Aproximación Bayesiana aplicada al Reparto Modal en Modelos de Transporte

  1. Óscar de Gregorio Vicente
Supervised by:
  1. Beatriz González Pérez Director
  2. Miguel Ángel Gómez Villegas Director

Defence university: Universidad Complutense de Madrid

Year of defence: 2021

Committee:
  1. Rosario Susi García Chair
  2. Isabel Salazar Mendoza Secretary
  3. Rosa Ríos Prado Committee member
  4. Juan Miguel Marín Díazaraque Committee member
  5. Manuel Mendoza Ramirez Committee member
Department:
  1. Estadística e Investigación Operativa

Type: Thesis

Teseo: 155177 DIALNET lock_openE-Prints Complutense editor

Abstract

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...