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Road traffic can be predicted by machine learning equally effectively as by complex microscopic model.
- Source :
-
Scientific Reports . 9/4/2023, Vol. 13 Issue 1, p1-15. 15p. - Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MACHINE learning
*TRAFFIC signs & signals
*TRAFFIC engineering
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 13
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Scientific Reports
- Publication Type :
- Academic Journal
- Accession number :
- 171363886
- Full Text :
- https://doi.org/10.1038/s41598-023-41902-y