O uso de redes bayesianas para modelagem e simulação dos fatores de risco no transporte rodoviário de cargas
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da Uninove |
Texto Completo: | http://bibliotecatede.uninove.br/handle/tede/2214 |
Resumo: | In recent years, quantitative risk analysis in Road Freight Transport (TFR) has been successfully applied in studies to assess the risks to which the chemical industries and other segments give rise. Recently, relevant studies have shown that transporting different modes (on roads, tracks, pipelines and inland waterways) of hazardous materials plays an important role in determining risk. In particular, with regard to road transport carried out by trucks, as it is an important modality for economic development and very common for the handling of various types of cargo, to assess the level of risk of a given activity, it is necessary to determine the severity of index of that risk for each situation that may occur during TFR. In this context, the purpose of this research is to investigate and calculate the risks related to the activity of road freight transport through Bayesian Networks. These models can estimate different risk scenarios in the TFR activity, with a view to allowing greater assertiveness in measuring the level of risk. The computational models were implemented in software from Bayesian Networks and data entry was performed in Excel® spreadsheets establishing a simplified use interface. The methodology adopted for this study is field research based on the participation of specialists and academic sources, as well as the use of a systematic literature review, the application of the Delphi technique and, finally, a Survey. The results showed that through the use of the proposed model, it was possible to have greater assertiveness in choosing the best scenario for carrying out the TFR activity, since it is also possible to identify whether a given scenario can be classified as low, medium or high degree of risk. Thus, the risk prediction method for CRT makes it possible to assess the probability of the occurrence of one or more risk factors during its activity. In the end, the proposed approach contributes to a better understanding of the probability of the most recurrent risk factors in TFR. |
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Librantz, Andre Felipe HenriquesOliveira Neto, Geraldo Cardoso deLibrantz, Andre Felipe HenriquesGonçalves, Rodrigo FrancoLucato, Wagner Cezarhttp://lattes.cnpq.br/9304206257581804Teles, Helbert Barbosa2020-07-31T19:03:11Z2020-03-04Teles, Helbert Barbosa. O uso de redes bayesianas para modelagem e simulação dos fatores de risco no transporte rodoviário de cargas. 2020. 131 f. Dissertação( Programa de Mestrado em Engenharia de Produção) - Universidade Nove de Julho, São Paulo.http://bibliotecatede.uninove.br/handle/tede/2214In recent years, quantitative risk analysis in Road Freight Transport (TFR) has been successfully applied in studies to assess the risks to which the chemical industries and other segments give rise. Recently, relevant studies have shown that transporting different modes (on roads, tracks, pipelines and inland waterways) of hazardous materials plays an important role in determining risk. In particular, with regard to road transport carried out by trucks, as it is an important modality for economic development and very common for the handling of various types of cargo, to assess the level of risk of a given activity, it is necessary to determine the severity of index of that risk for each situation that may occur during TFR. In this context, the purpose of this research is to investigate and calculate the risks related to the activity of road freight transport through Bayesian Networks. These models can estimate different risk scenarios in the TFR activity, with a view to allowing greater assertiveness in measuring the level of risk. The computational models were implemented in software from Bayesian Networks and data entry was performed in Excel® spreadsheets establishing a simplified use interface. The methodology adopted for this study is field research based on the participation of specialists and academic sources, as well as the use of a systematic literature review, the application of the Delphi technique and, finally, a Survey. The results showed that through the use of the proposed model, it was possible to have greater assertiveness in choosing the best scenario for carrying out the TFR activity, since it is also possible to identify whether a given scenario can be classified as low, medium or high degree of risk. Thus, the risk prediction method for CRT makes it possible to assess the probability of the occurrence of one or more risk factors during its activity. In the end, the proposed approach contributes to a better understanding of the probability of the most recurrent risk factors in TFR.Nos últimos anos, a análise quantitativa de riscos no Transporte Rodoviário de Cargas (TRC), tem sido aplicada com sucesso em estudos para avaliar os riscos aos quais as indústrias químicas e outros segmentos dão origem. Recentemente, estudos relevantes mostraram que o transporte em diferentes modais (nas estradas, trilhos, oleodutos e vias navegáveis interiores) de materiais perigosos desempenha um papel importante na determinação do risco. Em especial, no que concerne ao transporte rodoviário realizado por caminhões, por ser um modal importante para o desenvolvimento econômico e muito comum para a movimentação de vários tipos de carga, para avaliar o nível de risco de uma determinada atividade é necessário determinar a gravidade do índice desse risco para cada situação que possa ocorrer durante o TRC. Neste contexto, o propósito desta pesquisa é investigar e calcular os riscos relacionados à atividade do transporte rodoviário de cargas por meio de Redes Bayesianas. Estes modelos podem estimar diferentes cenários de risco na atividade do TRC, com vistas a permitir maior assertividade na aferição do nível de risco. Os modelos computacionais foram implementados em softwares de Redes Bayesianas e a entrada de dados foi realizada em planilhas Excel ® estabelecendo uma interface de uso simplificado. A metodologia adotada para este estudo é a pesquisa de campo fundamentada por meio da participação de especialistas e fontes acadêmicas, bem como, a utilização de uma revisão sistemática da literatura, a aplicação da técnica Delphi e, por último, uma Survey. Os resultados apontaram que por meio da utilização do modelo proposto, foi possível ter maior assertividade na escolha do melhor cenário para a realização da atividade do TRC, uma vez que é possível também, identificar se um determinado cenário pode ser classificado com baixo, médio ou alto grau de risco. Assim, o método de predição de risco para o TRC possibilita avaliar a probabilidade de ocorrência de um ou mais fatores de risco durante a sua atividade. Ao final, a abordagem proposta contribui para uma melhor compreensão a respeito da probabilidade dos fatores de risco mais recorrentes no TRC.Submitted by Nadir Basilio (nadirsb@uninove.br) on 2020-07-31T19:03:11Z No. of bitstreams: 1 Helbert Teles.pdf: 3983249 bytes, checksum: e5207f37d46f1adf3683d6ba426e6a78 (MD5)Made available in DSpace on 2020-07-31T19:03:11Z (GMT). No. of bitstreams: 1 Helbert Teles.pdf: 3983249 bytes, checksum: e5207f37d46f1adf3683d6ba426e6a78 (MD5) Previous issue date: 2020-03-04application/pdfporUniversidade Nove de JulhoPrograma de Pós-Graduação de Mestrado e Doutorado em Engenharia de ProduçãoUNINOVEBrasilEngenhariaredes bayesianasfatores de riscotransporte rodoviário de cargamodeling and simulationbayesian networksrisk managementrisk factorsroad freight transportENGENHARIAS::ENGENHARIA DE PRODUCAOO uso de redes bayesianas para modelagem e simulação dos fatores de risco no transporte rodoviário de cargasThe use of Bayesian networks for modeling and simulation of risk factors in road cargo transportinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis2551182063231974631600info:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da Uninoveinstname:Universidade Nove de Julho (UNINOVE)instacron:UNINOVEORIGINALHelbert Teles.pdfHelbert Teles.pdfapplication/pdf3983249http://localhost:8080/tede/bitstream/tede/2214/2/Helbert+Teles.pdfe5207f37d46f1adf3683d6ba426e6a78MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82165http://localhost:8080/tede/bitstream/tede/2214/1/license.txtbd3efa91386c1718a7f26a329fdcb468MD51tede/22142021-10-08 18:38:54.971oai:localhost:tede/2214Tk9UQTogQ09MT1FVRSBBUVVJIEEgU1VBIFBSw5NQUklBIExJQ0VOw4dBCkVzdGEgbGljZW7Dp2EgZGUgZXhlbXBsbyDDqSBmb3JuZWNpZGEgYXBlbmFzIHBhcmEgZmlucyBpbmZvcm1hdGl2b3MuCgpMSUNFTsOHQSBERSBESVNUUklCVUnDh8ODTyBOw4NPLUVYQ0xVU0lWQQoKQ29tIGEgYXByZXNlbnRhw6fDo28gZGVzdGEgbGljZW7Dp2EsIHZvY8OqIChvIGF1dG9yIChlcykgb3UgbyB0aXR1bGFyIGRvcyBkaXJlaXRvcyBkZSBhdXRvcikgY29uY2VkZSDDoCBVbml2ZXJzaWRhZGUgClhYWCAoU2lnbGEgZGEgVW5pdmVyc2lkYWRlKSBvIGRpcmVpdG8gbsOjby1leGNsdXNpdm8gZGUgcmVwcm9kdXppciwgIHRyYWR1emlyIChjb25mb3JtZSBkZWZpbmlkbyBhYmFpeG8pLCBlL291IApkaXN0cmlidWlyIGEgc3VhIHRlc2Ugb3UgZGlzc2VydGHDp8OjbyAoaW5jbHVpbmRvIG8gcmVzdW1vKSBwb3IgdG9kbyBvIG11bmRvIG5vIGZvcm1hdG8gaW1wcmVzc28gZSBlbGV0csO0bmljbyBlIAplbSBxdWFscXVlciBtZWlvLCBpbmNsdWluZG8gb3MgZm9ybWF0b3Mgw6F1ZGlvIG91IHbDrWRlby4KClZvY8OqIGNvbmNvcmRhIHF1ZSBhIFNpZ2xhIGRlIFVuaXZlcnNpZGFkZSBwb2RlLCBzZW0gYWx0ZXJhciBvIGNvbnRlw7pkbywgdHJhbnNwb3IgYSBzdWEgdGVzZSBvdSBkaXNzZXJ0YcOnw6NvIApwYXJhIHF1YWxxdWVyIG1laW8gb3UgZm9ybWF0byBwYXJhIGZpbnMgZGUgcHJlc2VydmHDp8Ojby4KClZvY8OqIHRhbWLDqW0gY29uY29yZGEgcXVlIGEgU2lnbGEgZGUgVW5pdmVyc2lkYWRlIHBvZGUgbWFudGVyIG1haXMgZGUgdW1hIGPDs3BpYSBhIHN1YSB0ZXNlIG91IApkaXNzZXJ0YcOnw6NvIHBhcmEgZmlucyBkZSBzZWd1cmFuw6dhLCBiYWNrLXVwIGUgcHJlc2VydmHDp8Ojby4KClZvY8OqIGRlY2xhcmEgcXVlIGEgc3VhIHRlc2Ugb3UgZGlzc2VydGHDp8OjbyDDqSBvcmlnaW5hbCBlIHF1ZSB2b2PDqiB0ZW0gbyBwb2RlciBkZSBjb25jZWRlciBvcyBkaXJlaXRvcyBjb250aWRvcyAKbmVzdGEgbGljZW7Dp2EuIFZvY8OqIHRhbWLDqW0gZGVjbGFyYSBxdWUgbyBkZXDDs3NpdG8gZGEgc3VhIHRlc2Ugb3UgZGlzc2VydGHDp8OjbyBuw6NvLCBxdWUgc2VqYSBkZSBzZXUgCmNvbmhlY2ltZW50bywgaW5mcmluZ2UgZGlyZWl0b3MgYXV0b3JhaXMgZGUgbmluZ3XDqW0uCgpDYXNvIGEgc3VhIHRlc2Ugb3UgZGlzc2VydGHDp8OjbyBjb250ZW5oYSBtYXRlcmlhbCBxdWUgdm9jw6ogbsOjbyBwb3NzdWkgYSB0aXR1bGFyaWRhZGUgZG9zIGRpcmVpdG9zIGF1dG9yYWlzLCB2b2PDqiAKZGVjbGFyYSBxdWUgb2J0ZXZlIGEgcGVybWlzc8OjbyBpcnJlc3RyaXRhIGRvIGRldGVudG9yIGRvcyBkaXJlaXRvcyBhdXRvcmFpcyBwYXJhIGNvbmNlZGVyIMOgIFNpZ2xhIGRlIFVuaXZlcnNpZGFkZSAKb3MgZGlyZWl0b3MgYXByZXNlbnRhZG9zIG5lc3RhIGxpY2Vuw6dhLCBlIHF1ZSBlc3NlIG1hdGVyaWFsIGRlIHByb3ByaWVkYWRlIGRlIHRlcmNlaXJvcyBlc3TDoSBjbGFyYW1lbnRlIAppZGVudGlmaWNhZG8gZSByZWNvbmhlY2lkbyBubyB0ZXh0byBvdSBubyBjb250ZcO6ZG8gZGEgdGVzZSBvdSBkaXNzZXJ0YcOnw6NvIG9yYSBkZXBvc2l0YWRhLgoKQ0FTTyBBIFRFU0UgT1UgRElTU0VSVEHDh8ODTyBPUkEgREVQT1NJVEFEQSBURU5IQSBTSURPIFJFU1VMVEFETyBERSBVTSBQQVRST0PDjU5JTyBPVSAKQVBPSU8gREUgVU1BIEFHw4pOQ0lBIERFIEZPTUVOVE8gT1UgT1VUUk8gT1JHQU5JU01PIFFVRSBOw4NPIFNFSkEgQSBTSUdMQSBERSAKVU5JVkVSU0lEQURFLCBWT0PDiiBERUNMQVJBIFFVRSBSRVNQRUlUT1UgVE9ET1MgRSBRVUFJU1FVRVIgRElSRUlUT1MgREUgUkVWSVPDg08gQ09NTyAKVEFNQsOJTSBBUyBERU1BSVMgT0JSSUdBw4fDlUVTIEVYSUdJREFTIFBPUiBDT05UUkFUTyBPVSBBQ09SRE8uCgpBIFNpZ2xhIGRlIFVuaXZlcnNpZGFkZSBzZSBjb21wcm9tZXRlIGEgaWRlbnRpZmljYXIgY2xhcmFtZW50ZSBvIHNldSBub21lIChzKSBvdSBvKHMpIG5vbWUocykgZG8ocykgCmRldGVudG9yKGVzKSBkb3MgZGlyZWl0b3MgYXV0b3JhaXMgZGEgdGVzZSBvdSBkaXNzZXJ0YcOnw6NvLCBlIG7Do28gZmFyw6EgcXVhbHF1ZXIgYWx0ZXJhw6fDo28sIGFsw6ltIGRhcXVlbGFzIApjb25jZWRpZGFzIHBvciBlc3RhIGxpY2Vuw6dhLgo=Biblioteca Digital de Teses e Dissertaçõeshttp://bibliotecatede.uninove.br/PRIhttp://bibliotecatede.uninove.br/oai/requestbibliotecatede@uninove.br||bibliotecatede@uninove.bropendoar:2021-10-08T21:38:54Biblioteca Digital de Teses e Dissertações da Uninove - Universidade Nove de Julho (UNINOVE)false |
dc.title.por.fl_str_mv |
O uso de redes bayesianas para modelagem e simulação dos fatores de risco no transporte rodoviário de cargas |
dc.title.alternative.eng.fl_str_mv |
The use of Bayesian networks for modeling and simulation of risk factors in road cargo transport |
title |
O uso de redes bayesianas para modelagem e simulação dos fatores de risco no transporte rodoviário de cargas |
spellingShingle |
O uso de redes bayesianas para modelagem e simulação dos fatores de risco no transporte rodoviário de cargas Teles, Helbert Barbosa redes bayesianas fatores de risco transporte rodoviário de carga modeling and simulation bayesian networks risk management risk factors road freight transport ENGENHARIAS::ENGENHARIA DE PRODUCAO |
title_short |
O uso de redes bayesianas para modelagem e simulação dos fatores de risco no transporte rodoviário de cargas |
title_full |
O uso de redes bayesianas para modelagem e simulação dos fatores de risco no transporte rodoviário de cargas |
title_fullStr |
O uso de redes bayesianas para modelagem e simulação dos fatores de risco no transporte rodoviário de cargas |
title_full_unstemmed |
O uso de redes bayesianas para modelagem e simulação dos fatores de risco no transporte rodoviário de cargas |
title_sort |
O uso de redes bayesianas para modelagem e simulação dos fatores de risco no transporte rodoviário de cargas |
author |
Teles, Helbert Barbosa |
author_facet |
Teles, Helbert Barbosa |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Librantz, Andre Felipe Henriques |
dc.contributor.advisor-co1.fl_str_mv |
Oliveira Neto, Geraldo Cardoso de |
dc.contributor.referee1.fl_str_mv |
Librantz, Andre Felipe Henriques |
dc.contributor.referee2.fl_str_mv |
Gonçalves, Rodrigo Franco |
dc.contributor.referee3.fl_str_mv |
Lucato, Wagner Cezar |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/9304206257581804 |
dc.contributor.author.fl_str_mv |
Teles, Helbert Barbosa |
contributor_str_mv |
Librantz, Andre Felipe Henriques Oliveira Neto, Geraldo Cardoso de Librantz, Andre Felipe Henriques Gonçalves, Rodrigo Franco Lucato, Wagner Cezar |
dc.subject.por.fl_str_mv |
redes bayesianas fatores de risco transporte rodoviário de carga |
topic |
redes bayesianas fatores de risco transporte rodoviário de carga modeling and simulation bayesian networks risk management risk factors road freight transport ENGENHARIAS::ENGENHARIA DE PRODUCAO |
dc.subject.eng.fl_str_mv |
modeling and simulation bayesian networks risk management risk factors road freight transport |
dc.subject.cnpq.fl_str_mv |
ENGENHARIAS::ENGENHARIA DE PRODUCAO |
description |
In recent years, quantitative risk analysis in Road Freight Transport (TFR) has been successfully applied in studies to assess the risks to which the chemical industries and other segments give rise. Recently, relevant studies have shown that transporting different modes (on roads, tracks, pipelines and inland waterways) of hazardous materials plays an important role in determining risk. In particular, with regard to road transport carried out by trucks, as it is an important modality for economic development and very common for the handling of various types of cargo, to assess the level of risk of a given activity, it is necessary to determine the severity of index of that risk for each situation that may occur during TFR. In this context, the purpose of this research is to investigate and calculate the risks related to the activity of road freight transport through Bayesian Networks. These models can estimate different risk scenarios in the TFR activity, with a view to allowing greater assertiveness in measuring the level of risk. The computational models were implemented in software from Bayesian Networks and data entry was performed in Excel® spreadsheets establishing a simplified use interface. The methodology adopted for this study is field research based on the participation of specialists and academic sources, as well as the use of a systematic literature review, the application of the Delphi technique and, finally, a Survey. The results showed that through the use of the proposed model, it was possible to have greater assertiveness in choosing the best scenario for carrying out the TFR activity, since it is also possible to identify whether a given scenario can be classified as low, medium or high degree of risk. Thus, the risk prediction method for CRT makes it possible to assess the probability of the occurrence of one or more risk factors during its activity. In the end, the proposed approach contributes to a better understanding of the probability of the most recurrent risk factors in TFR. |
publishDate |
2020 |
dc.date.accessioned.fl_str_mv |
2020-07-31T19:03:11Z |
dc.date.issued.fl_str_mv |
2020-03-04 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
Teles, Helbert Barbosa. O uso de redes bayesianas para modelagem e simulação dos fatores de risco no transporte rodoviário de cargas. 2020. 131 f. Dissertação( Programa de Mestrado em Engenharia de Produção) - Universidade Nove de Julho, São Paulo. |
dc.identifier.uri.fl_str_mv |
http://bibliotecatede.uninove.br/handle/tede/2214 |
identifier_str_mv |
Teles, Helbert Barbosa. O uso de redes bayesianas para modelagem e simulação dos fatores de risco no transporte rodoviário de cargas. 2020. 131 f. Dissertação( Programa de Mestrado em Engenharia de Produção) - Universidade Nove de Julho, São Paulo. |
url |
http://bibliotecatede.uninove.br/handle/tede/2214 |
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por |
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2551182063231974631 |
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openAccess |
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Universidade Nove de Julho |
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Programa de Pós-Graduação de Mestrado e Doutorado em Engenharia de Produção |
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UNINOVE |
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Brasil |
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Engenharia |
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Universidade Nove de Julho |
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