Modelling urban bus fleet emissions with machine learning boosting methods: City of Madrid

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:
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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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
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
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