Road network and GPS tracking with data processing and quality assessment

GPS technology has been embedded into portable, low-cost electronic devices nowadays to track the movements of mobile objects. This implication has greatly impacted the transportation field by creating a novel and rich source of traffic data on the road network. Although the promise offered by GPS devices to overcome problems like underreporting, respondent fatigue, inaccuracies and other human errors in data collection is significant; the technology is still relatively new that it raises many issues for potential users. These issues tend to revolve around the following areas: reliability, data processing and the related application. This thesis aims to study the GPS tracking form the methodological, technical and practical aspects. It first evaluates the reliability of GPS based traffic data based on data from an experiment containing three different traffic modes (car, bike and bus) traveling along the road network. It then outline the general procedure for processing GPS tracking data and discuss related issues that are uncovered by using real-world GPS tracking data of 316 cars. Thirdly, it investigates the influence of road network density in finding optimal location for enhancing travel efficiency and decreasing travel cost. The results show that the geographical positioning is reliable. Velocity is slightly underestimated, whereas altitude measurements are unreliable.Post processing techniques with auxiliary information is found necessary and important when solving the inaccuracy of GPS data. The densities of the road network influence the finding of optimal locations. The influence will stabilize at a certain level and do not deteriorate when the node density is higher.

How do different densities in a network affect the optimal location of service centers?

The p-median problem is often used to locate p service centers by minimizing their distances to a geographically distributed demand (n). The optimal locations are sensitive to geographical context such as road network and demand points especially when they are asymmetrically distributed in the plane. Most studies focus on evaluating performances of the p-median model when p and n vary. To our knowledge this is not a very well-studied problem when the road network is alternated especially when it is applied in a real world context. The aim in this study is to analyze how the optimal location solutions vary, using the p-median model, when the density in the road network is alternated. The investigation is conducted by the means of a case study in a region in Sweden with an asymmetrically distributed population (15,000 weighted demand points), Dalecarlia. To locate 5 to 50 service centers we use the national transport administrations official road network (NVDB). The road network consists of 1.5 million nodes. To find the optimal location we start with 500 candidate nodes in the network and increase the number of candidate nodes in steps up to 67,000. To find the optimal solution we use a simulated annealing algorithm with adaptive tuning of the temperature. The results show that there is a limited improvement in the optimal solutions when nodes in the road network increase and p is low. When p is high the improvements are larger. The results also show that choice of the best network depends on p. The larger p the larger density of the network is needed. 

Does road network density matter in optimally locating facilities?

Optimal location on the transport infrastructure is the preferable requirement for many decision making processes. Most studies have focused on evaluating performances of optimally locate p facilities by minimizing their distances to a geographically distributed demand (n) when p and n vary. The optimal locations are also sensitive to geographical context such as road network, especially when they are asymmetrically distributed in the plane. The influence of alternating road network density is however not a very well-studied problem especially when it is applied in a real world context. This paper aims to investigate how the density level of the road network affects finding optimal location by solving the specific case of p-median location problem. A denser network is found needed when a higher number of facilities are to locate. The best solution will not always be obtained in the most detailed network but in a middle density level. The solutions do not further improve or improve insignificantly as the density exceeds 12,000 nodes, some solutions even deteriorate. The hierarchy of the different densities of network can be used according to location and transportation purposes and increase the efficiency of heuristic methods. The method in this study can be applied to other location-allocation problem in transportation analysis where the road network density can be differentiated. 

Machine Learning Algorithms in Heavy Process Manufacturing

In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.

Case studies for contract-based systems

Of the ways in which agent behaviour can be regulated in a multiagent system, electronic contracting – based on explicit representation of different parties' responsibilities, and the agreement of all parties to them – has significant potential for modern industrial applications. Based on this assumption, the CONTRACT project aims to develop and apply electronic contracting and contract-based monitoring and verification techniques in real world applications. This paper presents results from the initial phase of the project, which focused on requirements solicitation and analysis. Specifically, we survey four use cases from diverse industrial applications, examine how they can benefit from an agent-based electronic contracting infrastructure and outline the technical requirements that would be placed on such an infrastructure. We present the designed CONTRACT architecture and describe how it may fulfil these requirements. In addition to motivating our work on the contract-based infrastructure, the paper aims to provide a much needed community resource in terms of use case themselves and to provide a clear commercial context for the development of work on contract-based system.

Evaluating And Optimizing Sustainable Drainage Design To Maximize Multiple Benefits: Case Studies In China

In the past, the focus of drainage design was on sizing pipes and storages in order to provide sufficient network capacity. This traditional approach, together with computer software and technical guidance, had been successful for many years. However, due to rapid population growth and urbanisation, the requirements of a “good” drainage design have also changed significantly. In addition to water management, other aspects such as environmental impacts, amenity values and carbon footprint have to be considered during the design process. Going forward, we need to address the key sustainability issues carefully and practically. The key challenge of moving from simple objectives (e.g. capacity and costs) to complicated objectives (e.g. capacity, flood risk, environment, amenity etc) is the difficulty to strike a balance between various objectives and to justify potential benefits and compromises. In order to assist decision makers, we developed a new decision support system for drainage design. The system consists of two main components – a multi-criteria evaluation framework for drainage systems and a multi-objective optimisation tool. The evaluation framework is used for the quantification of performance, life-cycle costs and benefits of different drainage systems. The optimisation tool can search for feasible combinations of design parameters such as the sizes, order and type of drainage components that maximise multiple benefits. In this paper, we will discuss real-world application of the decision support system. A number of case studies have been developed based on recent drainage projects in China. We will use the case studies to illustrate how the evaluation framework highlights and compares the pros and cons of various design options. We will also discuss how the design parameters can be optimised based on the preferences of decision makers. The work described here is the output of an EngD project funded by EPSRC and XP Solutions.

Multi-Objective Pipe Smoothing Genetic Algorithm For Water Distribution Network Design

This paper describes the formulation of a Multi-objective Pipe Smoothing Genetic Algorithm (MOPSGA) and its application to the least cost water distribution network design problem. Evolutionary Algorithms have been widely utilised for the optimisation of both theoretical and real-world non-linear optimisation problems, including water system design and maintenance problems. In this work we present a pipe smoothing based approach to the creation and mutation of chromosomes which utilises engineering expertise with the view to increasing the performance of the algorithm whilst promoting engineering feasibility within the population of solutions. MOPSGA is based upon the standard Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and incorporates a modified population initialiser and mutation operator which directly targets elements of a network with the aim to increase network smoothness (in terms of progression from one diameter to the next) using network element awareness and an elementary heuristic. The pipe smoothing heuristic used in this algorithm is based upon a fundamental principle employed by water system engineers when designing water distribution pipe networks where the diameter of any pipe is never greater than the sum of the diameters of the pipes directly upstream resulting in the transition from large to small diameters from source to the extremities of the network. MOPSGA is assessed on a number of water distribution network benchmarks from the literature including some real-world based, large scale systems. The performance of MOPSGA is directly compared to that of NSGA-II with regard to solution quality, engineering feasibility (network smoothness) and computational efficiency. MOPSGA is shown to promote both engineering and hydraulic feasibility whilst attaining good infrastructure costs compared to NSGA-II.

Improving mutual fund market timing measures: a markov switching approach

Market timing performance of mutual funds is usually evaluated with linear models with dummy variables which allow for the beta coefficient of CAPM to vary across two regimes: bullish and bearish market excess returns. Managers, however, use their predictions of the state of nature to deÞne whether to carry low or high beta portfolios instead of the observed ones. Our approach here is to take this into account and model market timing as a switching regime in a way similar to Hamilton s Markov-switching GNP model. We then build a measure of market timing success and apply it to simulated and real world data.

A estrutura subcognitiva da visão estratégica : um experimento psicológico sobre a natureza dos Chunks

How strategical decisions are taken? The present work consists of a psychological experiment that it aims to search the knowledge about the subcognitive structure of strategical vision of deeper form, investigating its interaction with the cognitives processes of human beings ¿ perception, memory, and learning. We also argue the nature of chunks (pieces or units), that, in opposition to the current theories, we consider to be provided with essence or meanings in detriment of the appearance or superficial features. In this way, we choose as domain for our experiment the chess game, because its dealing with lesser complexity of the one that decisions in the politics or industry. Thus, we shows the importance that the perception of the abstract roles playing in specific chess position, leading to a strategical vision of this. Moreover, after the experiment, was verified that the expert chess players are capable to perceive distinct positions in the appearance as being similar strategically", while that the beginners had gotten greater difficulty. Finally, we present part of an emergent theory that claims that the human being cognition is nothing more than the abstract perception, as well as the replication of this theory in other domains, for example in the management and the real world."

Uso da análise discriminante regularizada (RDA) no reconhecimento de padrões em imagens digitais hiperespectral de sensoriamento remoto

Em cenas naturais, ocorrem com certa freqüência classes espectralmente muito similares, isto é, os vetores média são muito próximos. Em situações como esta, dados de baixa dimensionalidade (LandSat-TM, Spot) não permitem uma classificação acurada da cena. Por outro lado, sabe-se que dados em alta dimensionalidade [FUK 90] tornam possível a separação destas classes, desde que as matrizes covariância sejam suficientemente distintas. Neste caso, o problema de natureza prática que surge é o da estimação dos parâmetros que caracterizam a distribuição de cada classe. Na medida em que a dimensionalidade dos dados cresce, aumenta o número de parâmetros a serem estimados, especialmente na matriz covariância. Contudo, é sabido que, no mundo real, a quantidade de amostras de treinamento disponíveis, é freqüentemente muito limitada, ocasionando problemas na estimação dos parâmetros necessários ao classificador, degradando portanto a acurácia do processo de classificação, na medida em que a dimensionalidade dos dados aumenta. O Efeito de Hughes, como é chamado este fenômeno, já é bem conhecido no meio científico, e estudos vêm sendo realizados com o objetivo de mitigar este efeito. Entre as alternativas propostas com a finalidade de mitigar o Efeito de Hughes, encontram-se as técnicas de regularização da matriz covariância. Deste modo, técnicas de regularização para a estimação da matriz covariância das classes, tornam-se um tópico interessante de estudo, bem como o comportamento destas técnicas em ambientes de dados de imagens digitais de alta dimensionalidade em sensoriamento remoto, como por exemplo, os dados fornecidos pelo sensor AVIRIS. Neste estudo, é feita uma contextualização em sensoriamento remoto, descrito o sistema sensor AVIRIS, os princípios da análise discriminante linear (LDA), quadrática (QDA) e regularizada (RDA) são apresentados, bem como os experimentos práticos dos métodos, usando dados reais do sensor. Os resultados mostram que, com um número limitado de amostras de treinamento, as técnicas de regularização da matriz covariância foram eficientes em reduzir o Efeito de Hughes. Quanto à acurácia, em alguns casos o modelo quadrático continua sendo o melhor, apesar do Efeito de Hughes, e em outros casos o método de regularização é superior, além de suavizar este efeito. Esta dissertação está organizada da seguinte maneira: No primeiro capítulo é feita uma introdução aos temas: sensoriamento remoto (radiação eletromagnética, espectro eletromagnético, bandas espectrais, assinatura espectral), são também descritos os conceitos, funcionamento do sensor hiperespectral AVIRIS, e os conceitos básicos de reconhecimento de padrões e da abordagem estatística. No segundo capítulo, é feita uma revisão bibliográfica sobre os problemas associados à dimensionalidade dos dados, à descrição das técnicas paramétricas citadas anteriormente, aos métodos de QDA, LDA e RDA, e testes realizados com outros tipos de dados e seus resultados.O terceiro capítulo versa sobre a metodologia que será utilizada nos dados hiperespectrais disponíveis. O quarto capítulo apresenta os testes e experimentos da Análise Discriminante Regularizada (RDA) em imagens hiperespectrais obtidos pelo sensor AVIRIS. No quinto capítulo são apresentados as conclusões e análise final. A contribuição científica deste estudo, relaciona-se à utilização de métodos de regularização da matriz covariância, originalmente propostos por Friedman [FRI 89] para classificação de dados em alta dimensionalidade (dados sintéticos, dados de enologia), para o caso especifico de dados de sensoriamento remoto em alta dimensionalidade (imagens hiperespectrais). A conclusão principal desta dissertação é que o método RDA é útil no processo de classificação de imagens com dados em alta dimensionalidade e classes com características espectrais muito próximas.

Essays on regulation and risk

In this thesis, we investigate some aspects of the interplay between economic regulation and the risk of the regulated firm. In the first chapter, the main goal is to understand the implications a mainstream regulatory model (Laffont and Tirole, 1993) have on the systematic risk of the firm. We generalize the model in order to incorporate aggregate risk, and find that the optimal regulatory contract must be severely constrained in order to reproduce real-world systematic risk levels. We also consider the optimal profit-sharing mechanism, with an endogenous sharing rate, to explore the relationship between contract power and beta. We find results compatible with the available evidence that high-powered regimes impose more risk to the firm. In the second chapter, a joint work with Daniel Lima from the University of California, San Diego (UCSD), we start from the observation that regulated firms are subject to some regulatory practices that potentially affect the symmetry of the distribution of their future profits. If these practices are anticipated by investors in the stock market, the pattern of asymmetry in the empirical distribution of stock returns may differ among regulated and non-regulated companies. We review some recently proposed asymmetry measures that are robust to the empirical regularities of return data and use them to investigate whether there are meaningful differences in the distribution of asymmetry between these two groups of companies. In the third and last chapter, three different approaches to the capital asset pricing model of Kraus and Litzenberger (1976) are tested with recent Brazilian data and estimated using the generalized method of moments (GMM) as a unifying procedure. We find that ex-post stock returns generally exhibit statistically significant coskewness with the market portfolio, and hence are sensitive to squared market returns. However, while the theoretical ground for the preference for skewness is well established and fairly intuitive, we did not find supporting evidence that investors require a premium for supporting this risk factor in Brazil.

Uma resenha sobre modelos de apreçamento de derivativos

Apresento aqui uma abordagem que unifica a literatura sobre os vários modelos de apreçamento de derivativos que consiste em obter por argumentos intuitivos de não arbitragem uma Equação Diferencial Parcial(EDP) e através do método de Feynman-Kac uma solução que é representada por uma esperança condicional de um processo markoviano do preço do derivativo descontado pela taxa livre de risco. Por este resultado, temos que a esperança deve ser tomada com relação a processos que crescem à taxa livre de risco e por este motivo dizemos que a esperança é tomada em um mundo neutro ao risco(ou medida neutra ao risco). Apresento ainda como realizar uma mudança de medida pertinente que conecta o mundo real ao mundo neutro ao risco e que o elemento chave para essa mudança de medida é o preço de mercado dos fatores de risco. No caso de mercado completo o preço de mercado do fator de risco é único e no caso de mercados incompletos existe uma variedade de preços aceitáveis para os fatores de risco pelo argumento de não arbitragem. Neste último caso, os preços de mercado são geralmente escolhidos de forma a calibrar o modelo com os dados de mercado.

Uma técnica para modelagem de cenários consistentes na previsão da demanda por energia elétrica

As técnicas qualitativas disponiveis para a modelagem de cenários têm sido reconhecidas pela extrema limitação, evidenciada no principio das atividades do processo, como a fase inicial de concepção. As principais restrições têm sido: • inexistência de uma ferramenta que teste a consistência estrutural interna do modelo, ou pela utilização de relações econômicas com fundamentação teórica mas sem interface perfeita com o ambiente, ou pela adoção de variações binárias para testes de validação; • fixação "a priori" dos possíveis cenários, geralmente classificados sob três adjetivos - otimista, mais provável e pessimista - enviesados exatamente pelos atributos das pessoas que fornecem esta informação. o trabalho trata da utilização de uma ferramenta para a interação entre uma técnica que auxilia a geração de modelos, suportada pela lógica relacional com variações a quatro valores e expectativas fundamentadas no conhecimento do decisor acerca do mundo real. Tem em vista a construção de um sistema qualitativo de previsão exploratória, no qual os cenários são obtidos por procedimento essencialmente intuitivo e descritivos, para a demanda regional por eletricidade. Este tipo de abordagem - apresentada por J. Gershuny - visa principalmente ao fornecimento de suporte metodológico para a consistência dos cenários gerados qualitativamente. Desenvolvimento e estruturação do modelo são realizados em etapas, partindo-se de uma relação simples e prosseguindo com a inclusão de variáveis e efeitos que melhoram a explicação do modelo. o trabalho apresenta um conjunto de relações para a demanda regional de eletricidade nos principais setores de consumo residencial, comercial e industrial bem como os cenários resultantes das variações mais prováveis das suas componentes exógenas. Ao final conclui-se que esta técnica é útil em modelos que: • incluem variáveis sociais relevantes e de dificil mensuração; • acreditam na importância da consistência externa entre os resultados gerados pelo modelo e aqueles esperados para a tomada de decisões; • atribuem ao decisor a responsabilidade de compreender a fundamentação da estrutura conceitual do modelo. Adotado este procedimento, o autor aqui recomenda que o modelo seja validado através de um procedimento iterativo de ajustes com a participação do decisor. As técnicas quantitativas poderão ser adotadas em seguida, tendo o modelo como elemento de consistência.

Aplicação de opções reais em projetos de automação : um estudo de caso na indústria de cerveja

Projetos de automação industrial são avaliados financeiramente através dos métodos bastante utilizados de Valor Presente Líquido (VPL) e Taxa Interna de Retorno (TIR). Estudos recentes mostram uma tendência para a utilização de outros métodos para realizar esta avaliação. Este trabalho explora o método alternativo de Opções Reais para avaliação desses projetos que busca se aproximar mais do mundo real considerando maior incerteza e flexibilidade do que o VPL. Inicialmente será feita uma revisão de literatura focando o método binomial dentro de Opções Reais. A metodologia proposta é um estudo de caso de um projeto no mercado de cerveja no Brasil, utilizando como fonte uma empresa multinacional de grande porte fornecedora desse projeto. Posteriormente será feita uma análise financeira primeiramente utilizando somente o VPL e posteriormente incorporando o método binomial de Opções Reais, resultando no VPL estendido. O objetivo é precificar a flexibilidade das Opções Reais presentes no contrato de fornecimento do projeto em estudo que normalmente não são consideradas. Com isso pretende-se verificar se o método binomial de Opções Reais faz real diferença no projeto em estudo.

O mercado de opções de Petrobras é eficiente? Um estudo a partir da estratégia delta-gama-neutra

This study aims to verify if the Petrobras options market is efficient in the semi-strong form, that is, if all public information is reflected in these derivative prices. For this purpose, this work tries to achieve profit systematically through the Delta-GammaNeutral strategy using the company's stock and options. In order to simulate the strategy exactly as it would be used in the real world, we built the order books every five minutes considering all buying and selling orders sent to the underlying asset and to the options. We apply the strategy when distortions between implied volatilities extracted from the options are detected. The results show that the Petrobras options market is not efficient, since in 371 day trade strategies, which have an average investment of R$81,000 and average duration of one hour and thirteen minutes, the average return was 0.49% - which corresponds to more than 1,600% of the 1-day risk free interest rate - and 85% of strategies were profitable.