Improving control over operational characteristics of subway rolling stock by using retrospective passenger flow estimation

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.340757

Keywords:

route reconstruction, subway, adaptive repair planning, optimization of timetables

Abstract

This study investigates the process that forms operational loads on the electric rolling stock within a city’s underground railroad system. The task addressed relates to the impossibility to directly measure passenger flows on separate sections of the network because most control systems only record passenger entry and exit events without tracking their full route. This creates analytical gaps and makes it impossible to accurately assess the actual load, which is critical for technical diagnostics as well as maintenance.

To solve this problem, a method for estimating passenger flows has been devised, based on stochastic modelling of passenger movement on an oriented graph of the transport network.

An approach to load estimation has been proposed, the distinctive feature of which is the rejection of determining a single most probable route for each passenger. Instead, the conditional "weight" of a passenger is probabilistically distributed among the set of all possible routes that could lead him/her from the potential entry station to the actual exit station within time constraints.

Method verification by simulation on a conditional network showed high accuracy of the results (the average relative error did not exceed 0.5%). The distribution of errors is symmetrical, close to normal, and concentrated around zero, which indicates the absence of systematic deviations. The accuracy is attributed to the fact that the probability distribution makes it possible to level out the uncertainty of the passenger’s choice of a specific route and obtain an objective integrated assessment of the load at the level of individual routes.

The scope of practical application of the results includes technical monitoring systems for rolling stock, adaptive repair planning, optimization of timetables, as well as improvement of transport safety in the context of using anonymous means of payment for travel

Author Biographies

Ivan Siroklyn, Ukrainian State University of Railway Transport

PhD

Department of Automation and Computer Telecontrol of Trains

Serhii Zmii, Ukrainian State University of Railway Transport

PhD

Department of Automation and Computer Telecontrol of Trains

Vasyl Sotnyk, Ukrainian State University of Railway Transport

PhD

Department of Automation and Computer Telecontrol of Trains

Olena Shcheblykina, Ukrainian State University of Railway Transport

Doctor of Philosophy (PhD)

Department of Automation and Computer Telecontrol of Trains

References

  1. Cats, O., Gkiotsalitis, K., Schöbel, A. (2025). 50 years of Operations Research in public transport. EURO Journal on Transportation and Logistics, 14, 100160. https://doi.org/10.1016/j.ejtl.2025.100160
  2. Hussain, E., Bhaskar, A., Chung, E. (2021). Transit OD matrix estimation using smartcard data: Recent developments and future research challenges. Transportation Research Part C: Emerging Technologies, 125, 103044. https://doi.org/10.1016/j.trc.2021.103044
  3. Sari Aslam, N., Barros, J., Lin, H., Murcio, R., Bei, H. (2024). Alighting location estimation from public transit data: a case study of Shenzhen. Transportation Planning and Technology, 48 (5), 937–952. https://doi.org/10.1080/03081060.2024.2382247
  4. Tiam-Lee, T. J., Henriques, R. (2022). Route choice estimation in rail transit systems using smart card data: handling vehicle schedule and walking time uncertainties. European Transport Research Review, 14 (1). https://doi.org/10.1186/s12544-022-00558-x
  5. Mo, B., Ma, Z., Koutsopoulos, H., Zhao, J. (2023). Passenger Path Choice Estimation Using Smart Card Data: A Latent Class Approach with Panel Effects Across Days. arXiv. https://doi.org/10.48550/arXiv.2301.03808
  6. Nagy, R., Horvát, F., Fischer, S. (2024). Innovative Approaches in Railway Management: Leveraging Big Data and Artificial Intelligence for Predictive Maintenance of Track Geometry. Tehnički vjesnik - Technical Gazette, 31 (4), 1245–1259. https://doi.org/10.17559/tv-20240420001479
  7. Ou, Y., Mihăiţă, A.-S., Ellison, A., Mao, T., Lee, S., Chen, F. (2025). Rail Digital Twin and Deep Learning for Passenger Flow Prediction Using Mobile Data. Electronics, 14 (12), 2359. https://doi.org/10.3390/electronics14122359
  8. Su, G., Li, P., Lian, D., Mo, P. (2025). A coordinated passenger flow control model for urban rail transit considering willingness to board. Multimodal Transportation, 4 (3), 100225. https://doi.org/10.1016/j.multra.2025.100225
  9. Liu, J., Huang, W., Zhang, N., Ma, Z., Qian, Z. (2024). A Data-Driven Multi-Modal Fusion Method for Path Choice Estimation in Urban Rail Systems. https://doi.org/10.2139/ssrn.4994725
  10. Wen, D., Lv, H., Yu, H. (2025). Data‐Driven Approach for Passenger Assignment in Urban Rail Transit Networks: Insights From Passenger Route Choices and Itinerary Choices. Journal of Advanced Transportation, 2025 (1). https://doi.org/10.1155/atr/6620828
  11. Makridis, M. A., Kouvelas, A. (2023). An adaptive framework for real-time freeway traffic estimation in the presence of CAVs. Transportation Research Part C: Emerging Technologies, 149, 104066. https://doi.org/10.1016/j.trc.2023.104066
  12. Bernal, E., Rey, Á. (2019). Study of the Structural and Robustness Characteristics of Madrid Metro Network. Sustainability, 11 (12), 3486. https://doi.org/10.3390/su11123486
  13. Tian, X., Zheng, B., Wang, Y., Huang, H.-T., Hung, C.-C. (2021). TRIPDECODER: Study Travel Time Attributes and Route Preferences of Metro Systems from Smart Card Data. ACM/IMS Transactions on Data Science, 2 (3), 1–21. https://doi.org/10.1145/3430768
Improving control over operational characteristics of subway rolling stock by using retrospective passenger flow estimation

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Published

2025-10-31

How to Cite

Siroklyn, I., Zmii, S., Sotnyk, V., & Shcheblykina, O. (2025). Improving control over operational characteristics of subway rolling stock by using retrospective passenger flow estimation. Eastern-European Journal of Enterprise Technologies, 5(3 (137), 68–74. https://doi.org/10.15587/1729-4061.2025.340757

Issue

Section

Control processes