Russian Federation
Russian Federation
Russian Federation
Purpose: To develop guidelines for the management of transport operations at port freight stations, based on intelligent algorithms and the application of axiomatic design to technological processes, as well as the processing of arrays of time parameter data. Methods: Following a thorough analysis of the scientific papers relevant to these studies, intelligent algorithms and the author’s models of digital axiomats of transport processes at port stations were utilised in the selection of local operation options. Results: The article sets out principles for creating algorithms to manage transport operations at port freight stations. These principles are based on the author’s axiomatics of technological processes and selection of effective solutions for resolving transport conflicts in the context of multiple service options. The article also discusses the evaluation and selection of rational options using intelligent methods. A study was conducted into the potential for adapting intelligent control algorithms to transport operations at port freight stations, with a view to selecting effective technological parameters for local operations. Practical significance: The author presents their approach to creating control algorithm flowcharts using the example of a station’s transport and technological scheme. This takes into account various service options, potential transport conflicts, and time delays.
Port freight station, transport operation parameters, axiomatics, neural network, control algorithm flowcharts, intellectualization, analytical modelling, transport conflict model schemes
1. Transport of Russia: All-Russian transport weekly information and analytical newspaper. URL: http://www. transportrussia.ru (data obrascheniya: 22.05.2025).
2. Severo-Kavkazskaya zheleznaya doroga. URL: http:// skzdservice.ru/page/4 (data obrascheniya: 22.05.2025).
3. Solodkiy A. I. Razvitie intellektual'nyh transportnyh sistem v Rossii: problemy i puti ih resheniya. Novyy etap / A. I. Solodkiy // Intellekt. Innovacii. Investicii. — 2020. — № 6. — S. 10–19. — DOI: https://doi.org/10.25198/2077-7175-2020-6-10.
4. Seliverstov S. A. Aksiomaticheskie metody organizacii transportno-logisticheskoy infrastruktury / S. A. Seliverstov, Ya. A. Seliverstov // Razvitie infrastruktury i logisticheskih tehnologiy v transportnyh sistemah: Sbornik trudov, Sankt-Peterburg, 23–25 sentyabrya 2015 g. — SPb.: PGUPS, 2016. — S. 67–77.
5. Mamaev E. A. Matematicheskaya model' organizacii ekspluatacionnoy raboty v zadachah povysheniya propusknoy sposobnosti zheleznodorozhnogo uchastka / E. A. Mamaev, E. A. Chebotareva // Izvestiya Peterburgskogo universiteta putey soobscheniya. — 2025. — T. 22. — № 1. — S. 60–74. — DOI: https://doi.org/10.20295/1815-588X-2025-1-60-74.
6. Mirovoy opyt primeneniya sistemnyh resheniy v oblasti ITS. URL: https://dr.rosavtodor.gov.ru/ department/deyatelnost-dr/intellektualnye-transportnye- sistemy/mirovoj-opyt-vnedreniya-i-razvitiya-its (data obra- scheniya: 19.07.2025).
7. Osokin O. V. Intellektual'noe soprovozhdenie proizvodstvennyh processov na zheleznodorozhnom transporte: special'nost' 05.22.08 «Upravlenie processami perevozok»: dis. … d-ra tehn. nauk / O. V. Osokin. — Ekaterinburg, 2014. — 355 s.
8. Pokrovskaya O. D. Logisticheskie transportnye sistemy Rossii v usloviyah novyh sankciy / O. D. Pokrovskaya // Byulleten' rezul'tatov nauchnyh issledovaniy. — 2022. — № 1. — S. 80–94. — DOI:https://doi.org/10.20295/2223- 9987-2022-1-80-94.
9. Rahmangulov A. N. Imitacionnye modeli v cifrovyh dvoynikah zheleznodorozhnyh uzlov / A. N. Rahmangulov i dr. // Vestnik Ural'skogo gosudarstvennogo universiteta putey soobscheniya. — 2022. — № 3. — S. 43–59.
10. Chislov O. Fuzzy modelling of the transportation logistics processes / O. Chislov, N. Lyabakh, M. Kolesnikov, M. Bakalov et al. // Journal of Physics: Conference Series. — 2021. — Vol. 2131. — Iss. 032007. — DOI:https://doi.org/10.1088/1742- 6596/2131/3/032007.
11. Polozhishnikov V. B. O primenenii iskusstvennyh neyronnyh setey na zheleznodorozhnom transporte / V. B. Polozhishnikov, V. A. Akmanov, S. N. Tomaschenko, T. V. Shipunov // Zheleznodorozhnyy transport. — 2019. — № 3. — S. 33–36.
12. Kolesnikov V. I. Intellektualizaciya transportnyh processov na osnove gibridnyh tehnologiy i mul'tiagentnyh sistem / V. I. Kolesnikov, S. M. Kovalev, V. N. Ivanchenko // Vestnik Rostovskogo gosudarstvennogo universiteta putey soobscheniya. — 2012. — № 1(45). — S. 107–113.
13. Leckiy E. K. Cifrovye servisy intellektual'noy podderzhki prinyatiya resheniy pri upravlenii gruzovymi perevozkami na zheleznodorozhnom transporte / E. K. Leckiy, A. V. Semin // Transport: nauka, tehnika, upravlenie. — 2019. — № 9. — S. 17–20.
14. Efanov D. V. Tehnologii cifrovogo modelirovaniya v zheleznodorozhnoy otrasli / D. V. Efanov, A. S. Shilenko // Avtomatika, svyaz', informatika. — 2020. — № 2. — S. 34–38. — DOI 10.34649/ AT.2020.2.2.007.
15. Chislov O. N. Metody cifrovizacii i intellektualizacii parametrov logisticheskogo vzaimodeystviyav sisteme «zh.-d. stanciya — port» v usloviyah mul'tiagentnosti transportno-tehnologicheskih processov: monografiya / O. N. Chislov, M. V. Kolesnikov, V. M. Zadorozhniy, M. V. Bakalov i dr.; FGBOU VO RGUPS; ANO VO NTU «Sirius». — Rostov-na-Donu: RGUPS, 2022. — 396 s.
16. Luganchenko N. M. Principy intellektualizacii v metodah modelirovaniya zheleznodorozhnyh transportno-logisticheskih processov / N. M. Luganchenko, O. N. Chislov // Intellektual'nye transportnye sistemy: materialy III Mezhdunarodnoy nauchno-prakticheskoy konferencii, Moskva, 30 maya 2024 goda. — Moskva: Rossiyskiy universitet transporta (MIIT), 2024. — S. 239–246. — DOI:https://doi.org/10.30932/9785002446094-2024-239-246.
17. Karpathy A. The Unreasonable Effectiveness of Recurrent Neural Networks / A. Karpathy. URL: https:// karpathy.github.io/2015/05/21/rnn-effectiveness (data obrascheniya: 12.08.2025).
18. Makarov D. Rekurrentnaya neyronnaya set' / D. Makarov / URL: https://www.dmitrymakarov.ru/learning/ rnn (data obrascheniya: 12.08.2025).
19. Instrukciya po raschetu propusknoy i provoznoy sposobnostey zheleznyh dorog OAO «RZhD»: Utv. rasporyazheniem OAO «RZhD» ot 04.03.2022 g. № 545/r. — 364 s.
20. Svid. 2022681955 Rossiyskaya Federaciya. Svidetel'stvo o gosudarstvennoy registracii programmy dlya EVM. Axiomatic v. 1 — programma rascheta parametrov transportnyh processov / O. N. Chislov, N. M. Luganchenko, D. S. Bezusov i dr. — № 2022681124: zayavl. 07.11.2022: opubl. 17.11.2022. DOI:https://doi.org/10.21307/tp-2021-031.
