RT info:eu-repo/semantics/conferenceObject
T1 Supervised machine learning algorithms for measuring and promoting sustainable transportation and green logistics
A1 Castaneda, Juliana
A1 Cardona, John F.
A1 Martins, Leandro do C.
A1 Juan, Angel A.
K1 Sostenibilidad
K1 Sustainability
K1 Transporte sostenible
K1 Sustainable transport
K1 Ingeniería civil
K1 Civil engineering
K1 Transporte
K1 Transportation
K1 Matemáticas
K1 Mathematics
AB The sustainable development of freight transport has received much attention in recentyears. The new regulations for sustainable transport activities established by the EuropeanCommission and the United Nations have created the need for road freight transportcompanies to develop methodologies to measure the social and environmental impact oftheir activities. This work aims to develop a model based on supervised machine learningmethods with intelligent classification algorithms and key performance indicators for eachdimension of sustainability as input data. This model allows establishing the level ofsustainability (high, medium or low). Several classification algorithms were trained,finding that the support vector machines algorithm is the most accurate, with 98% accuracyfor the data set used. The model is tested by establishing the level of sustainability of aEuropean company in the road freight sector, thus allowing the establishment of greenstrategies for its sustainable development.
PB Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional
SN 978-84-18465-12-3
YR 2021
FD 2021-07
LK http://hdl.handle.net/10259/7017
UL http://hdl.handle.net/10259/7017
LA eng
NO 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
NO This work has been partially supported by the Spanish Ministry of Science (PID2019- 111100RB-C21 / AEI /10.13039/501100011033, RED2018-102642-T), and the Erasmus+ Program (2019-I-ES01-KA103-062602).
DS Repositorio Institucional de la Universidad de Burgos
RD 29-abr-2024