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Bus line classification using neural networks
Cita
Jiménez Alonso, Felipe 
ORCID: https://orcid.org/0000-0002-9532-3835, Serradilla García, Francisco ORCID: https://orcid.org/0000-0001-7621-0627, Román de Andrés, Alfonso and Naranjo Hernández, José Eugenio ORCID: https://orcid.org/0000-0002-4211-9419
(2014).
Bus line classification using neural networks.
"Transportation Research Part D: Transport and Environment", v. 30
(n. null);
pp. 32-37.
ISSN 1361-9209.
https://doi.org/10.1016/j.trd.2014.05.008.
ORCID: https://orcid.org/0000-0002-9532-3835, Serradilla García, Francisco ORCID: https://orcid.org/0000-0001-7621-0627, Román de Andrés, Alfonso and Naranjo Hernández, José Eugenio ORCID: https://orcid.org/0000-0002-4211-9419
(2014).
Bus line classification using neural networks.
"Transportation Research Part D: Transport and Environment", v. 30
(n. null);
pp. 32-37.
ISSN 1361-9209.
https://doi.org/10.1016/j.trd.2014.05.008.
Descripción
| Título: | Bus line classification using neural networks |
|---|---|
| Autor/es: |
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| Tipo de Documento: | Artículo |
| Título de Revista/Publicación: | Transportation Research Part D: Transport and Environment |
| Fecha: | Julio 2014 |
| ISSN: | 1361-9209 |
| Volumen: | 30 |
| Número: | null |
| Materias: | |
| ODS: | |
| Palabras Clave Informales: | Cluster Urban buses Neural network Bootstrap method |
| Escuela: | E.T.S.I. Industriales (UPM) |
| Departamento: | Ingeniería Mecánica y de Fabricación [hasta 2014] |
| Licencias Creative Commons: | Reconocimiento - Sin obra derivada - No comercial |
Resumen
Grouping urban bus routes is necessary when there are evidences of significant differences among them. In Jiménez et al. (2013), a reduced sample of routes was grouped into clusters utilizing kinematic measured data. As a further step, in this paper, the remaining urban bus routes of a city, for which no kinematic measurements are available, are classified. For such purpose we use macroscopic geographical and functional variables to describe each route, while the clustering process is performed by means of a neural network. Limitations caused by reduced training samples are solved using the bootstrap method.
Más información
| ID de Registro: | 32358 |
|---|---|
| Identificador DC: | https://oa.upm.es/32358/ |
| Identificador OAI: | oai:oa.upm.es:32358 |
| URL Portal Científico: | https://portalcientifico.upm.es/es/ipublic/item/5490186 |
| Identificador DOI: | 10.1016/j.trd.2014.05.008 |
| URL Oficial: | http://www.sciencedirect.com/science/article/pii/S... |
| Depositado por: | Memoria Investigacion |
| Depositado el: | 21 Oct 2014 16:19 |
| Ultima Modificación: | 12 Nov 2025 00:00 |
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