures of parameter variation. We propose the concept of Nested Uncertainty Budgets to manage uncertainty in a tractable way through a hierarchical structure and, under this framework, obtain a subproblem that includes both continuous and discrete deviation variables.

This study proposed a utility-driven two-stage stochastic mixed-integer linear programming model to understand how the patient preferences impact the additive manufacturing (AM) supply chain design decisions. The goal of the mathematical model is to maximize the utilities derived from the customer preferences by appropriately allocating the AM facilities in the targeted region under customer decision and demand uncertainty. The mathematical model is visualized and validated by developing a real-life case study that utilizes the biomedical implants data for the state of Mississippi. A number of sensitivity analyses are conducted to understand how the patients' behavioral decisions (e.g., price-centric versus time- or quality-centric customers) to purchase biomedical implants impact the AM supply chain design decisions. The results revealed key managerial insights that could be utilized by healthcare service providers and interested stakeholders to provide quality healthcare services by managing patient-centric AM facility siting decisions.

<p>Blood products are used for transfusion in many routine procedures as well as emergency medical care. The balance between the supply and demand of blood products in Canada is being threatened by an increasing aging population, a growing immigrant population, and advances in medical technology which places additional strain on the blood supply. The objective of this research is to investigate the effects of demographic determinants and clinic accessibility on the frequency of blood donation in Canada excluding the province of Québec, providing a national assessment of blood donor correlates at the individual level. Exploration of these demographic factors in addition to clinic accessibility may help to explain why a limited number of repeat donors are currently contributing, with many donors giving blood only once a year. Repeat donors are vital to maintain a safe and secure blood supply, therefore it is important to retain existing donors in addition to recruiting new volunteers. In this study, individual donor and clinic information is obtained from the Canadian Blood Services 2008 national dataset, with contextual data from the 2006 Canadian Census. Discrete choice models are used to assess the effects of these variables on the frequency of blood donation across the country, highlighting the importance of clinic accessibility. The analysis is prepared for major Census Metropolitan Areas in Canada. Results may contribute to service optimization and targeted advertising, ultimately aiming to encourage the eligible population to donate.</p> / Master of Arts (MA)

The primary objective of this thesis is to assess the different statistical methods used forcalibrating the Samgods model, which is a transportation planning tool employed in Sweden toforecast the demand for freight transport. By focusing on the specific context of national logisticsmodels, this research aims to enhance the accuracy and reliability of the Samgods model throughproposed improvements.In addition to evaluating the calibration techniques for the Samgods model, this thesisexplores the broader application of statistical estimation methods in national logistics models.It examines their potential benefits and limitations in order to shed light on their significance.The findings of this research highlight the crucial role of statistical estimations in improvingthe accuracy of national logistics models, thus enabling better-informed decision-making intransport planning and logistics management.By estimating the cost sensitivity parameter in the Swedish national logistics model, Samgods,this thesis contributes to a deeper understanding of the role of statistical estimations in optimizingsuch models. It underscores the importance of reliable and accurate data analysis in transportationplanning and logistics management. Ultimately, the aim is to provide valuable insights into howstatistical estimations can enhance the effectiveness of national logistics models.

The financial and environmental effects of traffic congestion and automobile-centric air pollution continue to be problems that must be addressed within the United States. In response, travel demand management (TDM) has emerged as a potential way to reduce automobile-based travel in order to minimize these effects. TDM strategies are highly dependent on specific urban form characteristics such as bicycle lanes, sidewalks, or transit facilities. A current gap exists in the analytical tools available to transportation planners when evaluating TDM projects. The standard transportation models do not take into account urban form characteristics in a systematic way. These characteristics play an import role in an individual's selection of walking, bicycling, or transit based travel modes. This gap needs to be filled in order to evaluate TDM projects with the same decision-making rigor that is applied to road expansion projects. The purpose of this project is to develop an enhanced transportation mode choice model that presents a systematic approach for incorporating urban form characteristics. This approach determines which elements of urban form have the strongest influence on transportation mode choice behavior. This work is being done in conjunction with the Roanoke Valley Allegheny Metropolitan Planning Organization as a way to evaluate the potential of TDM projects in promoting non-automobile forms of travel within the Roanoke region. This approach to developing an enhanced transportation mode choice model is a step forward in address the gap between TDM strategies and the tools needed to evaluate them. / Master of Urban and Regional Planning