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Road traffic can be predicted by machine learning equally effectively as by complex microscopic model

Since high-quality real data acquired from selected road sections are not always available, a traffic control solution can use data from software traffic simulators working offline. The results show that in contrast to microscopic traffic simulation, the algorithms employing neural networks can work...

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Detalles Bibliográficos
Autores principales: Sroczyński, Andrzej, Czyżewski, Andrzej
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10477175/
https://www.ncbi.nlm.nih.gov/pubmed/37666950
http://dx.doi.org/10.1038/s41598-023-41902-y
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author Sroczyński, Andrzej
Czyżewski, Andrzej
author_facet Sroczyński, Andrzej
Czyżewski, Andrzej
author_sort Sroczyński, Andrzej
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description Since high-quality real data acquired from selected road sections are not always available, a traffic control solution can use data from software traffic simulators working offline. The results show that in contrast to microscopic traffic simulation, the algorithms employing neural networks can work in real-time, so they can be used, among others, to determine the speed displayed on variable message road signs. This paper describes an experiment to develop and test machine learning models, i.e., long short-term memory, gated recurrent unit recurrent networks, and stacked autoencoder networks. It compares their effectiveness with traffic prediction results generated using a widely recognized traffic simulator that analyzes traffic at the level of individual vehicles.
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spelling pubmed-104771752023-09-06 Road traffic can be predicted by machine learning equally effectively as by complex microscopic model Sroczyński, Andrzej Czyżewski, Andrzej Sci Rep Article Since high-quality real data acquired from selected road sections are not always available, a traffic control solution can use data from software traffic simulators working offline. The results show that in contrast to microscopic traffic simulation, the algorithms employing neural networks can work in real-time, so they can be used, among others, to determine the speed displayed on variable message road signs. This paper describes an experiment to develop and test machine learning models, i.e., long short-term memory, gated recurrent unit recurrent networks, and stacked autoencoder networks. It compares their effectiveness with traffic prediction results generated using a widely recognized traffic simulator that analyzes traffic at the level of individual vehicles. Nature Publishing Group UK 2023-09-04 /pmc/articles/PMC10477175/ /pubmed/37666950 http://dx.doi.org/10.1038/s41598-023-41902-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Sroczyński, Andrzej
Czyżewski, Andrzej
Road traffic can be predicted by machine learning equally effectively as by complex microscopic model
title Road traffic can be predicted by machine learning equally effectively as by complex microscopic model
title_full Road traffic can be predicted by machine learning equally effectively as by complex microscopic model
title_fullStr Road traffic can be predicted by machine learning equally effectively as by complex microscopic model
title_full_unstemmed Road traffic can be predicted by machine learning equally effectively as by complex microscopic model
title_short Road traffic can be predicted by machine learning equally effectively as by complex microscopic model
title_sort road traffic can be predicted by machine learning equally effectively as by complex microscopic model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10477175/
https://www.ncbi.nlm.nih.gov/pubmed/37666950
http://dx.doi.org/10.1038/s41598-023-41902-y
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