2026-06-21T14:35:13Zhttps://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.
2022-09-22T09:40:46Z
2022-09-22T09:40:46Z
2021-07
978-84-18465-12-3
http://hdl.handle.net/10259/7017
10.36443/10259/7017
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.
eng
info:eu-repo/semantics/openAccess
Sostenibilidad
Transporte sostenible
Sustainability
Sustainable transport
Supervised machine learning algorithms for measuring and promoting sustainable transportation and green logistics
info:eu-repo/semantics/conferenceObject