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García Navalón, Alvaro, Fonseca González, Natalia Elizabeth ORCID: https://orcid.org/0000-0001-8065-7261, Mira Mcwilliams, José Manuel
ORCID: https://orcid.org/0000-0001-6105-8714 and Mera Rosero, Zamir Andrés
(2019).
Modelling urban bus fleet emissions with machine learning boosting methods: City of Madrid.
In: "Proceedings of the 23rd Transport and Air Pollution (TAP) conference 2019", 15-17 Mayo 2019, Tesalónica, Grecia. ISBN 978-92-76-17328-1. p. 788.
https://doi.org/10.2760/289885.
Title: | Modelling urban bus fleet emissions with machine learning boosting methods: City of Madrid |
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Author/s: |
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Item Type: | Presentation at Congress or Conference (Poster) |
Event Title: | Proceedings of the 23rd Transport and Air Pollution (TAP) conference 2019 |
Event Dates: | 15-17 Mayo 2019 |
Event Location: | Tesalónica, Grecia |
Title of Book: | Proceedings of the 23rd Transport and Air Pollution (TAP) conference 2019 |
Date: | 2019 |
ISBN: | 978-92-76-17328-1 |
Subjects: | |
Freetext Keywords: | emissions models; urban bus; boosting; machine learning |
Faculty: | E.T.S.I. de Minas y Energía (UPM) |
Department: | Energía y Combustibles |
Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
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Boosting is a machine learning methodology which consists in an ensemble (set) of similar models estimated from the same data set. It is an iterative and cumulative algorithm intended to minimize the error of a single "weak" model. The purpose of this work is to assess the applicability of this technique to the modelling and prediction of instantaneous emissions of urban buses in the city of Madrid.
Item ID: | 65127 |
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DC Identifier: | https://oa.upm.es/65127/ |
OAI Identifier: | oai:oa.upm.es:65127 |
DOI: | 10.2760/289885 |
Official URL: | https://ec.europa.eu/jrc/en/publication/proceeding... |
Deposited by: | Memoria Investigacion |
Deposited on: | 04 Nov 2020 08:11 |
Last Modified: | 04 Nov 2020 08:11 |