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Submitted by Mariana Dias Chaves null (mariana.unesp@hotmail.com) on 2018-02-16T19:43:53Z No. of bitstreams: 1 Batistao, MDC-TeseDr.pdf: 6348711 bytes, checksum: 0f1b9c7f3392530f6d2f279ee0b58768 (MD5) / Approved for entry into archive by Claudia Adriana Spindola null (claudia@fct.unesp.br) on 2018-02-19T11:31:34Z (GMT) No. of bitstreams: 1 batistao_mdc_dr_prud.pdf: 6348711 bytes, checksum: 0f1b9c7f3392530f6d2f279ee0b58768 (MD5) / Made available in DSpace on 2018-02-19T11:31:34Z (GMT). No. of bitstreams: 1 batistao_mdc_dr_prud.pdf: 6348711 bytes, checksum: 0f1b9c7f3392530f6d2f279ee0b58768 (MD5) Previous issue date: 2018-02-02 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Essa pesquisa apresenta um estudo acerca dos fatores contribuintes de acidentes rodoviários com o objetivo de fornecer evidências para analisar o comportamento dos fatores contribuintes envolvidos nesses acidentes, mais especificamente nos trechos críticos. Desejase identificar a relação dos fatores com o grau de severidade de um acidente (danos materiais, sem vítimas fatais e com vítimas fatais) e o impacto de cada classe de fator na ocorrência de um acidente. A intensão é embasar uma análise geoespacial levando em consideração técnicas estatísticas e cartográficas e contribuir para melhorar a qualidade das informações sobre segurança viária no país e seu atual cenário crítico. A estrutura metodológica da pesquisa consiste em três etapas principais: (I) Identificação e determinação de segmentos de trechos críticos, (II) Mapeamento dos fatores contribuintes “via” para o acidente e (III) Investigação e estudo dos fatores contribuintes. Quatro trechos de rodovias do oeste do estado de São Paulo foram escolhidos como área de estudo. Na etapa I propôs-se um método de interpolação espacial de escolha de segmentos de trechos críticos levando a premissa existência da dependência geográfica dos acidentes em consideração. No total, foram identificados oito segmentos de trechos críticos na área de estudo. A etapa II concentrou-se no mapeamento dos fatores contribuintes desses segmentos de trechos críticos. Essa etapa trouxe o caráter tecnológico à pesquisa por fazer uso da integração de geotecnologias e a contribuição das Ciências Cartográficas para os estudos de segurança viária, por gerar informação a partir do mapeamento da localização dos fatores contribuintes. Das quatro classes de fatores (humano, ambiente, veículo e via) as características da via foram escolhidas para serem mapeadas, tendo-se deparado com a ausência de qualquer dado dessa classe de fatores tanto no banco de dados dos acidentes como no boletim de ocorrências. A relação com as outras três classes de fatores foi tratada na etapa III da pesquisa, cujos resultados proporcionaram montar o ranking dos seis fatores contribuintes da via mais frequentes nos segmentos de trechos críticos. Adicionalmente, foram construídos três modelos de regressão logística ordinal para investigar o impacto de cada uma das quatro classes de fatores no grau de severidade do acidente (três graus de severidade). Para isso, o grau foi tratado como variável dependente dos modelos. Quatro variáveis independentes (fatores contribuintes) foram consideradas significativas e escolhidas para compor os modelos: consumo de drogas (da classe de fator contribuinte humano), estado dos pneus (da classe de fator veículo), vegetação (da classe de fator via) e sinalização (da classe de fator via). Por fim, os modelos puderam ser analisados a partir da razão de chances (odds ratio) para complementar as informações e sintetizar os resultados como contribuições da pesquisa. / This research presents a study about the contributing factors of road accidents in order to provide evidences to analyse the behaviour of contributing factors involved in these accidents more specifically in critical sections. The intention is to identify the relationship between those factors and the severity degree of an accident (material damage, no fatalities and fatalities) and the impact of each factor class on an accident occurrence. The aim is to base on geospatial analysis taking into account statistical and cartographic techniques and contribute to improve the quality of the road safety information in the country which has a current critical scene. The methodological structure of this thesis consists of following three main steps: (I) Identification and determination of critical sections segments, (II) mapping “road” contributing factors for each accident and (III) Investigation and study of the contributing factors. Four sections of highways in the west of São Paulo state were chosen as the study area. In Step I, proposed a spatial interpolation method to choose critical sections segments premising the existence of geographical dependence of the considered accidents. In entire, eight critical sections segments were identified in the study area. Step II focused on mapping the contributing factors of these segments. This step brought the technological character to this research by making use of geotechnologies integration and the contribution of Cartographic Sciences to road safety by generating information of the contributing factors location from mapping. Of the four factors classes (human, environment, vehicle and road), the road characteristics were chosen to be mapped, since no data from this factor class was found in both the accident database and the occurrence report. The relation with the other three factors classes was the subject of step III, which results provided a ranking of the six most frequent contributing factors in critical sections segments. In addition, three ordinal logistic regression models were constructed to investigate the impact of each of the four factors classes on the accident severity degree (three severity degrees). For this, the severity degree was considered as the models dependent variable. Four significant independent variables (contributing factors) were chosen to compose the following models: drug consumption (from the human contributing factor class), tire condition (vehicle factor class), vegetation (road factor class) and signaling (road factor class). Lastly, the models could be analysed by the odds ratio method to complement the information and synthesize the results as research contributions.

The prediction of a company's financial health is of critical importance to a variety of stakeholders ranging from auditors, creditors, customers, employees, financial institutions and investors through to management. There has been considerable research in this field, ranging from the univariate dichotomous approach of Beaver (1966) to the multivariate multi-state approaches of Lau (1987) and Ward (1994). All of the South African studies namely, Strebel and Andrews (1977), Daya (1977), De La Rey (1981), Clarke et al (1991) and Court et al (1999), and even, Lukhwareni's (2005) four separate models, were dichotomous in nature providing either a "Healthy" or a "Failed" state; or a "Winner" or "Loser" as in the latter case. Notwithstanding, all of these models would be classified as first stage, initial screening models. This study has focused on following a two stage approach to identifying (first stage) and analysing (second stage) the States of Health in a company. It has not adopted the rigid "Healthy" or "Failed" dichotomous methodology. For the first stage, three-state models were developed classifying a company as Healthy, Intermittent or Distressed. Both three year and five year Profit after Tax (PAT) averages for Real Earnings Growth (REG) calculations were used to determine the superior definition for the Intermittent state; with the latter coming out as superior. Models were developed for the current year (Yn), one (Yn-1), two (Yn-2) and three years (Yn-3) forward using a Test sample of twenty companies and their predictive accuracy determined by using a Holdout sample of twenty-two companies and all their data points or years of information. The statistical methods employed were a Naïve model using the simple Shareholder Value Added (SVA) ratio, CHAID and MDA, with the latter providing very disappointing results - for the Yn year (five year average), the Test sample results were 100%, 95% and 95%, respectively; with the Holdout sample results being 81.3%, 83.8% and 52.5%, respectively. The Yn-1 to Yn-3 models produced very good results for the Test sample but somewhat disappointing Holdout sample results. The best two Yn models namely, the Naïve and the CHAID models, were modified so as to enable a comparison with the notable, dichotomous De La Rey (1981) model. As such, three different approaches were adopted and in all cases, both the modified Naïve (100%, 81.3%, 100%) and the modified CHAID (100%, 85.9%, 98%) produced superior results to the De La Rey model (84.8%, 62.6%, 75.3%). For the second stage, a Financial Risk Analysis Model (FRAM) using ratios in the categories of Growth, Performance Analysis, Investment Analysis and Financial Status were used to provide underlying information or clues, independent of the first stage model, so as to enable the stakeholder to establish a more meaningful picture of the company. This would pave the way for the appropriate strategy and course of action to be followed, to take the company to the next level; whether it be taking the company out of a Distressed State (D) or further improving on its Healthy status (H). / Business Management / D. BL.

The prediction of a company's financial health is of critical importance to a variety of stakeholders ranging from auditors, creditors, customers, employees, financial institutions and investors through to management. There has been considerable research in this field, ranging from the univariate dichotomous approach of Beaver (1966) to the multivariate multi-state approaches of Lau (1987) and Ward (1994). All of the South African studies namely, Strebel and Andrews (1977), Daya (1977), De La Rey (1981), Clarke et al (1991) and Court et al (1999), and even, Lukhwareni's (2005) four separate models, were dichotomous in nature providing either a "Healthy" or a "Failed" state; or a "Winner" or "Loser" as in the latter case. Notwithstanding, all of these models would be classified as first stage, initial screening models. This study has focused on following a two stage approach to identifying (first stage) and analysing (second stage) the States of Health in a company. It has not adopted the rigid "Healthy" or "Failed" dichotomous methodology. For the first stage, three-state models were developed classifying a company as Healthy, Intermittent or Distressed. Both three year and five year Profit after Tax (PAT) averages for Real Earnings Growth (REG) calculations were used to determine the superior definition for the Intermittent state; with the latter coming out as superior. Models were developed for the current year (Yn), one (Yn-1), two (Yn-2) and three years (Yn-3) forward using a Test sample of twenty companies and their predictive accuracy determined by using a Holdout sample of twenty-two companies and all their data points or years of information. The statistical methods employed were a Naïve model using the simple Shareholder Value Added (SVA) ratio, CHAID and MDA, with the latter providing very disappointing results - for the Yn year (five year average), the Test sample results were 100%, 95% and 95%, respectively; with the Holdout sample results being 81.3%, 83.8% and 52.5%, respectively. The Yn-1 to Yn-3 models produced very good results for the Test sample but somewhat disappointing Holdout sample results. The best two Yn models namely, the Naïve and the CHAID models, were modified so as to enable a comparison with the notable, dichotomous De La Rey (1981) model. As such, three different approaches were adopted and in all cases, both the modified Naïve (100%, 81.3%, 100%) and the modified CHAID (100%, 85.9%, 98%) produced superior results to the De La Rey model (84.8%, 62.6%, 75.3%). For the second stage, a Financial Risk Analysis Model (FRAM) using ratios in the categories of Growth, Performance Analysis, Investment Analysis and Financial Status were used to provide underlying information or clues, independent of the first stage model, so as to enable the stakeholder to establish a more meaningful picture of the company. This would pave the way for the appropriate strategy and course of action to be followed, to take the company to the next level; whether it be taking the company out of a Distressed State (D) or further improving on its Healthy status (H). / Business Management / D. BL.