Publication: Aproximación Bayesiana aplicada al Reparto Modal en Modelos de Transporte
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2021-07-26
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Universidad Complutense de Madrid
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Abstract
El trabajo presentado en esta Memoria esta orientado a demostrar que la utilización de las Redes Bayesianas (RB), aplicadas al área de la planificacion de los transportes representa una mejora innovadora comparándola con otras técnicas habitualmente empleadas hasta la fecha como son los modelos Logit y las Redes Neuronales(RN).En concreto, su utilización en la etapa del reparto modal de modelos de demanda de cuatro etapas (generación-atracción, distribución, reparto o elección modal y asignación), que es aquella correspondiente en la que se enfrenta la unidad de decisión dentro de un conjunto discreto de alternativas, tanto para pasajeros como mercancías...
The work showed in this Report is aimed at demonstrating that the use of Bayesian Networks (RB), applied to the area of transport planning, represents an innovative improvement compared to other techniques commonly used to date such as Logit models and Neural Networks (RN). Specifically, its use in the stage of modal split of four-stage demand models (trip generation, distribution, modal split and assignment), which is the one in which the decision unit is faced within a discreet set of alternatives, both for passengers and freight...
The work showed in this Report is aimed at demonstrating that the use of Bayesian Networks (RB), applied to the area of transport planning, represents an innovative improvement compared to other techniques commonly used to date such as Logit models and Neural Networks (RN). Specifically, its use in the stage of modal split of four-stage demand models (trip generation, distribution, modal split and assignment), which is the one in which the decision unit is faced within a discreet set of alternatives, both for passengers and freight...
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Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Matemáticas, leída el 28/01/2021