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

Andrzej Sroczyński; Andrzej Czyżewski

doi:10.1038/s41598-023-41902-y

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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Authors (2)

Andrzej Sroczyński mgr inż.
- SzDW
Andrzej Czyżewski prof. dr hab. inż.

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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:

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 -,iss. 13, 14523 (2023), s.1-15

DOI: