2026-06-19T13:00:17Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/70172024-05-20T09:45:58Zcom_10259.4_104com_10259_2604col_10259_6848
Castaneda, Juliana
Cardona, John F.
Martins, Leandro do C.
Juan, Angel A.
2021-07
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
The sustainable development of freight transport has received much attention in recent
years. The new regulations for sustainable transport activities established by the European
Commission and the United Nations have created the need for road freight transport
companies to develop methodologies to measure the social and environmental impact of
their activities. This work aims to develop a model based on supervised machine learning
methods with intelligent classification algorithms and key performance indicators for each
dimension of sustainability as input data. This model allows establishing the level of
sustainability (high, medium or low). Several classification algorithms were trained,
finding that the support vector machines algorithm is the most accurate, with 98% accuracy
for the data set used. The model is tested by establishing the level of sustainability of a
European company in the road freight sector, thus allowing the establishment of green
strategies for its sustainable development.
application/pdf
http://hdl.handle.net/10259/7017
eng
Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional
Supervised machine learning algorithms for measuring and promoting sustainable transportation and green logistics
info:eu-repo/semantics/conferenceObject
TEXT
RIUBU. Repositorio Institucional de la Universidad de Burgos
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