Global ETD Search

51	Investigation of <i>Pseudomonas</i> Biofilm Development and Removal on Dairy Processing Equipment Surfaces Using Fourier Transform Infrared (FT-IR) Spectroscopy Manuzon, Michele Yabes 05 November 2009 (has links) No description available. Food Science <i>Pseudomonas</i> biofilm Fourier transform infrared spectroscopy microbial identification detection quantification multivariate analysis partial least squares regression (PLSR)
52	REAL-TIME PREDICTION OF SHIMS DIMENSIONS IN POWER TRANSFER UNITS USING MACHINE LEARNING Jansson, Daniel, Blomstrand, Rasmus January 2019 (has links) No description available. MATLAB Machine Learning Smart Manufacturing Classification Regression Power Transfer Units Industry 4.0 Assembly Line Support Vector Machines Artificial Neural Network Partial Least Squares Regression Decision Tree Bagging Bagged Decision Trees Support Vector Regression Machines SVR SVM ANN PTU DT Bagged DTs Robotics Robotteknik och automation
53	兩段迴歸結合蒙地卡羅模擬對可轉債定價之研究 / Pricing Convertible Bonds by Piecewise Regression and Monte Carlo Simulation 董恆元, Tung, Heng Yuan Unknown Date (has links) 可轉換公司債兼具了選擇權以及債券的性質，價值又會受到股價之影響，以傳統的方法定價十分不易。由於蒙地卡羅模擬能解決定價問題上狀態變數或許為多維度及路徑相依的問題，Kind 與Wilde 在2004 年提出以蒙地卡羅模擬對可轉債定價，且以最小平方迴歸法估計繼續持有價值，並在僅考慮轉換及還本兩種選擇權及沒有違約風險之下，以數值範例呈現單一迴歸模式無法適當估計繼續持有價值。然而，他們並未進行實證。本研究乃以民國99 年台灣發行的可轉債為研究對象，除考慮發行時的合約條件外，另加上信用評等的考量以將違約機率透過現金流量套入定價過程中，並分別以兩段迴歸及單一迴歸估計繼續持有價值以結合蒙地卡羅模擬，實證結果顯示就可轉債之起始定價的偏差比而言，兩段迴歸得到的結果優於單一迴歸。惟在兩段迴歸之下，超過八成的可轉債其模擬價格依然高於市場價格。實證結果也顯示價性(moneyness)及擔保狀況與定價的偏差有關。 / Convertible bonds (CBs) possess features of both bonds and options, and their prices are affected by the underlying stocks, which make the pricing problem an uneasy task for traditional methods. Since Monte Carlo simulation can handle the problems of path-dependence and multivariate dimensions faced by pricing, Kind and Wilde (2004) suggested to price CBs via least-squares Monte Carlo simulations (LSM), which estimate the continuation values by least squares regression. They also demonstrated that a single regression line could not appropriately estimate the continuation value even only conversion and redemption were allowed and the CB was free of default. So the idea of piecewise regression was recommended to improve the estimation process. However, they didn’t apply piecewise regression to real data. Therefore, piecewise regression together with Monte Carlo simulation were employed to investigate the pricing issue of Taiwan’s CBs. CBs issued on 2010 were selected, besides reviewing the contents of CB’s contracts, default risks based on credit ratings were taken into account to evaluate the discounted cash flows in the pricing procedure. Comparing the estimated model prices of LSM with initial selling prices, the mispricing rates of single regression model and piecewise regression model were obtained for further analysis. Result shows that the modified piecewise regression method performs better in mispricing rate. However, similar to previous findings, 80% of the estimated model prices based on piecewise regressions are still higher than market prices. It also shows that moneyness and guaranteed condition will relate to mispricing rate. 可轉換公司債定價蒙地卡羅模擬最小平方迴歸法繼續持有價值兩段迴歸定價偏差比價性擔保狀況 Convertible bonds pricing Monte Carlo simulation Least-squares regression continuation value piecewise regression mispricing rate moneyness guaranteed condition
54	Propriétés fonctionnelles et spectrales d’espèces végétales de tourbières ombrotrophes le long d’un gradient de déposition d’azote Girard, Alizée 12 1900 (has links) Les tourbières ombrotrophes, ou bogs sont particulièrement vulnérables à l’augmentation de la déposition atmosphérique d’azote. Cet apport d’un nutriment normalement limitant altère la capacité des tourbières à accumuler le carbone (C), en plus de mener à des changements de leur composition végétale. L’imagerie spectrale est une approche prometteuse puisqu’elle rend possible la détection des espèces végétales et de certaines caractéristiques chimiques des plantes, à distance. Toutefois, l’ampleur des différences spectrales intra- et interespèces n’est pas encore connue. Nous avons évalué la façon dont la chimie, la structure et la signature spectrale des feuilles changent chez Chamaedaphne calyculata, Kalmia angustifolia, Rhododendron groenlandicum et Eriophorum vaginatum, dans trois tourbières du sud du Québec et de l’Ontario, incluant une tourbière où se déroule une expérience de fertilisation à long terme. Nous avons mesuré des changements dans les traits fonctionnels dus aux différences dans la quantité d’azote disponible dans les sites. Toutefois, la déposition atmosphérique d’azote a eu relativement peu d’effet sur les spectres foliaires ; les variations spectrales les plus importantes étaient entre les espèces. En fait, nous avons trouvé que les quatre espèces ont un spectre caractéristique, une signature spectrale permettant leur identification au moyen d’analyses discriminantes des moindres carrés partiels (PLSDA). De plus, nous avons réussi à prédire plusieurs traits fonctionnels (l’azote, le carbone ; et la proportion d’eau et de matière sèche) avec moins de 10 % d’erreur grâce à des régressions des moindres carrés partiels (PLSR) des données spectrales. Notre étude fournit de nouvelles preuves que les variations intraspécifiques, causées en partie par des variations environnementales considérables, sont perceptibles dans les spectres foliaires. Toutefois, les variations intraspécifiques n’affectent pas l’identification des espèces ou la prédiction des traits. Nous démontrons que les spectres foliaires comprennent des informations sur les espèces et leurs traits fonctionnels, confirmant le potentiel de la spectroscopie pour le suivi des tourbières. / Abstract Bogs, as nutrient-poor ecosystems, are particularly sensitive to atmospheric nitrogen (N) deposition. Nitrogen deposition alters bog plant community composition and can limit their ability to sequester carbon (C). Spectroscopy is a promising approach for studying how N deposition affects bogs because of its ability to remotely determine changes in plant species composition in the long term as well as shorter-term changes in foliar chemistry. However, there is limited knowledge on the extent to which bog plants differ in their foliar spectral properties, how N deposition might affect those properties, and whether subtle inter- or intraspecific changes in foliar traits can be spectrally detected. Using an integrating sphere fitted to a field spectrometer, we measured spectral properties of leaves from the four most common vascular plant species (Chamaedaphne calyculata, Kalmia angustifolia, Rhododendron groenlandicum and Eriophorum vaginatum) in three bogs in southern Québec and Ontario, Canada, exposed to different atmospheric N deposition levels, including one subjected to a 18 years N fertilization experiment. We also measured chemical and morphological properties of those leaves. We found detectable intraspecific changes in leaf structural traits and chemistry (namely chlorophyll b and N concentrations) with increasing N deposition and identified spectral regions that helped distinguish the site-specific populations within each species. Most of the variation in leaf spectral, chemical and morphological properties was among species. As such, species had distinct spectral foliar signatures, allowing us to identify them with high accuracy with partial least squares discriminant analyses (PLSDA). Predictions of foliar traits from spectra using partial least squares regression (PLSR) were generally accurate, particularly for the concentrations of N and C, soluble C, leaf water, and dry matter content (<10% RMSEP). However, these multi-species PLSR models were not accurate within species, where the range of values was narrow. To improve the detection of short-term intraspecific changes in functional traits, models should be trained with more species-specific data. Our field study showing clear differences in foliar spectra and traits among species, and some within-species differences due to N deposition, suggest that spectroscopy is a promising approach for assessing long-term vegetation changes in bogs subject to atmospheric pollution. traits foliaires signature spectrale tourbière ombrotrophe télédétection azote déposition atmosphériqued'azote functional traits spectral signature ombrotrophic bog partial least squares regression (PLSR) spectranomics spectroscopy peatland reflectance
55	Stochastic approximation and least-squares regression, with applications to machine learning / Approximation stochastique et régression par moindres carrés : applications en apprentissage automatique Flammarion, Nicolas 24 July 2017 (has links) De multiples problèmes en apprentissage automatique consistent à minimiser une fonction lisse sur un espace euclidien. Pour l’apprentissage supervisé, cela inclut les régressions par moindres carrés et logistique. Si les problèmes de petite taille sont résolus efficacement avec de nombreux algorithmes d’optimisation, les problèmes de grande échelle nécessitent en revanche des méthodes du premier ordre issues de la descente de gradient. Dans ce manuscrit, nous considérons le cas particulier de la perte quadratique. Dans une première partie, nous nous proposons de la minimiser grâce à un oracle stochastique. Dans une seconde partie, nous considérons deux de ses applications à l’apprentissage automatique : au partitionnement de données et à l’estimation sous contrainte de forme. La première contribution est un cadre unifié pour l’optimisation de fonctions quadratiques non-fortement convexes. Celui-ci comprend la descente de gradient accélérée et la descente de gradient moyennée. Ce nouveau cadre suggère un algorithme alternatif qui combine les aspects positifs du moyennage et de l’accélération. La deuxième contribution est d’obtenir le taux optimal d’erreur de prédiction pour la régression par moindres carrés en fonction de la dépendance au bruit du problème et à l’oubli des conditions initiales. Notre nouvel algorithme est issu de la descente de gradient accélérée et moyennée. La troisième contribution traite de la minimisation de fonctions composites, somme de l’espérance de fonctions quadratiques et d’une régularisation convexe. Nous étendons les résultats existants pour les moindres carrés à toute régularisation et aux différentes géométries induites par une divergence de Bregman. Dans une quatrième contribution, nous considérons le problème du partitionnement discriminatif. Nous proposons sa première analyse théorique, une extension parcimonieuse, son extension au cas multi-labels et un nouvel algorithme ayant une meilleure complexité que les méthodes existantes. La dernière contribution de cette thèse considère le problème de la sériation. Nous adoptons une approche statistique où la matrice est observée avec du bruit et nous étudions les taux d’estimation minimax. Nous proposons aussi un estimateur computationellement efficace. / Many problems in machine learning are naturally cast as the minimization of a smooth function defined on a Euclidean space. For supervised learning, this includes least-squares regression and logistic regression. While small problems are efficiently solved by classical optimization algorithms, large-scale problems are typically solved with first-order techniques based on gradient descent. In this manuscript, we consider the particular case of the quadratic loss. In the first part, we are interestedin its minimization when its gradients are only accessible through a stochastic oracle. In the second part, we consider two applications of the quadratic loss in machine learning: clustering and estimation with shape constraints. In the first main contribution, we provided a unified framework for optimizing non-strongly convex quadratic functions, which encompasses accelerated gradient descent and averaged gradient descent. This new framework suggests an alternative algorithm that exhibits the positive behavior of both averaging and acceleration. The second main contribution aims at obtaining the optimal prediction error rates for least-squares regression, both in terms of dependence on the noise of the problem and of forgetting the initial conditions. Our new algorithm rests upon averaged accelerated gradient descent. The third main contribution deals with minimization of composite objective functions composed of the expectation of quadratic functions and a convex function. Weextend earlier results on least-squares regression to any regularizer and any geometry represented by a Bregman divergence. As a fourth contribution, we consider the the discriminative clustering framework. We propose its first theoretical analysis, a novel sparse extension, a natural extension for the multi-label scenario and an efficient iterative algorithm with better running-time complexity than existing methods. The fifth main contribution deals with the seriation problem. We propose a statistical approach to this problem where the matrix is observed with noise and study the corresponding minimax rate of estimation. We also suggest a computationally efficient estimator whose performance is studied both theoretically and experimentally. Optimisation convexe Accélération Moyennage Gradient stochastique Régression par moindres carrés Approximation stochastique Algorithme dual moyenné Descente miroire Partionnement discriminatif Relaxation convexe Parcimonie Sériation statistique Apprentissage de permutation Estimation minimax Contraintes de forme Convex optimization Acceleration Averaging Stochastic gradient Least-squares regression Stochastic approximation Dual averaging Mirror descent Discriminative clustering Convex relaxation Sparsity Statistical seriation Permutation learning Minimax estimation Shape constraints 519
56	Natural gas storage level forecasting using temperature data Sundin, Daniel January 2020 (has links) Even though the theory of storage is historically a popular view to explain commodity futures prices, many authors focus on the oil price link. Past studies have shown an increased futures price volatility on Mondays and days when natural gas storage levels are released, which could both implicate that storage levels and temperature data are incorporated in the prices. In this thesis, the U.S. natural gas storage level change is studied as a function of the consumption and production. Consumption and production are furthered segmented and separately forecasted by modelling inverse problems that are solved by least squares regression using temperature data and timeseries analysis. The results indicate that each consumer consumption segment is highly dependent of the temperature with R2-values of above 90%. However, modelling each segment completely by time-series analysis proved to be more efficient due to lack of flexibility in the polynomials, lack of used weather stations and seasonal patterns in addition to the temperatures. Although the forecasting models could not beat analysts’ consensus estimates, these present natural gas storage level drivers and can thus be used to incorporate temperature forecasts when estimating futures prices. Natural gas storage forecasting Natural gas storage Natural gas Storage forecasting Forecasting Futures Futures forecasting Natural gas futures forecasting Least squares regression Regression Machine learning Exponential Weighted Moving Average Moving Average EWMA MA Seasonality Commodity futures Optimisation Inverse problems Consumption forecasting Production Forecasting Residential Commercial Industrial Electric power NOAA EIA Pipelines Polynomial Weather stations Mathematics Matematik
57	Using Laser-Induced Breakdown Spectroscopy (LIBS) for Material Analysis / Using Laser-Induced Breakdown Spectroscopy (LIBS) for Material Analysis Pořízka, Pavel January 2014 (has links) Tato doktorská práce je zaměřena na vývoj algoritmu ke zpracování dat naměřených zařízením pro spektrometrii laserem indukovaného plazmatu (angl. LIBS). Zařízení LIBS s tímto algoritmem by mělo být následně schopno provést třídění vzorků a kvantitativní analýzu analytu in-situ a v reálném čase. Celá experimentální část této práce byla provedena ve Spolkovém institutu pro materiálový výzku a testování (něm. BAM) v Berlíně, SRN, kde byl sestaven elementární LIBS systém. Souběžně s experimentílní prací byl vytvořen přehled literárních zdrojů s cílem podat ucelený pohled na problematiku chemometrických metod používaných k analýze LIBS měření. Použití chemometrických metod pro analýzu dat získaných pomocí LIBS měření je obecně doporučováno především tehdy, jsou-li analyzovány vzorky s komplexní matricí. Vývoj algoritmu byl zaměřen na kvantitativní analýzu a třídění vyvřelých hornin na základě měření pomocí LIBS aparatury. Sada vzorků naměřených použitím metody LIBS sestávala z certifikovaných referenčních materiálů a vzorků hornin shromážděných přímo na nalezištích mědi v Íránu. Vzorky z Íránu byly následně na místě roztříděny zkušeným geologem a množství mědi v daných vzorcích bylo změřeno na Univerzitě v Clausthalu, SRN. Výsledné kalibrační křivky byly silně nelineární, přestože byly sestaveny i z měření referenčních vzorků. Kalibrační křivku bylo možné rozložit na několik dílčích tak, že závislost intenzity měděné čáry na množství mědi se nacházela v jiném trendu pro jednotlivé druhy hornin. Rozdělení kalibrační křivky je zpravidla přisuzováno tzv. matričnímu jevu, který silně ovlivňuje měření metodou LIBS. Jinými slovy, pokud určujeme množství analytu ve vzorcích s různou matricí, je výsledná kalibrační křivka sestavená pouze z jedné proměnné (intenzity zvolené spektrální čáry analytu) nepřesná. Navíc, normalizace takto vytvořených kalibračních křivek k intenzitě spektrální čáry matrčního prvku nevedla k výraznému zlepšení linearity. Je obecně nemožné vybrat spektrální čáru jednoho matričního prvku pokud jsou analyzovány prvky s komplexním složením matric. Chemometrické metody, jmenovitě regrese hlavních komponent (angl. PCR) a regrese metodou nejmenších čtverců (angl. PLSR), byly použity v multivariační kvantitatvní analýze, tj. za použití více proměnných/spektrálních čar analytu a matričních prvků. Je potřeba brát v potaz, že PCR a PLSR mohou vyvážit matriční jev pouze do určité míry. Dále byly vzorky úspěšně roztříděny pomocí analýzy hlavních komponent (angl. PCA) a Kohonenových map na základě složení matričních prvků (v anglické literatuře se objevuje termín ‚spectral fingerprint‘) Na základě teorie a experimentálních měření byl navržen algoritmus pro spolehlivé třídění a kvantifikaci neznámých vzorků. Tato studie by měla přispět ke zpracování dat naměřených in-situ přístrojem pro dálkovou LIBS analýzu. Tento přístroj je v současnosti vyvíjen v Brně na Vysokém učení technickém. Toto zařízení bude nenahraditelné při kvantifikaci a klasifikaci vzorků pouze tehdy, pokud bude použito zároveň s chemometrickými metodami a knihovnami dat. Pro tyto účely byla již naměřena a testována část knihoven dat v zaměření na aplikaci metody LIBS do těžebního průmyslu.
58	Statistical Methods For Kinetic Modeling Of Fischer Tropsch Synthesis On A Supported Iron Catalyst Critchfield, Brian L. 15 December 2006 (has links) (PDF) Fischer-Tropsch Synthesis (FTS) is a promising technology for the production of ultra-clean fuels and chemical feedstocks from biomass, coal, or natural gas. Iron catalysts are ideal for conversion of coal and biomass. However, precipitated iron catalysts used in slurry-bubble column reactors suffer from high attrition resulting in difficulty separating catalysts from product and increased slurry viscosity. Thus, development of an active and selective-supported iron catalyst to manage attrition is needed. This thesis focuses on the development of a supported iron catalyst and kinetic models of FTS on the catalyst using advanced statistical methods for experimental design and analysis. A high surface area alumina, modified by the addition of approximately 2 wt% lanthanum, was impregnated with approximately 20 wt% Fe and 1% Pt in a two step procedure. Approximately 10 wt% Fe and 0.5 wt% Pt was added in each step. The catalyst had a CO uptake of 702 μmol/g, extent of reduction of 69%, and was reduced at 450°C. The catalyst was stable over H2 partial pressures of 4-10 atm, CO partial pressures of 1-4 atm, and temperatures of 220-260°C. Weisz modulus values were less than 0.15. A Langmuir-Hinshelwood type rate expression, derived from a proposed FTS mechanism, was used with D-optimal criterion to develop experiments sequentially at 220°C and 239°C. Joint likelihood confidence regions for the rate expression parameters with respect to run number indicate rapid convergence to precise-parameter estimates. Difficulty controlling the process at the designed conditions and steep gradients around the D-optimal criterion resulted in consecutive runs having the same optimal condition. In these situations another process condition was chosen to avoid consecutive replication of the same process condition. A kinetic model which incorporated temperature effects was also regressed. Likelihood and bootstrap confidence intervals suggested that the model parameters were precise. Histograms and skewness statistics calculated from Bootstrap resampling show parameter-effect nonlinearities were small. fischer tropsch synthesis bootstrap method d optimal computer generated experimental designs kinetic model statistical experimental design kinetic model boottstrap resampling slurry-bubble column supported iron catalyst nonlinear least squares regression lanthanum modified alumina langmuir hinshelwood rate expression likelihood confidence regions skewness statistic histogram mechanism parameter effect nonlinearity sequential experimental design weisz modulus attrition optimal design impregnation Chemical Engineering
59	Phytochemical investigation of Acronychia species using NMR and LC-MS based dereplication and metabolomics approaches / Etude phytochimique d’espèces du genre Acronychia en utilisant des approches de déréplication et métabolomique basées sur des techniques RMN et SM Kouloura, Eirini 28 November 2014 (has links) Les plantes médicinales constituent une source inexhaustible de composés (des produits naturels - PN) utilisé en médecine pour la prévention et le traitement de diverses maladies. L'introduction de nouvelles technologies et méthodes dans le domaine de la chimie des produits naturels a permis le développement de méthodes ‘high throughput’ pour la détermination de la composition chimique des extraits de plantes, l'évaluation de leurs propriétés et l'exploration de leur potentiel en tant que candidats médicaments. Dernièrement, la métabolomique, une approche intégrée incorporant les avantages des technologies d'analyse moderne et la puissance de la bioinformatique s’est révélé un outil efficace dans la biologie des systèmes. En particulier, l'application de la métabolomique pour la découverte de nouveaux composés bioactifs constitue un domaine émergent dans la chimie des produits naturels. Dans ce contexte, le genre Acronychia de la famille des Rutaceae a été choisi sur la base de son usage en médecine traditionnelle pour ses propriétés antimicrobienne, antipyrétique, antispasmodique et anti-inflammatoire. Nombre de méthodes chromatographiques modernes, spectrométriques et spectroscopiques sont utilisées pour l'exploration de leur contenu en métabolites suivant trois axes principaux constituant les trois chapitres de cette thèse. En bref, le premier chapitre décrit l’étude phytochimique d’Acronychia pedunculata, l’identification des métabolites secondaires contenus dans cette espèce et l'évaluation de leurs propriétés biologiques. Le deuxième chapitre vise au développement de méthodes analytiques pour l'identification des dimères d’acétophénones (marqueurs chimiotaxonomiques du genre) et aux stratégies utilisées pour la déréplication de ces différents extraits et la caractérisation chimique des composés par UHPLC-HRMSn. Le troisième chapitre se concentre sur l'application de méthodologies métabolomique (RMN et LC-MS) pour l'analyse comparative (entre les différentes espèces, origines, organes), pour des études chimiotaxonomiques (entre les espèces) et pour la corrélation des composés contenus avec une activité pharmacologique. / Medicinal plants constitute an unfailing source of compounds (natural products – NPs) utilised in medicine for the prevention and treatment of various deceases. The introduction of new technologies and methods in the field of natural products chemistry enabled the development of high throughput methodologies for the chemical composition determination of plant extracts, evaluation of their properties and the exploration of their potentials as drug candidates. Lately, metabolomics, an integrated approach incorporating the advantages of modern analytical technologies and the power of bioinformatics has been proven an efficient tool in systems biology. In particular, the application of metabolomics for the discovery of new bioactive compounds constitutes an emerging field in natural products chemistry. In this context, Acronychia genus of Rutaceae family was selected based on its well-known traditional use as antimicrobial, antipyretic, antispasmodic and anti-inflammatory therapeutic agent. Modern chromatographic, spectrometric and spectroscopic methods were utilised for the exploration of their metabolite content following three basic axes constituting the three chapters of this thesis. Briefly, the first chapter describes the phytochemical investigation of Acronychia pedunculata, the identification of secondary metabolites contained in this species and evaluation of their biological properties. The second chapter refers to the development of analytical methods for the identification of acetophenones (chemotaxonomic markers of the genus) and to the dereplication strategies for the chemical characterisation of extracts by UHPLC-HRMSn. The third chapter focuses on the application of metabolomic methodologies (LC-MS & NMR) for comparative analysis (between different species, origins, organs), chemotaxonomic studies (between species) and compound-activity correlations. Produits naturels Acronychia Rutaceae Acétophénones de type Acronychia Dimères de phloroglucinol Rotamères Enantiomères Diastereomères Chromatographie supercritique (SFC) Activité antibactérienne Cytotoxicité Activité anti-inflammatoire Ionisation par électrospray (ESI) Orbitrap Métabolomique Carbon-13 NMR UPLC-ESI(±)-HRMS/MS Analyse en composantes principales (ACP) Sparse PLS (sPLS) Natural products Acronychia Rutaceae Acronychia-type acetophenones Phloroglucinol dimers Rotamers Enantiomers Diastereomers Supercritical fluid chromatography (SFC) Antibacterial activity Cytotoxicity Anti-inflammatory activity Activity, electrospray ionization (ESI) Multistage mass spectrometry Orbitrap Metabolomics Carbon-13 NMR UPLC-ESI(±)-HRMS/MS Principal component analysis (PCA) Partial least squares regression (PLS-r) Orthogonal partial least squares (OPLS) Sparse PLS (sPLS) 615.321
60	Highway Development Decision-Making Under Uncertainty: Analysis, Critique and Advancement El-Khatib, Mayar January 2010 (has links) While decision-making under uncertainty is a major universal problem, its implications in the field of transportation systems are especially enormous; where the benefits of right decisions are tremendous, the consequences of wrong ones are potentially disastrous. In the realm of highway systems, decisions related to the highway configuration (number of lanes, right of way, etc.) need to incorporate both the traffic demand and land price uncertainties. In the literature, these uncertainties have generally been modeled using the Geometric Brownian Motion (GBM) process, which has been used extensively in modeling many other real life phenomena. But few scholars, including those who used the GBM in highway configuration decisions, have offered any rigorous justification for the use of this model. This thesis attempts to offer a detailed analysis of various aspects of transportation systems in relation to decision-making. It reveals some general insights as well as a new concept that extends the notion of opportunity cost to situations where wrong decisions could be made. Claiming deficiency of the GBM model, it also introduces a new formulation that utilizes a large and flexible parametric family of jump models (i.e., Lévy processes). To validate this claim, data related to traffic demand and land prices were collected and analyzed to reveal that their distributions, heavy-tailed and asymmetric, do not match well with the GBM model. As a remedy, this research used the Merton, Kou, and negative inverse Gaussian Lévy processes as possible alternatives. Though the results show indifference in relation to final decisions among the models, mathematically, they improve the precision of uncertainty models and the decision-making process. This furthers the quest for optimality in highway projects and beyond. highway development decision making under uncertainty real options jump processes infinitely divisible distributions Lévy processes measure theory characteristic function characteristic exponent Lévy jump measure probability measure cádlág stochastic process Wiener process Poisson process compound Poisson process finite activity models jump diffusion models Merton model Gaussian jump process Kou model double exponential jump process leptokurtic volatility smile infinite activity models pure jumps process generalized hyperbolic model modified Bessel function negative inverse Gaussian model geometric Brownian motion model log-normal distribution model normality assumption heavy-tailed distribution asymmetric distribution quantile-quantile plot Rydberg algorithm parameter calibration moments cummulants sum of squares Monte Carlo simulation least-squares regression backward dynamic programming discrete-time Markov chain land price traffic demand highway service quality index data collection traffic volume Annual Average Daily Traffic Ontario Ministry of Transportation Land Registry Office Freedom of Information Act seasonality value of transportation types of transportation systems importance of highway systems cost of wrong decisions monetary cost private sector cost human cost land expropriation environmental cost irreversibility cost factor time cost factor political cost factor opportunity cost opportunity cost of wrong decisions economic cost of wrong decisions economic cost of mis-estimation economic profit of wrong decisions cost of inaction price of making right decisions scope of decisions rehabilitation decision land acquisition decision expansion decision uncertainty modeling land acquisition cost expropriation cost construction cost material cost Montréal-Mirabel International Airport Pickering Airport 407 Express Toll Route Highway 407 Statistics

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