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Features of the use of BIG DATA in the study of freight traffic on railway transport
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FEATURES OF THE USE OF BIG DATA IN THE STUDY OF FREIGHT TRAFFIC ON RAILWAY TRANSPORT
UDK 656.021.5 Вместимость или грузоподъемность транспортных средств
Yarmolinskiy Fedor 1
Pokrovskaya Oksana 2
Pasechnik Ekaterina 3
Pakulina Elena 4
Trapeznikov Aleksandr 5
Authors:
1.  Emperor Alexander I St. Petersburg State Transport University (Postgraduate student of the Department “Operational Work Management”)

Russian Federation
2.  Emperor Alexander I St. Petersburg State Transport University (the Department “Operational Work Management”, Head of Department)

3.  Emperor Alexander I St. Petersburg State Transport University (Department of Business Informatics, Assistant)

Russian Federation
4.  Emperor Alexander I St. Petersburg State Transport University (Kafedra «Nazemnye transportno-tehnologicheskie kompleksy», Assistant)

Russian Federation
5.  Military Institute of Railway Troops and Military Communications of the VA MTO named after Army General A. V. Khrulev (1st department (organization of daily activities and security of military service), Lecturer)

Russian Federation
Type:
Article
DOI:
https://doi.org/10.20295/2223-9987-2024-01-107-122
Pages:
from 107 to 122
Status:
Published
Received:
02.04.2024
Accepted:
02.04.2024
Published:
02.04.2024
Subject area:
UDK 656.021.5 Вместимость или грузоподъемность транспортных средств
Language:
Russian
Keywords:
big data, cargo flows, predictive analytics, digitalization, forecast, neural network model
Abstract and keywords
Abstract (English):
Objective: to characterize the features and prospects of using big data tools and technologies in the management of the transportation process on railways. Methods: neural network modeling, system analysis, forecasting, programming, big data, predictive analytics. Results: a datalogical model of entities for storing up-to-date data on cargo flows is proposed, and a structure for building a system for accumulating information is proposed. In addition, the paper examines the applied issues of solving the problems of storing, receiving and processing data using big data methods. Practical significance: Improving the management of railway transportation processes in the context of digital transformation in terms of obtaining more accurate forecasts.

Keywords:
big data, cargo flows, predictive analytics, digitalization, forecast, neural network model
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References

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