Article

Received 30.01.2026

Revised 01.06.2026

Accepted 25.06.2026

Published 06.07.2026

Retrieved from Vol. 30, No. 2, 2026

Pages 20 -29

Sakhno, S., & Dobrovolska, А. (2026). Hierarchical fuzzy modelling of risks in road transportation of dangerous goods taking into account the influence of the human factor. The National Transport University Bulletin, 30(2), 20-29. https://doi.org/10.33744/2308-6645-2026-2-30-20-29

Hierarchical fuzzy modelling of risks in road transportation of dangerous goods taking into account the influence of the human factor

Serhii Sakhno*, Аnna Dobrovolska

Mojapochta1@ukr.net

The aim of the study was to develop a hierarchical fuzzy model for assessing the risks of road transportation of dangerous goods, taking into account the influence of the human factor to increase the accuracy and informativeness of decision-making in the field of transport security. The study employed a systematic approach, analysis and synthesis, expert assessment, comparative analysis, and fuzzy modelling based on the Mamdani algorithm, implemented in MATLAB, to assess human factor risks. The research resulted in the development of a hierarchical fuzzy framework for evaluating risks associated with the road transportation of dangerous goods. A key focus of the model is the assessment of human factors based on three input variables: the driver’s health status, their experience in transporting dangerous goods (measured in tonne-kilometres), and the duration of accident-free operation. The fuzzy model, implemented using the Mamdani algorithm, includes 27 fuzzy logic rules and has demonstrated stable behaviour even under varying input parameters. Testing the model on four real drivers produced scores reflecting the quality of the human factor, ranging from 28.5 to 62.6 points. These scores indicate the model’s sensitivity to individual personnel characteristics. An integrated assessment of overall risk is generated through a two-level aggregation of results from sub-models, allowing the system to adapt to different transportation scenarios. This model can be effectively incorporated as a critical component of a logistics operator’s Decision Support System for dynamic risk analysis prior to each transport operation

decision support system; expert evaluation; accident-free experience; membership function; logistics safety
  1. Aliabadi, M.M., Mohammadfam, I., & Khorshidikia, S. (2024). Human error identification and risk assessment in loading and unloading of petroleum products by road trucks using the SHERPA and fuzzy inference system method. Heliyon, 10(15), article number e34072. doi: 10.1016/j.heliyon.2024.e34072.
  2. Beczkowska, S.A., & Grabarek, I. (2021). The importance of the human factor in safety for the transport of dangerous goods. International Journal of Environmental Research and Public Health, 18, article number 7525. doi: 10.3390/ijerph18147525.
  3. Benchkovska, S., Choromansky, V., & Grabarek, I. (2020) Risk and human factor in carriage of dangerous goods by road. Transport Problems, 15(4), 19-28. doi: 10.21307/tp-2020-044.
  4. Bozhko, K., Antonenko, N., Ilchenko, V., Navrotska, T., Halak, I., & Zaplitna, T. (2024a). Assessment of investment efficiency in human capital of a company using fuzzy modeling method based on linear and logarithmic input scales. In R.E. Khoury & N. Nasrallah (Eds.), Intelligent systems, business, and innovation research. Studies in systems, decision and control (Vol. 489, pp. 151-166). Cham: Springer. doi: 10.1007/978-3-031-36895-0_14.
  5. Bozhko, K., Antonenko, N., Masalitina, V., Turpak, T., Petrovska, S., & Shcherbakova, N. (2024b). Variable assessment of a start-up projects by an investor based on fuzzy models. In R. El Khoury (Ed.), Technology-driven business innovation: Unleashing the digital advantage. Studies in systems, decision and control (Vol. 2, pp. 161-173). Cham: Springer. doi: 10.1007/978-3-031-62656-2_15.
  6. Erküvün, K.S., Usta, S., & Güneri, A.F. (2025). Risk assessment of hazardous materials transportation with fuzzy Fine Kinney and fuzzy AHP methods. International Journal of Reliability, Quality and Safety Engineering, 33(2). doi: 10.1142/S0218539325500524.
  7. Fornalchyk, Y., Afonin, M., Postranskyy, T., & Boikiv, M. (2021). Risk assessment during the transportation of dangerous goods considering the functional state of the driver. Transport Problems, 16(1), 135-146. doi: 10.21307/tp-2021-012.
  8. Gul, M., Yucesan, M., & Ak, M.F. (2022). Control measure prioritization in Fine-Kinney-based risk assessment: A Bayesian BWM-fuzzy VIKOR combined approach in an oil station. Environmental Science and Pollution Research, 29(39), 59385-59402. doi: 10.1007/s11356-022-19454-x.
  9. Luan, T., Zhang, X., Li, H., Wang, K., & Li, X. (2024). Dynamic risk analysis of hazardous materials highway tunnel transportation based on fuzzy Bayesian network. SSRN. doi: 10.2139/ssrn.4777441.
  10. Ma, F., Wu, R., Xue, B., Li, J., & Wang, X. (2025). Transportation risk assessment for hazardous chemical road transport companies using an extreme gradient boosting model. Transportation Research Record: Journal of the Transportation Research Board, 2679(6), 770-784. doi: 10.1177/03611981251320383.
  11. Matić, B., Marinković, M., Jovanović, S., Sremac, S., & Stević, Ž. (2022). Intelligent novel IMF D-SWARA – rough MARCOS algorithm for selection construction machinery for sustainable construction of road infrastructure. Buildings, 12(7), article number 1059. doi: 10.3390/buildings12071059.
  12. Milošević, T.D., Pamučar, D.S., & Chatterjee, P. (2021). Model for selecting a route for the transport of hazardous materials using a fuzzy logic system. Military Engineering Bulletin, 69(2), 355-390. doi: 10.5937/vojtehg69-29629.
  13. Mohammadfam, I., Zarei, E., Yazdi, M., & Gholamizadeh, K. (2022). Quantitative risk analysis on rail transportation of hazardous materials. Mathematical Problems in Engineering, 2022(1), article number 162829. doi: 10.1155/2022/6162829.
  14. Panchyshyn, T., Loik, R., & Demchyshak, N. (2024). Investing in human capital development in the conditions of intellectualization of the economy. Economy and Society, 69. doi: 10.32782/2524-0072/2024-69-143.
  15. Satıcı, S.R., & Mete, S. (2024). Fine-Kinney-based occupational risk assessment using Pythagorean fuzzy AHP-COPRAS for the lifting equipment in the energy distribution and investment sector. Gazi University Journal of Science, 37(2), 854-873. doi: 10.35378/gujs.1227756.
  16. Stević, Ž., Das, D.K., Tešić, R., Vidas, M., & Vojinović, D. (2022). Objective criticism and negative conclusions on using the fuzzy SWARA method in multi-criteria decision making. Mathematics, 10(4), article number 635. doi: 10.3390/math10040635.
  17. Tsopa, V., Borodina, N., Cheberiachko, S., Deryugin, O., Hilpert, V., & Borovytskyi, O. (2022). Assessment of professional risks of the driver of a technological truck for forestry conditions. Social Development and Security, 12(5), 93-110. doi: 10.33445/sds.2022.12.5.9.
  18. Vahabzadeh, S., Haghshenas, S.S., Ghoushchi, S.J., Guido, G., Simic, V., & Marinkovic, D. (2025). A new framework for risk assessment of road transportation of hazardous substances. Facta Universitatis, Series: Mechanical Engineering, 23(3), 479-509. doi: 10.22190/FUME240801007V.
  19. Vojinović, N., Sremac, S., & Zlatanović, D. (2021). A novel integrated fuzzy-rough MCDM model for evaluation of companies for transport of dangerous goods. Complexity, 2021(1), article number 5141611. doi: 10.1155/2021/5141611.
  20. Wang, Z., Zhao, L., Cao, N., Yu, M., & Chen, M. (2018). The route choice of hazardous material transportation with value-at-risk model using chance measure in uncertain environments. Advances in Mechanical Engineering, 10(2). doi: 10.1177/1687814017753801.
  21. WMA. (2024). WMA Declaration of Helsinki – ethical principles for medical research involving human participants. Retrieved from https://www.wma.net/policies-post/wma-declaration-of-helsinki/.

https://doi.org/10.33744/2308-6645-2026-2-30-20-29