Devising a method for determining the workload on a locomotive driver based on a multicriteria additive model to improve railroad transport safety

Authors

DOI:

https://doi.org/10.15587/1729-4061.2026.365618

Keywords:

railroad transport, traffic safety, traction rolling stock, cognitive load, ergatic system

Abstract

This study investigates the workload on the locomotive driver while driving a train. Up to now, research into the influence of the human factor on train control has not been fully completed. The reasons are the multifactorial nature of the driver’s activity, the limited statistical data, as well as the difficulty of determining the psychophysiological state of a person. A serious issue is the lack of a scientifically substantiated method for determining the level of the driver’s workload during trip. This work addresses this problem and defines quantitative characteristics of the state of the human operator while operating traction rolling stock. By determining the parameters of the environment in which the "driver-train" ergatic system operates and the factors affecting the driver’s workload, it was possible to solve the task. Based on these data, a criterion has been devised to define the workload.

The method is based on an additive approach that combines the flow of information, the complexity of external conditions, as well as the factor of decision-making speed. The advantages of this approach are the relative simplicity of calculations, which ensures the ease of implementation of monitoring the driver’s condition on board the locomotive in real time. It has been established that the total number of signals affecting the driver reaches 20300, of which 165 are critically important. It has been found that the main reserve for reducing the load on a person in the "driver-train" ergatic system is to reduce the amount of information received by the train driver.

In the future, the results of this study could be used to assess different modes of movement along different routes to identify the most dangerous values of the load on the driver. In addition, the research could lay a groundwork for implementing and adjusting the functions of locomotive decision support systems

Author Biographies

Oleksandr Gorobchenko, National Transport University

Doctor of Technical Sciences, Professor

Department of Electromechanics and Rolling Stock of Railways

Denys Zaika, National Transport University

Doctor of Philosophy (PhD)

Department of Electromechanics and Rolling Stock of Railways

Oleksandr Nevedrov, LLC "Research and Production Enterprise" "LOKOMOTIV TRANS SERVICE"

Doctor of Philosophy (PhD), Director

Halyna Holub, National Transport University

Candidate of Technical Sciences, Associate Professor

Department of Automation and Computer-Integrated Technologies of Transport

Viktor Tkachenko, National Transport University

Doctor of Technical Sciences, Professor

Department of Electromechanics and Rolling Stock of Railways

Serhii Kara, PJSC «SRPA «Impulse»

Candidate of Technical Sciences, Business Development Manager

References

  1. Shaffer, F., Ginsberg, J. P. (2017). An Overview of Heart Rate Variability Metrics and Norms. Frontiers in Public Health, 5. https://doi.org/10.3389/fpubh.2017.00258
  2. Chen, J., Li, H., Han, L., Wu, J., Azam, A., Zhang, Z. (2022). Driver vigilance detection for high-speed rail using fusion of multiple physiological signals and deep learning. Applied Soft Computing, 123, 108982. https://doi.org/10.1016/j.asoc.2022.108982
  3. Abe, T. (2023). PERCLOS-based technologies for detecting drowsiness: current evidence and future directions. SLEEP Advances, 4 (1). https://doi.org/10.1093/sleepadvances/zpad006
  4. Dankovich, L. J., Joyner, J. S., He, W., Sesay, A., Vaughn-Cooke, M. (2024). CogWatch: An open-source platform to monitor physiological indicators for cognitive workload and stress. HardwareX, 19, e00538. https://doi.org/10.1016/j.ohx.2024.e00538
  5. Hassanzadeh-Rangi, N., Jalilian, H., Farshad, A.-A., Khosravi, Y. (2023). Correlation of Work Fatigue and Mental Workload in Train Drivers: A Cross-sectional Study. Journal of Research in Health Sciences, 23 (4), e00600. https://doi.org/10.34172/jrhs.2023.135
  6. Wang, P., Fang, W., Guo, B. (2019). Mental workload evaluation and its application in train driving multitasking scheduling: a Timed Petri Net-based model. Cognition, Technology & Work, 23 (2), 299–313. https://doi.org/10.1007/s10111-019-00608-w
  7. Wang, H., Chen, D., Huang, Y., Zhang, Y., Qiao, Y., Xiao, J., Xie, N., Fan, H. (2023). Assessment of Vigilance Level during Work: Fitting a Hidden Markov Model to Heart Rate Variability. Brain Sciences, 13 (4), 638. https://doi.org/10.3390/brainsci13040638
  8. Gorobchenko, O., Matsiuk, V., Holub, H., Gritsuk, I., Nevedrov, O. (2024). Increasing the efficiency of operation and management of railroad transport infrastructure based on maximum levels of fault tolerance. Eastern-European Journal of Enterprise Technologies, 5 (3 (131)), 55–65. https://doi.org/10.15587/1729-4061.2024.311829
  9. Catelani, M., Ciani, L., Guidi, G., Patrizi, G. (2021). An enhanced SHERPA (E-SHERPA) method for human reliability analysis in railway engineering. Reliability Engineering & System Safety, 215, 107866. https://doi.org/10.1016/j.ress.2021.107866
  10. Chen, Z., Guo, Z., Feng, G., Shi, L., Zhang, J. (2021). A Qualitative Study on the Workload of High-Speed Railway Dispatchers. Engineering Psychology and Cognitive Ergonomics, 251–260. https://doi.org/10.1007/978-3-030-77932-0_21
  11. Zoer, I., Sluiter, J. K., Frings-Dresen, M. H. W. (2014). Psychological work characteristics, psychological workload and associated psychological and cognitive requirements of train drivers. Ergonomics, 57 (10), 1473–1487. https://doi.org/10.1080/00140139.2014.938130
  12. Gorobchenko, O., Holub, H., Zaika, D. (2024). Theoretical basics of the self-learning system of intelligent locomotive decision support systems. Archives of Transport, 71 (3), 169–186. https://doi.org/10.61089/aot2024.gaevsp41
  13. Goolak, S., Liubarskyi, B., Riabov, I., Chepurna, N., Pohosov, O. (2023). Simulation of a direct torque control system in the presence of winding asymmetry in induction motor. Engineering Research Express, 5 (2), 25070. https://doi.org/10.1088/2631-8695/acde46
  14. Tverdomed, V., Dmytro, Z., Kokriatska, N., Lukoševičius, V. (2024). The Detection of Railheads: An Innovative Direct Image Processing Method. Sustainability, 16 (12), 5109. https://doi.org/10.3390/su16125109
  15. Hwang, C.-L., Yoon, K. (1981). Multiple Attribute Decision Making. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-48318-9
  16. Belton, V., Stewart, T. J. (2002). Multiple Criteria Decision Analysis. Springer US. https://doi.org/10.1007/978-1-4615-1495-4
  17. Jahan, A., Edwards, K. L. (2016). Multi-criteria decision analysis for supporting the selection of engineering materials in product design. Butterworth-Heinemann. https://doi.org/10.1016/c2012-0-02834-7
  18. Ishizaka, A., Nemery, P. (2013). Multi‐Criteria Decision Analysis. https://doi.org/10.1002/9781118644898
  19. Gorobсhenko, O. (2021). Theoretical fundamentals of estimatability assessment of train situation signs for work of intellectual locomotive control systems. Collection of Scientific Works of the State University of Infrastructure and Technologies Series “Transport Systems and Technologies”, 1 (38), 223–231. https://doi.org/10.32703/2617-9040-2021-38-220-21
  20. Wickens, C. D., Hollands, J. G., Banbury, S., Parasuraman, R. (2015). Engineering Psychology and Human Performance. Psychology Press. https://doi.org/10.4324/9781315665177
  21. Naweed, A. (2020). Getting mixed signals: Connotations of teamwork as performance shaping factors in network controller and rail driver relationship dynamics. Applied Ergonomics, 82, 102976. https://doi.org/10.1016/j.apergo.2019.102976
  22. Gorobchenko, O., Nevedrov, O. (2020). Development of the structure of an intelligent locomotive DSS and as-sessment of its efectiveness. Archives of Transport, 56 (4), 47–58. https://doi.org/10.5604/01.3001.0014.5517
  23. Chambon, T., Guillaume, J.-L., Lallement, J. (2023). Information Complexity Ranking: A New Method of Ranking Images by Algorithmic Complexity. Entropy, 25 (3), 439. https://doi.org/10.3390/e25030439
  24. Zenil, H., Marshall, J. A. R., Tegnér, J. (2023). Approximations of algorithmic and structural complexity validate cognitive-behavioral experimental results. Frontiers in Computational Neuroscience, 16. https://doi.org/10.3389/fncom.2022.956074
  25. Lange, C., Ahrens, A. (2025). Digital Transmission Engineering. Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-46789-0
  26. Gibson, J. D. (2023). Digital Communications. Springer International Publishing. https://doi.org/10.1007/978-3-031-19588-4
  27. Renzo, M. D., Migliore, M. D. (2024). Electromagnetic Signal and Information Theory. IEEE BITS the Information Theory Magazine, 4 (1), 25–39. https://doi.org/10.1109/mbits.2024.3359523
  28. Кustov, V. (2025). Fundamental limitations of basic dangerous failure models for functional safety assessment in train control systems. Collection of Scientific Works of the Ukrainian State University of Railway Transport, 212, 301–314. https://doi.org/10.18664/1994-7852.212.2025.336537
  29. Rieger, T., Manzey, D. (2020). Human Performance Consequences of Automated Decision Aids: The Impact of Time Pressure. Human Factors: The Journal of the Human Factors and Ergonomics Society, 64 (4), 617–634. https://doi.org/10.1177/0018720820965019
  30. Laxar, D., Eitenberger, M., Maleczek, M., Kaider, A., Hammerle, F. P., Kimberger, O. (2023). The influence of explainable vs non-explainable clinical decision support systems on rapid triage decisions: a mixed methods study. BMC Medicine, 21 (1). https://doi.org/10.1186/s12916-023-03068-2
Devising a method for determining the workload on a locomotive driver based on a multicriteria additive model to improve railroad transport safety

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Published

2026-06-30

How to Cite

Gorobchenko, O., Zaika, D., Nevedrov, O., Holub, H., Tkachenko, V., & Kara, S. (2026). Devising a method for determining the workload on a locomotive driver based on a multicriteria additive model to improve railroad transport safety. Eastern-European Journal of Enterprise Technologies, 3(3 (141), 76–86. https://doi.org/10.15587/1729-4061.2026.365618

Issue

Vol. 3 No. 3 (141) (2026): Control processes

Section

Control processes

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Copyright (c) 2026 Oleksandr Gorobchenko, Denys Zaika, Oleksandr Nevedrov, Halyna Holub, Viktor Tkachenko, Serhii Kara

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