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Modelling urban bus fleet emissions with machine learning boosting methods: City of Madrid
Citation
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.
Description
| Title: | Modelling urban bus fleet emissions with machine learning boosting methods: City of Madrid |
|---|---|
| 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 |
Abstract
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.
Funding Projects
Type
Code
Acronym
Leader
Title
Government of Spain
TRA2015-68803-R
Unspecified
Unspecified
Optimization system for urban driving cycles, application to the generation of patterns adapted to environmental requirements and vehicle fleet exploitation situations
More information
| 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 |
