GENERALIZED MODEL OF CONTROL OF ERGATIC NAVIGATIONAL SUPPORT SYSTEMS WITH AN INTEGRAL INDICATOR OF THE INFLUENCE OF THE HUMAN FACTOR
https://doi.org/10.33815/2313-4763.2026.1.32.034-057
Keywords:
ergatic system, navigational support, human factor, safety of navigation, navigator, AIS/ECDIS data, fractal-episodic analysis, integral risk indicator, automated motion control
Abstract
The article develops a generalized model of the control of ergatic navigational support systems with an integral indicator of the influence of the navigator’s human factor. The relevance of the study is determined by the need for a formalized consideration of the behavioral, cognitive-temporal, and dynamic manifestations of the navigator’s activity within the contours of risk assessment, decision support, and automated control of vessel motion. The aim of the work is to construct an integrated model that combines the systemic representation of the ergatic system, the formalization of AIS/ECDIS data, the fractal-episodic representation of vessel micromotions, the cognitive-temporal states of the navigator, the gravitational-inertial interpretation of stability, and a risk-oriented control contour. The scientific novelty consists in the introduction of an integral indicator of the influence of the human factor on the increase in the risk of the ergatic navigational support system, which combines episodic behavioral, cognitive-temporal, dynamic-stability, and control-risk components into a unified information-analytical contour. The numerical approbation of the proposed indicator is intended to be carried out using the Monte Carlo method, which makes it possible to evaluate variability, sensitivity, and the probability of transition of the system to strained and critical operating modes. The practical significance of the work lies in the possibility of using the proposed model in decision support systems, the diagnostics of the navigator’s state, and the automated formation of safe modes of control of vessel motion.
References
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23. Song, R., Papadimitriou, E., Negenborn, R. R., van Gelder, P. (2024). Safety and Efficiency of Human-MASS Interactions: Towards an Integrated Framework. Journal of Marine Engineering and Technology. Vol. 24, No. 3. P. 159–178. https://doi.org/10.1080/20464177.2024.2414959.
2. Pietrzykowski, Z., Wielgosz, M., Breitsprecher, M. (2020). Navigators’ Behavior Analysis Using Data Mining. Journal of Marine Science and Engineering. Vol. 8, No. 1. Article 50. https://doi.org/10.3390/jmse8010050.
3. Hetherington, C., Flin, R., Mearns, K. (2006). Safety in Shipping: The Human Element. Journal of Safety Research. Vol. 37, No. 4. P. 401–411. https://doi.org/10.1016/j.jsr.2006.04.007.
4. Du, L., Goerlandt, F., Kujala, P. (2020). Review and Analysis of Methods for Assessing Maritime Waterway Risk Based on Non-Accident Critical Events Detected from AIS Data. Reliability Engineering & System Safety. Vol. 200. Article 106933. https://doi.org/10.1016/j.ress.2020.106933.
5. Tu, E., Zhang, G., Rachmawati, L., Rajabally, E., Huang, G. B. (2018). Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology. IEEE Transactions on Intelligent Transportation Systems. Vol. 19, No. 5. P. 1559–1582. https://doi.org/10.1109/TITS.2017.2724551.
6. Nosov, P. S., Palamarchuk, I. V., Zinchenko, S. M., Popovych, I. S., Nahrybelnyi, Y. A., Nosova, H. V. (2020). Development of Means for Experimental Identification of Navigator Attention in Ergatic Systems of Maritime Transport. Bulletin of the University of Karaganda. Technical Physics. No. 1 (97). P. 58–69. https://doi.org/10.31489/2020Ph1/58-69.
7. Car, M., Brčić, D., Žuškin, S., Svilicic, B. (2020). The Navigator’s Aspect of PNC before and after ECDIS Implementation: Facts and Potential Implications towards Navigation Safety Improvement. Journal of Marine Science and Engineering. Vol. 8, No. 11. Article 842. https://doi.org/10.3390/jmse8110842.
8. Nosov, P., Cherniavskyi, V., Zinchenko, S., Popovych, I., Prokopchuk, Y., Safonov, M. (2020). Identification of Distortion of the Navigator’s Time in Model Experiment. Bulletin of the University of Karaganda. Instrument and Experimental Techniques. No. 4 (100). P. 57–70. https://doi.org/10.31489/2020Ph4/57-70.
9. Kristić, M., Žuškin, S., Brčić, D., Car, M. (2021). Overreliance on ECDIS Technology: A Challenge for Safe Navigation. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation. Vol. 15, No. 2. P. 277–287. https://doi.org/10.12716/1001.15.02.02.
10. Nosov, P., Zinchenko, S., Ben, A., Prokopchuk, Y., Mamenko, P., Popovych, I., Moiseienko, V., Kruglyj, D. (2021). Navigation Safety Control System Development through Navigator Action Prediction by Data Mining Means. Eastern-European Journal of Enterprise Technologies. Vol. 2, No. 9 (110). P. 55–68. https://doi.org/10.15587/1729-4061.2021.229237.
11. Ponomaryova, V., Nosov, P. (2024). Development of a Navigator Qualification Model for Automated Ship Handling Control Tasks. Науковий вісник Херсонської державної морської академії. No. 2 (29). P. 6–23. https://doi.org/10.33815/2313-4763.2024.2.29.006-023.
12. Nosov, P., Koretsky, O., Zinchenko, S., Prokopchuk, Y., Gritsuk, I., Sokol, I., Kyrychenko, K. (2023). Devising an Approach to Safety Management of Vessel Control through the Identification of Navigator’s State. Eastern-European Journal of Enterprise Technologies. Vol. 4, No. 3 (124). P. 19–32. https://doi.org/10.15587/1729-4061.2023.286156.
13. Nosov, P., Melnyk, O., Malaksiano, M., Shumylo, O., Onishchenko, O., Yarovenko, V., Zinchenko, S., & Popovych, I. (2026). A Unified Fractal Processing Framework for Normalized AIS and ECDIS Ship Trajectories. Digital, 6(1), 11. https://doi.org/10.3390/digital6010011.
14. Pazouki, K., Forbes, N., Norman, R. A., Woodward, M. D. (2018). Investigation on the Impact of Human-Automation Interaction in Maritime Operations. Ocean Engineering. Vol. 153. P. 297–304. https://doi.org/10.1016/j.oceaneng.2018.01.103.
15. Kari, R., Steinert, M. (2021). Human Factor Issues in Remote Ship Operations: Lesson Learned by Studying Different Domains. Journal of Marine Science and Engineering. Vol. 9, No. 4. Article 385. https://doi.org/10.3390/jmse9040385.
16. Man, Y., Lundh, M., Porathe, T., MacKinnon, S. (2015). From Desk to Field – Human Factor Issues in Remote Monitoring and Controlling of Autonomous Unmanned Vessels. Procedia Manufacturing. Vol. 3. P. 2674–2681. https://doi.org/10.1016/j.promfg.2015.07.635.
17. Huang, Y., Chen, L., Negenborn, R. R., van Gelder, P. H. A. J. M. (2020). A Ship Collision Avoidance System for Human-Machine Cooperation during Collision Avoidance. Ocean Engineering. Vol. 217. Article 107913. https://doi.org/10.1016/j.oceaneng.2020.107913.
18. Aylward, K., Weber, R., Lundh, M., MacKinnon, S. N., Dahlman, J. (2022). Navigators’ Views of a Collision Avoidance Decision Support System for Maritime Navigation. The Journal of Navigation. Vol. 75, No. 5. P. 1035–1048. https://doi.org/10.1017/S0373463322000510.
19. Wu, X., Liu, K., Zhang, J., Yuan, Z., Liu, J., Yu, Q. (2021). An Optimized Collision Avoidance Decision-Making System for Autonomous Ships under Human–Machine Cooperation Situations. Journal of Advanced Transportation. Vol. 2021. Article 7537825. https://doi.org/10.1155/2021/7537825.
20. Veitch, E., Alsos, O. A. (2021). Human-Centered Explainable Artificial Intelligence for Marine Autonomous Surface Vehicles. Journal of Marine Science and Engineering. Vol. 9, No. 11. Article 1227. https://doi.org/10.3390/jmse9111227.
21. Wróbel, K., Gil, M., Chae, C.-J. (2021). On the Influence of Human Factors on Safety of Remotely-Controlled Merchant Vessels. Applied Sciences. Vol. 11, No. 3. Article 1145. https://doi.org/10.3390/app11031145.
22. Fan, S., Shi, K., Weng, J., Yang, Z. (2025). Letting Losses Be Lessons: Human-Machine Cooperation in Maritime Transport. Reliability Engineering & System Safety. Vol. 253. Article 110547. https://doi.org/10.1016/j.ress.2024.110547.
23. Song, R., Papadimitriou, E., Negenborn, R. R., van Gelder, P. (2024). Safety and Efficiency of Human-MASS Interactions: Towards an Integrated Framework. Journal of Marine Engineering and Technology. Vol. 24, No. 3. P. 159–178. https://doi.org/10.1080/20464177.2024.2414959.
Published
2026-06-28
Section
NAVIGATION AND SHIP POWER ENGINEERING
