STUDY OF METHODS FOR PREDICTING THE RECEIPT OF CONTAINER FLOWS FROM SHIPPERS TO THE RAILWAY TERMINAL STATION

Authors

DOI:

https://doi.org/10.30888/2663-5712.2022-16-01-042

Keywords:

railway terminal stations, road transport, container flows.

Abstract

The paper considers a tool, a neural network of the LTSM architecture, which is quite appropriate to use for forecasting the intensity of random flows, which represent the processes of the arrival of containers at railway terminal stations in the system

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References

Butko T., Prokhorov V., Kolisnyk A., Parkhomenko L. Devising an automated technology to organize the railroad transportation of containers for intermodal deliveries based on the theory of point. Eastern-European journal of enterprise technologies. 2020. Vol. 1, № 3 (103). P.6–12. DOI: 10.15587/1729-4061.2019.156098

Hochreiter S., Schmidhuber, J. Long short-term memory. Neural Computation. 1997. № 9 (8). P. 1735–1780.

LeCun Y., Bengio Y., Hinton G. Deep learning. Nature. 2015. № 521 (7553). P. 436–444.

Pokrovskaya M. A. A method for predicting traffic changes using a neural network model. Telecommunications and Transport. 2012. №6. С. 27–30.

Published

2023-01-23

How to Cite

Берестов, І., Раківненко, В., Кириченко, О., Колісник, А., & Щебликіна, О. (2023). STUDY OF METHODS FOR PREDICTING THE RECEIPT OF CONTAINER FLOWS FROM SHIPPERS TO THE RAILWAY TERMINAL STATION. SWorldJournal, 1(16-01), 40–45. https://doi.org/10.30888/2663-5712.2022-16-01-042

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Section

Articles