<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-06-19T13:31:33Z</responseDate><request verb="GetRecord" identifier="oai:riubu.ubu.es:10259/7017" metadataPrefix="etdms">https://riubu.ubu.es/oai/request</request><GetRecord><record><header><identifier>oai:riubu.ubu.es:10259/7017</identifier><datestamp>2024-05-20T09:45:58Z</datestamp><setSpec>com_10259.4_104</setSpec><setSpec>com_10259_2604</setSpec><setSpec>col_10259_6848</setSpec></header><metadata><thesis xmlns="http://www.ndltd.org/standards/metadata/etdms/1.0/" xmlns:doc="http://www.lyncode.com/xoai" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.ndltd.org/standards/metadata/etdms/1.0/ http://www.ndltd.org/standards/metadata/etdms/1.0/etdms.xsd">
<title>Supervised machine learning algorithms for measuring and promoting sustainable transportation and green logistics</title>
<creator>Castaneda, Juliana</creator>
<creator>Cardona, John F.</creator>
<creator>Martins, Leandro do C.</creator>
<creator>Juan, Angel A.</creator>
<subject>Sostenibilidad</subject>
<subject>Transporte sostenible</subject>
<subject>Sustainability</subject>
<subject>Sustainable transport</subject>
<description>Trabajo presentado en: R-Evolucionando el transporte, XIV Congreso de Ingeniería del Transporte (CIT 2021), realizado en modalidad online los días 6, 7 y 8 de julio de 2021, organizado por la Universidad de Burgos</description>
<description>The sustainable development of freight transport has received much attention in recent&#xd;
years. The new regulations for sustainable transport activities established by the European&#xd;
Commission and the United Nations have created the need for road freight transport&#xd;
companies to develop methodologies to measure the social and environmental impact of&#xd;
their activities. This work aims to develop a model based on supervised machine learning&#xd;
methods with intelligent classification algorithms and key performance indicators for each&#xd;
dimension of sustainability as input data. This model allows establishing the level of&#xd;
sustainability (high, medium or low). Several classification algorithms were trained,&#xd;
finding that the support vector machines algorithm is the most accurate, with 98% accuracy&#xd;
for the data set used. The model is tested by establishing the level of sustainability of a&#xd;
European company in the road freight sector, thus allowing the establishment of green&#xd;
strategies for its sustainable development.</description>
<date>2022-09-22</date>
<date>2022-09-22</date>
<date>2021-07</date>
<type>info:eu-repo/semantics/conferenceObject</type>
<identifier>978-84-18465-12-3</identifier>
<identifier>http://hdl.handle.net/10259/7017</identifier>
<identifier>10.36443/10259/7017</identifier>
<language>eng</language>
<relation>R-Evolucionando el transporte</relation>
<relation>http://hdl.handle.net/10259/6490</relation>
<relation>https://doi.org/10.36443/9788418465123</relation>
<relation>info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-111100RB-C21/ES/ALGORITMOS AGILES, INTERNET DE LAS COSAS, Y ANALITICA DE DATOS PARA UN TRANSPORTE SOSTENIBLE EN CIUDADES INTELIGENTES</relation>
<relation>info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RED2018-102642-T/ES/RED ESPAÑOLA EN TRANSPORTE SOSTENIBLE E INTELIGENTE</relation>
<relation>info:eu-repo/grantAgreement/EC/Erasmus+/2019-I-ES01-KA103-062602</relation>
<rights>info:eu-repo/semantics/openAccess</rights>
<publisher>Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional</publisher>
</thesis></metadata></record></GetRecord></OAI-PMH>