2026-04-28T12:44:56Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/66342025-03-24T11:15:41Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848
Repositorio Institucional de la Universidad de Burgos
author
Alonso de Armiño Pérez, Carlos
author
Urda Muñoz, Daniel
author
Alcalde Delgado, Roberto
author
García Pineda, Luis Santiago
author
Herrero Cosío, Álvaro
2022-05-04T13:38:12Z
2022-05-04T13:38:12Z
2022-01
2071-1050
http://hdl.handle.net/10259/6634
10.3390/su14020777
2071-1050
Road transport is an integral part of economic activity and is therefore essential for its development. On the downside, it accounts for 30% of the world’s GHG emissions, almost a third of which correspond to the transport of freight in heavy goods vehicles by road. Additionally, means of transport are still evolving technically and are subject to ever more demanding regulations, which aim to reduce their emissions. In order to analyse the sustainability of this activity, this study proposes the application of novel Artificial Intelligence techniques (more specifically, Machine Learning). In this research, the use of Hybrid Unsupervised Exploratory Plots is broadened with new Exploratory Projection Pursuit techniques. These, together with clustering techniques, form an intelligent visualisation tool that allows knowledge to be obtained from a previously unknown dataset. The proposal is tested with a large dataset from the official survey for road transport in Spain, which was conducted over a period of 7 years. The results obtained are interesting and provide encouraging evidence for the use of this tool as a means of intelligent analysis on the subject of developments in the sustainability of road transportation.
eng
Atribución 4.0 Internacional
Artificial intelligence
Unsupervised machine learning
Exploratory projection pursuit
Clustering
Road transportation
Transport sustainability
Age of transport means
An Intelligent Visualisation Tool to Analyse the Sustainability of Road Transportation
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/6634/1/Alonso-sustainability_2022.pdf
File
MD5
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application/pdf
Alonso-sustainability_2022.pdf