Road traffic can be predicted by machine learning equally effectively as by complex microscopic model
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-41902-y |
| _version_ | 1797576866714877952 |
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| author | Andrzej Sroczyński Andrzej Czyżewski |
| author_facet | Andrzej Sroczyński Andrzej Czyżewski |
| author_sort | Andrzej Sroczyński |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-10T21:59:49Z |
| format | Article |
| id | doaj.art-dc363dc884e34b71ac9ecaaa3834bf84 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T21:59:49Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-dc363dc884e34b71ac9ecaaa3834bf842023-11-19T13:00:26ZengNature PortfolioScientific Reports2045-23222023-09-0113111510.1038/s41598-023-41902-yRoad traffic can be predicted by machine learning equally effectively as by complex microscopic modelAndrzej Sroczyński0Andrzej Czyżewski1Multimedia Systems Department, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of TechnologyMultimedia Systems Department, Faculty of Electronics, Telecommunication and Informatics, Gdansk University of TechnologyAbstract 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.https://doi.org/10.1038/s41598-023-41902-y |
| spellingShingle | Andrzej Sroczyński Andrzej Czyżewski Road traffic can be predicted by machine learning equally effectively as by complex microscopic model Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-023-41902-y |
| work_keys_str_mv | AT andrzejsroczynski roadtrafficcanbepredictedbymachinelearningequallyeffectivelyasbycomplexmicroscopicmodel AT andrzejczyzewski roadtrafficcanbepredictedbymachinelearningequallyeffectivelyasbycomplexmicroscopicmodel |