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Doctoral Thesis
DOI
10.11606/T.18.2018.tde-14032018-115127
Document
Author
Full name
Antonio Nilder Duarte Furtado
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 1998
Supervisor
Committee
Kawamoto, Eiji (President)
Carvalho, André Carlos Ponce de Leon Ferreira de
Rabbani, Simin Jalali Rahnemay
Sanches, Suely da Penha
Setti, José Reynaldo Anselmo
Title in Portuguese
Uma nova abordagem na avaliação de projetos de transporte: o uso das redes neurais artificiais como técnica para avaliar e ordenar alternativas
Keywords in Portuguese
Avaliação de projetos de transporte
Inteligência artificial (IA)
Métodos e técnicas de avaliação
Redes neurais artificiais (RNA)
Tomada de decisão
Abstract in Portuguese
Esta tese apresenta um estudo para a utilização de Redes Neurais Artificiais (RNA) no processo de avaliação e ordenamento de alternativas de projetos de transporte. Partindo-se da ideia de que esse processo constitui-se em um padrão que pode ser captado pelas RNA, a verificação deste argumento foi feita selecionando-se um contexto de avaliação, definindo-se variáveis a serem consideradas no processo de avaliação, e criando-se estruturas de RNA para treinamento com base em outras avaliações já realizadas. Nesta pesquisa foram utilizados 180 "Estudos de Casos" recebidos de 32 Estados americanos. Esses dados serviram de entrada para um processo de aprendizagem utilizando-se o simulador "Neural Planner 4.52", que baseia-se em redes "Multilayer Perceptron (MLP)" e no treinamento em "Backpropagation". Várias redes foram treinadas para que fosse definida aquela com um melhor desempenho para o reconhecimento dos padrões existentes nesses casos apresentados. Os 486 experimentos demonstraram índices de acertos superiores a 92% que podem ser visualizados no programa computacional denominado "EVALUATOR", uma interface entre o simulador de RNA e usuários. Conclui-se, portanto, que as RNA podem reconhecer os padrões implícitos em avaliações anteriores e servem para avaliar e ordenar alternativas de outros projetos apresentados que pertençam ao mesmo contexto utilizado para treinamento.
Title in English
A new approach in transportation project evaluation: using artificial neural networks as a technique for appraising and ranking alternatives
Keywords in English
Artificial intelligence
Artificial neural networks
Decision-making
Evaluation methods and techniques
Transportation project evaluation
Abstract in English
This thesis presents a research aimed at the use of Artificial Neural Networks (ANN) for appraising and ranking transportation project alternatives. Based on the principle that this process of appraisal and ranking constitutes a pattern that can be perceived by ANN, the verification of this hypothesis was conducted selecting an evaluation context, defining variables to be considered in the process, and creating ANN structures for training based on other evaluation cases. In this research, 180 "Case Studies" from 32 American states were used. These data were used as input to a learning process using the simulator "Neural Planner 4.52", which is based on "Multilayer Perceptron (MLP)" networks and uses a "Backpropagation" training algorithm. Several networks were trained to obtain the one most capable of recognizing the patterns of the projects analyzed. More than 92% of the 486 experiments presented right indexes, as shown by a software called "EVALUATOR", a user interface between ANN simulator. The conclusion is that ANN can recognize the implicit patterns in previous evaluations and can be used to appraise and rank alternatives from other projects belonging to the same context used for the ANN training.
 
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Publishing Date
2018-03-16
 
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