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One of the significant weaknesses of the design speed concept is that it uses the design speed of the most restrictive geometric element as the design speed of the entire road. This leads to potential inconsistencies among successive sections of a road. Previous studies documented that a uniform design speed does not guarantee consistency on rural two-lane facilities. It is therefore reasonable to assume that similar inconsistencies could be found on rural four-lane non-freeway highways. The operating speed-based method is popularly used in other countries for examining design consistency. Numerous studies have been completed on rural two-lane highways for predicting operating speeds. However, little is known for rural four-lane non-freeway highways. This study aims to develop operating speed prediction models for horizontal curves on rural four-lane non-freeway highways using 74 horizontal curves. The data analysis showed that the operating speeds in each direction of travel had no statistical differences. However, the operating speeds on inside and outside lanes were significantly different. On each of the two lanes, the operating speeds at the beginning, middle, and ending points of the curve were statistically the same. The relationships between operating speed and design speed for inside and outside lanes were different. For the inside lane, the operating speed was statistically equal to the design speed. By contrary, for the outside lane, the operating speed was significantly lower than the design speed. However, the relationships between operating speed and posted speed limit for both inside and outside lanes were similar. It was found that the operating speed was higher than the posted speed limit. Two models were developed for predicting operating speed, since the operating speeds on inside and outside lanes were different. For the inside lane, the significant factors are: shoulder type, median type, pavement type, approaching section grade, and curve length. For the outside lane, the factors included shoulder type, median type, approaching section grade, curve length, curve radius and presence of approaching curve. These factors indicate that the curve itself does mainly influence the drivers speed choice.

With the number of vehicles increasing in the system every day, many statewide policies across the United States aim to increase the use of non- motorized transportation modes. This could have safety implications because the interaction between motorists and non-motorists could increase and potentially increasing pedestrian-vehicle crashes. Few models that predict the number of pedestrian crashes are not sensitive to site-specific conditions or intersection designs that may influence pedestrian crashes. Moreover, traditional statistical modeling techniques rely extensively on the sparsely available pedestrian crash database. This study focused on overcoming these limitations by developing models that quantify potential interactions between pedestrians and vehicles at various intersection designs using as surrogate safety measure the time to conflict. Several variables that capture volumes, intersection geometry, and operational performance were evaluated for developing pedestrian-vehicle conflict models for different intersection designs. Linear regression models were found to be best fit and potential conflict models were developed for signalized, unsignalized and roundabout intersections. Volume transformations were applied to signalized and unsignalized conditions to develop statistical models for unconventional intersections. The pedestrian-vehicle conflicting volumes, the number of lanes that pedestrians are exposed to vehicles, the percentage of turning vehicles, and the intersection conflict location (major or minor approach) were found to be significant predictors for estimating pedestrian safety at signalized and unsignalized intersections. For roundabouts, the pedestrian-vehicle conflicting volumes, the number of lanes that pedestrians have to cross, and the intersection conflict location (major or minor approach) were found to be significant predictors. Signalized intersection models were used for bowtie and median U-turn intersections using appropriate volume transformations. The combination of signalized intersection models for the intersection area and two-way unsignalized intersection models for the ramp area of the jughandle intersections were utilized with appropriate volume transformations. These models can be used to compare alternative intersection designs and provide designers and planners with a surrogate measure of pedestrian safety level for each intersection design examined.

This research aims to identify important factors contributing to a construction firm's profitability and to develop a prediction model which would help in determining the gross margin/profitability of a construction firm as a function of important parameters. All the data used in the research was taken from U.S Census Bureau reports. The novelty of the research lies on its focus at a state level, by dividing states into pertinent clusters and then analyzing the trends in each cluster independently. The research was divided into two phases. Phase 1 of the research focused on identification of the most important factors contributing to gross margin of a construction firm. The variables used were derived from the U.S Census Bureau data. Based on the independent variables and gross margin, all the states were divided into three clusters. Subsequently, a prediction model was developed for each cluster using step-wise backward elimination, thus, eliminating non-significant variables. Results of Model 1 gave impetus to developing Model 2. Model 1 clearly showed that labor productivity was the most important variable in determining gross margin. Model 2 was developed to predict gross margin as a function of single most important factor of labor productivity. Similar to Model 1, states were clustered based on their labor productivity and gross margin values. Prediction model was developed for each cluster. In this study, an excel embedded decision support tool was also developed. This tool would aid the decision-makers to view the state's level of gross margin and labor productivity at a glance. Decision support tool developed was in the form of color-coded maps, each of which was linked to a spreadsheet containing pertinent data. The most important conclusion of the research was that there exists a positive linear relationship between labor productivity and gross margin at a state level in the construction industry. The research also identified and quantified other important factors like percent of rental equipment used, percent of construction work sub-contracted out and percent of cost of materials, components and supplies which affect gross margin.

Este trabalho apresenta um modelo de previsão de acidentes com a participação de motocicletas que foi desenvolvido a partir do método da análise de regressão estatística adaptado às particularidades técnicas das rodovias do Estado do Rio Grande do Sul. O objetivo do trabalho é gerar uma ferramenta que possibilite prever o número de acidentes a partir da combinação do nível de exposição veicular associada com os prováveis fatores de risco deste peculiar ambiente. Com esta finalidade, é confrontado o número de acidentes observados com relação às características técnicas das rodovias investigadas com o intuito de avaliar os fatores de risco. Nove variáveis de controle representando atributos físicos, funcionais, econômicos e legais das rodovias foram analisadas sob diversos parâmetros tais como: largura da plataforma, sinuosidade; inclinação, intersecções, condição do pavimento, tráfego de veículos, tráfego de caminhões, urbanização e dispositivos de controle de tráfego. A aplicação do método estatístico permite classificar as rodovias mais importantes quanto ao nível de acidentes; identificar, mensurar e avaliar os fatores de risco; estimar a probabilidade média para a realização do evento sinistro e simular, em nível de projeto, a ocorrência futura de acidentes. Uma vez processado, o modelo obteve um fator de explicação (R2) para os dados em torno de 96%. As variáveis de controle que apresentaram maior efeito na variável de resposta foram obtidas através do tráfego de veículos seguido da largura da plataforma da rodovia. Após a análise do modelo, as rodovias com maior fator de propensão para acidentes foram a ERS734 sendo seguida pela ERS118 e ERS130. Os resultados que foram obtidos indicaram que a frota de motocicletas do Estado do Rio Grande do Sul - Brasil apresenta um risco de envolvimento em acidentes duas vezes maior que aquela incorrida pela frota dos Estados Unido e três vezes maior que aquela apresentada pela frota do Reino Unido. / This article presents an accident prediction model with the participation of motorcycles, developed by statistical regression analysis adapted to the technical peculiarities of the roads of the state of Rio Grande do Sul, Brazil. The aim of the model is to generate a tool to allow predicting the number of accidents based on the combination of vehicle exposure level with possible risk factors. The number of accidents observed is compared with road technical characteristics, aiming at evaluating risk factors. Nine control variables, representing physical, functional, economical and legal road attributes, were analyzed as to different parameters, such as platform width; sinuosity; inclination; junctions ; pavement condition; vehicle traffic; truck traffic; urbanization; and traffic control devices. The application of the statistical method allows the classification of the most important roads in terms of accident level; to identify, measure, and evaluate risk factors; to estimate mean accident probability; and to simulate, at project level, the future occurrence of accidents. Once processed, the model obtained an explanation factor (R2) for the data around 96%. Vehicle traffic, followed by highway platform width had the highest effect on the response variable. After being analyzed by the model, ERS734, followed by ERS118, and ERS130 presented the highest accident probability factor. The results obtained indicated that the risk of motorcycles being involved in accidents in the state of Rio Grande do Sul is twice as high as in the USA, and three times higher than in the United Kingdom.

Este trabalho apresenta um modelo de previsão de acidentes com a participação de motocicletas que foi desenvolvido a partir do método da análise de regressão estatística adaptado às particularidades técnicas das rodovias do Estado do Rio Grande do Sul. O objetivo do trabalho é gerar uma ferramenta que possibilite prever o número de acidentes a partir da combinação do nível de exposição veicular associada com os prováveis fatores de risco deste peculiar ambiente. Com esta finalidade, é confrontado o número de acidentes observados com relação às características técnicas das rodovias investigadas com o intuito de avaliar os fatores de risco. Nove variáveis de controle representando atributos físicos, funcionais, econômicos e legais das rodovias foram analisadas sob diversos parâmetros tais como: largura da plataforma, sinuosidade; inclinação, intersecções, condição do pavimento, tráfego de veículos, tráfego de caminhões, urbanização e dispositivos de controle de tráfego. A aplicação do método estatístico permite classificar as rodovias mais importantes quanto ao nível de acidentes; identificar, mensurar e avaliar os fatores de risco; estimar a probabilidade média para a realização do evento sinistro e simular, em nível de projeto, a ocorrência futura de acidentes. Uma vez processado, o modelo obteve um fator de explicação (R2) para os dados em torno de 96%. As variáveis de controle que apresentaram maior efeito na variável de resposta foram obtidas através do tráfego de veículos seguido da largura da plataforma da rodovia. Após a análise do modelo, as rodovias com maior fator de propensão para acidentes foram a ERS734 sendo seguida pela ERS118 e ERS130. Os resultados que foram obtidos indicaram que a frota de motocicletas do Estado do Rio Grande do Sul - Brasil apresenta um risco de envolvimento em acidentes duas vezes maior que aquela incorrida pela frota dos Estados Unido e três vezes maior que aquela apresentada pela frota do Reino Unido. / This article presents an accident prediction model with the participation of motorcycles, developed by statistical regression analysis adapted to the technical peculiarities of the roads of the state of Rio Grande do Sul, Brazil. The aim of the model is to generate a tool to allow predicting the number of accidents based on the combination of vehicle exposure level with possible risk factors. The number of accidents observed is compared with road technical characteristics, aiming at evaluating risk factors. Nine control variables, representing physical, functional, economical and legal road attributes, were analyzed as to different parameters, such as platform width; sinuosity; inclination; junctions ; pavement condition; vehicle traffic; truck traffic; urbanization; and traffic control devices. The application of the statistical method allows the classification of the most important roads in terms of accident level; to identify, measure, and evaluate risk factors; to estimate mean accident probability; and to simulate, at project level, the future occurrence of accidents. Once processed, the model obtained an explanation factor (R2) for the data around 96%. Vehicle traffic, followed by highway platform width had the highest effect on the response variable. After being analyzed by the model, ERS734, followed by ERS118, and ERS130 presented the highest accident probability factor. The results obtained indicated that the risk of motorcycles being involved in accidents in the state of Rio Grande do Sul is twice as high as in the USA, and three times higher than in the United Kingdom.

Orientador: Varese Salvador Timóteo / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Tecnologia / Made available in DSpace on 2018-08-19T10:25:36Z (GMT). No. of bitstreams: 1 Oliveira_TitoRicardoBianchin_M.pdf: 4698545 bytes, checksum: a02de15000b11124637f63ed02fe50ee (MD5) Previous issue date: 2011 / Resumo: O crescimento das redes de terceira geração, aliado a sua alta velocidade para transmissão de dados e banda disponível fazem com que novos terminais lançados no mercado utilizem o comportamento Always On, no qual o dispositivo fica 100% do tempo conectado a rede para transmissão e recepção de dados. Esse comportamento, no entanto, faz com que o consumo de bateria do dispositivo seja maior devido ao uso de aplicativos que recebem informações periodicamente, e principalmente pelo recebimento de pacotes não solicitados provenientes de ataques a rede. Este trabalho tem como objetivo analisar os elementos de rede responsáveis pela transmissão de pacotes de dados, identificando os fatores responsáveis pelo aumento de consumo. Ao final, e proposto um método para melhor aproveitamento dos recursos de radio para transmissão de dados e consequentemente, a diminuição do consumo de energia, utilizando um modelo de previsão / Abstract: The expansion of third generation network and its high speed of data transmission and available bandwidth, made that new designed mobile devices use the "Always On" concept, in which the device is 100% connected in packet switch network, and able for data transmission. This behavior makes the device's energy consumption to be higher due to usage of applications that receives periodically information, and mainly due to the unsolicited packages from hacker attack. This work has as main purpose analyze the network elements responsible for data package transmission, identifying the main factors related to the energy consumption increasing. Finally, it is proposed a method to enhance the radio resources for data transmission and energy consumption decreasing, using a prevision model / Mestrado / Tecnologia e Inovação / Mestre em Tecnologia

Please read the abstract in the section 00front of this document / Thesis (PhD)--University of Pretoria, 2009. / Civil Engineering / unrestricted

Le football européen en général et le football français en particulier font état de difficultés financières et de faillites récurrentes de clubs professionnels. Sur la seule période de 1975 à 2018, 81 clubs français de football engagés dans les championnats des quatre premières divisions ont connu une faillite. Le sujet de la faillite d’entreprises a été largement traité pour les secteurs courants de l’économie. De nombreuses recherches se sont attachées à construire des modèles de prédiction, puis progressivement d’autres travaux se sont concentrés sur le processus et les différentes trajectoires d’entrée dans la faillite.Les seuls travaux menés sur le football français ont appliqué le modèle de prédiction d’Altman (2000) sur les clubs de Ligue 1 et de Ligue 2 et ont cherché à identifier les facteurs de la défaillance. Un accès privilégié aux données financières et aux parties prenantes du football français nous a permis de construire un nouveau modèle de prédiction de faillite adapté aux spécificités du football que nous avons ensuite complété par uneanalyse qualitative proposant une hiérarchisation des facteurs explicatifs et leur enchaînement au sein d’un processus dynamique. Notre thèse conclue à l’impossibilité de généraliser un modèle de prédiction des faillites à l’ensemble des clubs des quatre premières divisions françaises. Néanmoins, les améliorations apportées par notre propre modèle permettent de meilleurs taux de classement entre les clubs défaillants et les clubs sains des trois premières divisions. Nous montrons également qu’au-delà d’un score ponctuel obtenu dans le modèle, son évolution dans le temps est un signal important pour identifier et anticiper la dégradation de la situation financière de chaque club. Les clubs ne passent pas d’un état de bonne santé à leur faillite de manière soudaine. Des entretiens menés avec des dirigeants, des actionnaires, des directeurs financiers et des membres de la Direction Nationale du Contrôle de Gestion nous ont permis de modéliser la dynamique globale d’entrée dans la faillite des clubs. Sur ces bases, nous proposons une nouvelle approche de la régulation financière pour mieux prévenir la faillite des clubs de football. / French football and European football in general regularly report of financial difficulties and even bankruptcies of professional clubs. Between 1975 and 2018, 81 clubs of the four premier French divisions went bankrupt. The issue of bankruptcy in business has been widely studied in the main sectors of the economy. Various studies have endeavoured to build prediction models and subsequently, other work has investigated the process and different ways of going bankrupt.The only work which investigated French football applied Altman’s prediction model (2000) to Ligue 1 and Ligue 2 clubs and aimed to identify the factors which lead to bankruptcy. Privileged access to financial information concerning these clubs and to people who have important roles in this domain allowed us tocreate a new model to predict bankruptcy which is adapted to the particularities of professional football. We then completed our study with qualitative analysis of the data and a proposal of a hierarchy of the explicative factors and their sequencing in what is a dynamic process. Our thesis concludes by stating that it is impossible to generalise a bankruptcy prediction model for all theclubs in each of the top four French divisions. Nevertheless, the improvements brought forward by our model allows for a more accurate division of the financially healthy and unhealthy clubs in the first three divisions. Equally, we show that beyond the initial score a club achieves with our model, the evolution of this score over time in an important indicator to help clubs anticipate a worsening financial situation; clubs do not suddenly go from a state of financial solvency to one of bankruptcy. Interviews undertaken with the executives, stakeholders and financial directors of clubs as well as those carried out with members of the Direction Nationale du Contrôle de Gestion (DNCG) allowed us to model the global dynamic for clubs who go bankrupt. From there, we propose a new approach to financial regulation to avoid more football clubs going bankrupt.

In August, 2014, the Financial Accounting Standards Board issued an update concerning the disclosure of uncertainties about an entity’s ability to continue as a going concern. The standard requires an entities management to evaluate whether there is substantial doubt about the entity’s ability to continue as a going concern and to provide related footnote disclosures in certain circumstances. One consequence of this regulation is the need for guidance for audit testing of management’s assessments in each phase of the audit. This research evaluates the usefulness of bankruptcy prediction models as analytical tools in the planning stage of an audit for going concern assertions and questions the use of precision as the only measure of a model’s effectiveness. I use simulation to manipulate the fundamental accounting data within five bankruptcy prediction models, explore failure rates in an environment with materiality concerns, and consider the total change in market value due to simulated errors. Given the inherent limitations of the information environment and/or current prediction models, my results indicate auditors’ current failure rates are not an indication of audit failure. The results suggest that bright-line testing using bankruptcy prediction models are sensitive to materiality and that the cost trade-off between Type I and Type II errors is an important indicator of model choice.