Submit manuscript
Home / Journals / Bulletin of scientific research results / Volume 2024 Issue 1 / Features of the use of BIG DATA in the study of freight traffic on railway transport
Features of the use of BIG DATA in the study of freight traffic on railway transport
Submit manuscript Download PDF
Text
To cite
Citations:

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
Text
Publication text (PDF): Read Download
References

1. Podhalyuzina V. A. Analiz sostoyaniya zheleznodorozhnogo transporta v Rossii // Ekonomika i socium. 2014. № 2–3 (11) [Elektronnyy resurs]. URL: https://cyberleninka.ru/article/n/ analiz-sostoyaniya-zheleznodorozhnogo-transporta-v-rossii (data obrascheniya: 17.01.2024).

2. Rogushina Yu. V. Razrabotka ontologicheskoy modeli informacionnoy potrebnosti pol'zovatelya pri semanticheskom poiske // Ontologiya proektirovaniya. 2014. № 2 (12) [Elektronnyy resurs]. URL: https://cyberleninka.ru/article/n/razrabotka-ontologicheskoy-modeli-informatsionnoypotrebnosti- polzovatelya-pri-semanticheskom-poiske (data obrascheniya: 17.01.2024).

3. Vlasov A. I., Podorin A. A., Malevanyy A. Yu. i dr. Analiz vizual'nyh modeley tehnologii bol'shih dannyh pri monitoringe perevozochnogo processa na osnove hranilischa reysov gruzovyh vagonov // Sovremennye tehnologii. Sistemnyy analiz. Modelirovanie. 2020. № 3 (67) [Elektronnyy resurs]. URL: https://cyberleninka.ru/article/n/analiz-vizualnyh-modeleytehnologii- bolshih-dannyh-pri-monitoringe-perevozochnogo-protsesa-na-osnove-hranilischareysov- gruzovyh (data obrascheniya: 17.01.2024).

4. Kravchenko V. O., Kryukova A. A. Bol'shie dannye — prakticheskie aspekty i osobennosti // Academy. 2016. № 6 (9) [Elektronnyy resurs]. URL: https://cyberleninka.ru/article/n/ bolshie-dannye-prakticheskie-aspekty-i-osobennosti (data obrascheniya: 17.01.2024).

5. Moskat N. A. Metody povysheniya effektivnosti avtomatizirovannoy sistemy operativnogo upravleniya na zheleznodorozhnom transporte // IVD. 2018. № 1 (48) [Elektronnyy resurs]. URL: https://cyberleninka.ru/article/n/metody-povysheniya-effektivnostiavtomatizirovannoy- sistemy-operativnogo-upravleniya-na-zheleznodorozhnom-transporte (data obrascheniya: 15.01.2024).

6. Groshev G. M., Klimova N. V., Sugorovskiy A. V. i dr. Avtomatizaciya informacionnogo obespecheniya nezavisimyh uchastnikov mul'timodal'nyh perevozok konteynerov v morskoy port v transportnom uzle // Avtomatika na transporte. 2018. № 3 [Elektronnyy resurs]. URL: https://cyberleninka.ru/article/n/avtomatizatsiya-informatsionnogo-obespecheniyanezavisimyh uchastnikov-multimodalnyh-perevozok-konteynerov-v-morskoy-port-v (data obrascheniya: 17.01.2024).

7. Arhipova E. S. Rol' obrabotki bol'shih dannyh v upravlenii sovremennym predpriyatiem // Ogarev-Online. 2019. № 7 (128) [Elektronnyy resurs]. URL: https://cyberleninka.ru/ article/n/rol-obrabotki-bolshih-dannyh-v-upravlenii-sovremennym-predpriyatiem (data obrascheniya: 17.01.2024).

8. Maloveckaya E. V., Kozlovskiy A. P. Analiz modeley i principov sistemnogo modelirovaniya pri postroenii prognoznyh modeley pogruzki gruzov // International Journal of Open Information Technologies. 2020. № 12 [Elektronnyy resurs]. URL: https://cyberleninka. ru/article/n/analiz-modeley-i-printsipov-sistemnogo-modelirovaniya-pri-postroenii-prognoznyhmodeley- pogruzki-gruzov (data obrascheniya: 17.01.2024).

9. Nazarenko Yu. L. Obzor tehnologii «bol'shie dannye» (big data) i programmno-apparatnyh sredstv, primenyaemyh dlya ih analiza i obrabotki // European science. 2017. № 9 (31) [Elektronnyy resurs]. URL: https://cyberleninka.ru/article/n/obzor-tehnologii-bolshie-dannye-big-data-i-programmnoapparatnyh- sredstv-primenyaemyh-dlya-ih-analiza-i-obrabotki (data obrascheniya: 17.01.2024).

10. Menschikov A. A., Perfil'ev V. E., Fedosenko M. Yu. i dr. Osnovnye problemy ispol'zovaniya bol'shih dannyh v sovremennyh informacionnyh sistemah // Stolypinskiy vestnik. 2022. № 1 [Elektronnyy resurs]. URL: https://cyberleninka.ru/article/n/osnovnyeproblemy- ispolzovaniya-bolshih-dannyh-v-sovremennyh-informatsionnyh-sistemah (data obrascheniya: 17.01.2024).

11. Pugachev S. V., Homonenko A. D., Yarmolinskiy F. A. O razrabotke informacionnoy sistemy gruzoperevozok OAO «RZhD» na osnove bezopasnoy integracii prilozheniy // Intellektual'nye tehnologii na transporte. 2023. № 1 (33) [Elektronnyy resurs]. URL: https://cyberleninka.ru/article/n/o-razrabotke-informatsionnoy-sistemy-gruzoperevozok-oao-rzhdna- osnove-bezopasnoy-integratsii-prilozheniy (data obrascheniya: 17.01.2024).

12. Kravchenko M. V., Nikitin A. S., Spiridonov S. I. Ob unifikacii obmena dannymi mezhdu raznorodnymi sredstvami i sistemami v edinom informacionnom prostranstve // I-methods. 2020. № 2 [Elektronnyy resurs]. URL: https://cyberleninka.ru/article/n/ob-unifikatsiiobmena dannymi-mezhdu-raznorodnymi-sredstvami-i-sistemami-v-edinom-informatsionnomprostranstve (data obrascheniya: 17.01.2024).

13. Plyasova S. V., Kalinin A. R., Zelenkina E. V. Big data kak ob'ekt ocenki // Imuschestvennye otnosheniya v RF. 2022. № 1 (244) [Elektronnyy resurs]. URL: https://cyberleninka.ru/ article/n/bigdata-kak-obekt-otsenki (data obrascheniya: 17.01.2024).

14. Akimov A. E. Bol'shie dannye, iskusstvennyy intellekt i oblachnye tehnologii: cifrovizaciya zheleznyh dorog // Innovacii i investicii. 2023. № 3. [Elektronnyy resurs]. URL: https://cyberleninka.ru/article/n/bolshie-dannye-iskusstvennyy-intellekt-i-oblachnyetehnologii- tsifrovizatsiya-zheleznyh-dorog (data obrascheniya: 17.01.2024).

15. Interaktivnyy avtoinformator dlya klientov zheleznodorozhnogo transporta [Rukois'] : vyp. kvalif. rab. ... step. mgs. / F. A. Yarmolinskiy ; nauchnyy rukovoditel' S. V. Puga- chev ; rec. A. N. Gorkunov ; FGBOU VO PGUPS, kafedra IVS. 2023. 147 s. 23 s.

16. Kurenkov P. V., Kotlyarenko A. F. Vneshnetorgovye perevozki v smeshannom soobschenii: ekonomika, logistika, upravlenie. Samara: SamGAPS, 2003. 636 s.

17. Kurenkov P. V. Material'nye potoki v makrologisticheskih sistemah: sistematizaciya i klassifikaciya // Transport: nauka, tehnika, upravlenie. 2019. № 7. S. 21–26.

18. Kurenkov P. V., Davydov S. V., Bolgova Yu. S. Camarskiy centr konsolidacii gruzopotokov v sisteme mezhdunarodnyh transportnyh koridorov // Logistika segodnya. 2007. № 5. S. 312–322.

19. Kurenkov P. V., Bagimov A. V. Vzaimodeystvie otpraviteley i poluchateley kamennogo uglya pri eksportnyh perevozkah v smeshannom soobschenii // Materialy mezhdunarodnogo nauchno-obrazovatel'nogo foruma. Burgas: 2014. № 1 (5). S. 258–265.

20. Kurenkov P. V., Solov'eva L. V. Izderzhki vzaimodeystviya kompaniy-operatorov i OAO «RZhD» // Materialy mezhdunarodnogo nauchno-obrazovatel'nogo foruma. Burgas: 2014. № 1 (5). S. 266–275.

21. Kurenkov P. V., Solov'eva L. V. Logisticheskie izderzhki vzaimodeystviya kompaniy operatorov i OAO «RZhD» // Logistika. 2014. № 4 (89). S. 24–27.

22. Kurenkov P. V., Solop I. A., Chebotareva E. A. i dr. Ocenka vypolneniya srokov dostavki gruzov na yuge Rossii // Ekonomika zheleznyh dorog. 2023. № 7. S. 13–25.

23. Solop I. A., Chebotareva E. A. Prichinno-sledstvennyy analiz vypolneniya nadezhnosti dostavki gruzov zheleznodorozhnym transportom v adres potrebiteley Yuzhnogo regiona i portov Azovo-Chernomorskogo basseyna // IVD. 2018. № 3 (50) [Elektronnyy resurs]. URL: https://cyberleninka.ru/article/n/prichinno-sledstvennyy-analiz-vypolneniya-nadezhnostidostavkigruzov-zheleznodorozhnym-transportom-v-adres-potrebiteley-yuzhnogo (data obrascheniya: 17.01.2024).

24. Sistema upravleniya oknami [Elektronnyy resurs]. URL: https://niias.ru/products-andservices/ products/asu/avtomatizirovannaya-sistema-planirovanie-uchet-i-analiz-provedeniya-okoni- vypolneniya-khozyaystvenn/?ysclid=lrj4m2iauo361494077 (data obrascheniya: 17.01.2024).

© atjournal.editorum.ru
Support Powered by Editorum,  Instructions
Login or Create
* Forgot password?