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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 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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- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1038/s41598-023-41902-y
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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Scientific Reports
no. 13,
ISSN: 2045-2322 - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Sroczyński A., Czyżewski A.: Road traffic can be predicted by machine learning equally effectively as by complex microscopic model// Scientific Reports -Vol. 13, (2023), s.14523-
- DOI:
- Digital Object Identifier (open in new tab) 10.1038/s41598-023-41902-y
- Sources of funding:
-
- publikacja za 140 pkt. finansowana ze środków centralnych PG
- Verified by:
- Gdańsk University of Technology
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