Current State of Intellectualization, Digitalization and Robotization Systems of Domestic Railway Transport

The paper systematizes the results of the scientific and practical conference «PRO//Movement. Transport Management Systems» (August 18-20, 2026, Nizhny Novgorod), dedicated to the digitalization of the transportation process, artificial intelligence, and the robotization of railway transport. The conference was attended by the management of the Russian Railways holding, the scientific sector complex, and manufacturing enterprises: managers, scientists, and engineers. The target model for managing freight flows, the «Digital Railway Station» project, and technologies such as virtual coupling, machine vision, predictive diagnostics, and physical artificial intelligence were discussed. Special attention is paid to the role of the scientific activities of JSC «NIIAS» in shaping a unified data architecture, models, and executive systems, as well as to issues of functional safety, economic efficiency, and the replication of developments. The prospects for the development of artificial intelligence in the railway industry are discussed.

1. Quaglietta, E., Pellegrini, P., Goverde, R. M. P., Albrecht, T., Jaekel, B., Marlière, G., Rodriguez, J., Dollevoet, T., Ambrogio, B., Carcasole, D., Giaroli, M., & Nicholson, G. (2016). The ON-TIME real-time railway traffic management framework: A proof-of-concept using a scalable standardised data communication architecture. Transportation Research Part C, 63, 23–50. https://doi.org/10.1016/j.trc.2015.11.014

2. Tang, R., De Donato, L., Bešinović, N., Flammini, F., Goverde, R. M. P., Lin, Z., Liu, R., Tang, T., Vittorini, V., & Wang, Z. (2022). A literature review of artificial intelligence applications in railway systems. Transportation Research Part C, 140, Article 103679. https://doi.org/10.1016/j.trc.2022.103679

3. Bešinović, N., De Donato, L., Flammini, F., Goverde, R. M. P., Lin, Z., Liu, R., Marrone, S., Nardone, R., Tang, T., & Vittorini, V. (2022). Artificial intelligence in railway transport: Taxonomy, regulations, and applications. IEEE Transactions on Intelligent Transportation Systems, (9), 14011–14024. https://doi.org/10.1109/TITS.2021.3131637

4. Shubinskiy, I. B., & Rozenberg, E. N. (2023). General provisions for justifying the functional safety of intelligent systems in railway transport. Dependability, 23(3), 38–45. https://doi.org/10.21683/1729-2646-2023-23-3-38-45 (in Russian)

5. Shubinskiy, I. B., Rozenberg, E. N., & Bochkov, A. V. (2024). Reliability, risks, and safety of control systems in railway transport. Infra-Inzheneriya. (in Russian)

6. Bochkov, K. A., Kharlap, S. N., & Litvinov, E. P. (2025). Review of architecture, features and methods of ensuring safety in automated control systems for critical technological processes of railway transport. In Problemy bezopasnosti na transporte: Materialy XIV Mezhdunarodnoy nauchno-prakticheskoy konferentsii, posvyashchennoy pyatiletke kachestva (pp. 140–143). Belorusskiy gosudarstvennyy universitet transporta. (in Russian)

7. Rozenberg, E. N., Popov, P. A., Talalayev, D. V., Olshanskiy, A. M., & Boyarinova, N. A. (2022). General approaches to safety justification of autonomous systems. Automation, Communications, Informatics, (1), 2–9. (in Russian)

8. Rozenberg, E. N., Dezhkov, M. A., & Novikov, V. G. (2025). Application of virtual coupling technology at the Eastern polygon. Automation, Communications, Informatics, (8), 7–11. https://doi.org/10.62994/AT.2025.8.8.002 (in Russian)

9. Felez, J., & Vaquero-Serrano, M. A. (2023). Virtual coupling in railways: A comprehensive review. Machines, 11(5), Article 521. https://doi.org/10.3390/machines11050521

10. Dolgiy, A. I., & Sukhanov, A. V. (2026). Fuzzy-logic approach to automatic analysis of freight station indicators based on “from the wheel” data. In Intellektualnyye transportnyye sistemy: Materialy V Mezhdunarodnoy nauchno-prakticheskoy konferentsii (Moskva, 21 maya 2026 g.) (pp.–145). RUT (MIIT). https://doi.org/10.30932/9785002709564-2026-139-145 (in Russian)

11. Dolgiy, A. I., Khatlamadzhiyan, A. E., Olgeyzer, I. A., & Sukhanov, A. V. (2025). Classification of railway station operation modes based on real “from the wheel” data. Vestnik RGUPS, (1(97)), 58–68. https://doi.org/10.46973/0201-727X_2025_1_58 (in Russian)

12. Khatlamadzhiyan, A. E., Olgeyzer, I. A., Sukhanov, A. V., & Iyerusalimov, V. S. (2024). Formation of objective indicators of marshalling yard operation based on “from the wheel” data. Automation on Transport, 10(3), 254–268. https://doi.org/10.20295/2412-9186-2-10-03-254-268 (in Russian)

13. Goverde, R. M. P. (2007). Railway timetable stability analysis using max-plus system theory. Transportation Research Part B, 41(2), 179–201. https://doi.org/10.1016/j.trb.2006.02.

14. D’Ariano, A., Corman, F., Pacciarelli, D., & Pranzo, M. (2008). Reordering and local rerouting strategies to manage train traffic in real time. Transportation Science, 42(4), 405–419. https://doi.org/10.1287/trsc.1080.0247

15. Lusby, R. M., Larsen, J., Ehrgott, M., & Ryan, D. (2011). Railway track allocation: Models and methods. OR Spectrum, 33(4), 843–883. https://doi.org/10.1007/s00291-009-0189-0

16. Corman, F., D’Ariano, A., Pacciarelli, D., & Pranzo, M. (2012). Bi-objective conflict detection and resolution in railway traffic management. Transportation Research Part C, 20(1), 79–94. https://doi.org/10.1016/j.trc.2010.09.009

17. Andreyev, V. E., Dolgiy, A. I., Kudyukin, V. V., Khatlamadzhiyan, A. E., Grishayev, S. Yu., & Olgeyzer, I. A. (2023). Digital railway station – from concept to real implementation. Automation, Communications, Informatics, (9), 2–6. https://doi.org/10.34649/AT.2023.9.9.001 (in Russian)

18. Kaewunruen, S., Sresakoolchai, J., & Lin, Y.-H. (2023). Digital twins for managing railway bridge maintenance, resilience, and climate change adaptation. Sensors, 23(1), Article 252. https://doi.org/10.3390/s23010252

19. van Dinter, R., Tekinerdogan, B., & Catal, C. (2022). Predictive maintenance using digital twins: A systematic literature review. Information and Software Technology, 151, Article 107008. https://doi.org/10.1016/j.infsof.2022.107008

20. Padovano, A., Longo, F., Manca, L., & Grugni, R. (2024). Improving safety management in railway stations through a simulation-based digital twin approach. Computers & Industrial Engineering, 187, Article 109839. https://doi.org/10.1016/j.cie.2023.109839

21. Zhang, J., & Zhang, J. (2023). Artificial intelligence applied on traffic planning and management for rail transport: A review and perspective. Discrete Dynamics in Nature and Society, Article https://doi.org/10.1155/2023/1832501

22. Rozenberg, I. N. (Ed.). (2021). Proceedings of JSC “NIIAS” (Issue 11, Vol. 3, pp. 7–68). T8 Izdatelskiye Tekhnologii. (in Russian)

23. Chen, M., Tworek, J., Jun, H., Yuan, Q., Ponde de Oliveira Pinto, H., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., . . . Zaremba, W. (2021). Evaluating large language models trained on code (arXiv:2107.03374). arXiv. https://doi.org/10.48550/arXiv.2107.03374

24. Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., & Fritz, M. (2023). Not what you’ve signed up for: Compromising real-world LLM-integrated applications with indirect prompt injection. In Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security (AISec ’23) (pp. 79–90). ACM. https://doi.org/10.1145/3605764.3623985

25. Moiseyev, V. V. (2022). Ensuring cybersecurity of microprocessor and relay-processor interlocking systems on the network of JSC “Russian Railways”: Prospects and practical application of protection tools. Automation on Transport, 8(3), 266–275. https://doi.org/10.20295/241-9186-2022-8-03-266-275 (in Russian)

26. Letskiy, E. K., & Gurov, A. I. (2025). Integration of artificial intelligence into functional testing of critical information infrastructure of railway transport. Intellectual Technologies on Transport, (1), 14–19. https://doi.org/10.20295/2413-2527-2025-141-14-19 (in Russian)

27. De Donato, L., Flammini, F., Marrone, S., Mazzariello, C., Nardone, R., Sansone, C., & Vittorini, V. (2022). A survey on audio-video based defect detection through deep learning in railway maintenance. IEEE Access, 10, 65376–65400. https://doi.org/10.1109/ACCESS.23183102

28. Alif, M. A. R., Tucker, G., & Hussain, M. (2026). Advances in computer vision for comprehensive railway engineering: From track inspection to rolling stock and safety monitoring. Railway Engineering Science. https://doi.org/10.1007/s40534-026-00434-7

29. Olivier, B., Guo, F., Qian, Y., & Connolly, D. P. (2025). A review of computer vision for railways. IEEE Transactions on Intelligent Transportation Systems, 26(7), 11034–11065. https://doi.org/10.1109/TITS.2025.3552011

30. Kumar, A., & Harsha, S. P. (2025). A systematic literature review of defect detection in railways using machine vision-based inspection methods. International Journal of Transportation Science and Technology, 18, 207–226. https://doi.org/10.1016/j.ijtst.2024.06.006

31. Dolgiy, A. I., Khatlamadzhiyan, A. E., Olgeyzer, I. A., Sukhanov, A. V., & Korniyenko, K. I. (2022). Innovative machine vision algorithms for diagnosing the longitudinal profile of marshalling tracks. Automation, Communications, Informatics, (8), 7–9. https://doi.org/10.3/AT.2022.8.8.002 (in Russian)

32. Olgeyzer, I. A., Sukhanov, A. V., Lyashchenko, A. M., & Glazunov, D. V. (2022). Computer vision as a way of intellectualizing hump automation systems. Engineering Automation Problems, (1), 46–53. https://doi.org/10.52261/02346206_2022_1_46 (in Russian)

33. Vasilyeva, S. A., & Lokhach, A. V. (2023). Automation of lateral rail wear prediction in curves as an element of predictive analysis of track condition. Put i putevoye khozyaystvo, (11), 5–7. (in Russian)

34. Ristić-Durrant, D., Franke, M., & Michels, K. (2021). A review of vision-based onboard obstacle detection and distance estimation in railways. Sensors, 21(10), Article 3452. https://doi.org/10.3390/s21103452

35. Pappaterra, M. J., Flammini, F., Vittorini, V., & Bešinović, N. (2021). A systematic review of artificial intelligence public datasets for railway applications. Infrastructures, 6(10), Article 136. https://doi.org/10.3390/infrastructures6100136

36. Gan, J., Li, Q., Wang, J., & Yu, H. (2017). A hierarchical extractor-based visual rail surface inspection system. IEEE Sensors Journal, 17(23), 7935–7944. https://doi.org/10.1109/JSEN.2017.2761858

37. Yarmolinskiy, F. A., Pokrovskaya, O. D., Pasechnik, E. D., Pakulina, E. V., & Trapeznikov, A. A. (2024). Features of using Big Data in the study of freight flows in railway transport. Bulletin of Research Results, (1), 107–122. https://doi.org/10.20295/2223-9987-2024-01-107-122 (in Russian)

38. Gurgenidze, I. R., Kuranin, B. N., Lysikov, M. G., Lyashenko, S. I., Stepanov, A. V., & Tororoshenko, S. V. (2014). Method of train traffic control using variant schedules (Russian Federation Patent No. 2524505 C1). OAO “NIIAS”. (in Russian)

39. Zhang, Q., Lusby, R. M., Shang, P., & Zhu, X. (2022). A heuristic approach to integrate train timetabling, platforming, and railway network maintenance scheduling decisions. Transportation esearch Part B, 158, 210–238. https://doi.org/10.1016/j.trb.2022.02.002

40. Sidorenko, V. G., Kulagin, M. A., & Rodina, A. E. (2025). Impact of digitalization of urban rail transport systems on train planning and traffic management processes. Automation on Transport, 11(1), 30–43. https://doi.org/10.20295/2412-9186-2025-11-01-30-43 (in Russian)

41. Alekseyev, V. M., Baranov, L. A., Kulagin, M. A., & Sidorenko, V. G. (2021). Building the architecture of an intelligent control system for an urban rail transport system. World of Transport and Transportation, 19(1), 18–46. https://doi.org/10.30932/1992-3252-202-19-1-18-46 (in Russian)

42. Sidorenko, V. G., Loginova, L. N., & Safronov, A. I. (2023). Development of information support for an intelligent control system of urban rail transport systems. Automation on Transport, 9(2), 178–192. https://doi.org/10.20295/2412-9186-2023-9-02-178-192 (in Russian)

43. Sidorenko, V. G., Kopylova, E. V., Safronov, A. I., & Tumanov, M. A. (2023). Experience and prospects of automation of transportation process management for high-speed transport in urban agglomerations. Automation on Transport, 9(1), 33–48. https://doi.org/10.20295/2-9186-2023-9-01-33-48 (in Russian)

44. Tarasov, K. A., Shein, M. Yu., & Boyarinova, N. A. (2026). ACS “KAMA”: Energy-optimal operation of a train package. Zheleznodorozhnyy transport, (5), 18–19. (in Russian)

45. Kononov, A. F., Makogon, V. D., Khatlamadzhiyan, A. E., & Shapekin, A. E. (2026). Projects in the field of robotization. Automation, Communications, Informatics, (3), 15–17. https://doi.org/10.62994/AT.2026.3.3.003 (in Russian)

46. Kupriyanovskiy, V. P., Pokusayev, O. N., Volokitin, Yu. I., Namiot, D. E., Petrunina, I. P., & Zazhigalkin, A. V. (2018). Formalized ontologies and services for high-speed railways and the digital railway. International Journal of Open Information Technologies, 6(3), 69–86. (in Russian)

47. Kasch, J., & Ahmadian, M. (2024). Design and operational assessment of a railroad track robot for railcar undercarriage condition inspection. Designs, 8(4), Article 70. https://doi.org/10.3390/designs8040070

48. Liu, H., Rahman, M., Rahimi, M., Starr, A., Durazo-Cardenas, I., Ruiz-Carcel, C., Ompusunggu, A., Hall, A., & Anderson, R. (2023). An autonomous rail-road amphibious robotic system for railway maintenance using sensor fusion and mobile manipulator. Computers & Electrical Engineering, Article 108874. https://doi.org/10.1016/j.compeleceng.2023.108874

49. Daniyan, I. A., Mpofu, K., & Nwankwo, S. O. (2023). Design of a robot for inspection and diagnostic operations of rail track facilities. International Journal of Quality & Reliability Management, 40(3), 653–673. https://doi.org/10.1108/IJQRM-03-2020-0083

50. Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Chen, X., Choromanski, K., Ding, T., Driess, D., Dubey, A., Finn, C., Florence, P., Fu, C., Gonzalez Arenas, M., Gopalakrishnan, K., Han, K., Hausman, K., Herzog, A., Hsu, J., Ichter, B., . . . Zitkovich, B. (2023). RT-2: Vision-language-action models transfer web knowledge to robotic control (arXiv:2307.15818). arXiv. https://doi.org/10.48550/arXiv.2307.15818

51. O’Neill, A., Rehman, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., Mandlekar, A., Jain, A., Tung, A., Bewley, A., Herzog, A., Irpan, A., Khazatsky, A., Rai, A., Gupta, A., Wang, A., Singh, A., . . . Lin, Z. (2024). Open X-Embodiment: Robotic learning datasets and RT-X models. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). IEEE. https://doi.org/10.1109/ICRA57147.2024.10611477

52. Kim, M. J., Pertsch, K., Karamcheti, S., Xiao, T., Balakrishna, A., Nair, S., Rafailov, R., Foster, E., Lam, G., Sanketi, P., Vuong, Q., Kollar, T., Burchfiel, B., Tedrake, R., Sadigh, D., Levine, S., Liang, P., & Finn, C. (2024). OpenVLA: An open-source vision-language-action model (arXiv:2406.09246). arXiv. https://doi.org/10.48550/arXiv.2406.09246

53. Popov, P. A. (2020). Application of advanced technologies for automatic operation at the Moscow Central Circle. Zheleznodorozhnyy transport, (11), 17–21. (in Russian)

54. Zhao, Y., Zhao, L., Cheng, B., Yao, G., Wen, X., & Gao, H. (2025). VLA-RAIL: A real-time asynchronous inference linker for VLA models and robots (arXiv:2512.24673). arXiv. https://arxiv.org/abs/2512.24673

55. Kudyukin, V. V., Vukolov, A. V., & Kuzmin, V. S. (2025). Simulation modeling of robotic complexes designed for train breaking-up at marshalling humps. Automation on Transport, 11(1), –29. https://doi.org/10.20295/2412-9186-2025-11-01-16-29 (in Russian)