A hybrid fuzzy modeling framework based on decomposed fuzzy sets and Z-numbers for risk prioritization in air traffic safety with a real case application

Akdemir, E. B.; Kaya, I.

doi:10.22111/ijfs.2025.9347

Document Type : Research Paper

Authors

E. B. Akdemir ¹^{, 2}
I. Kaya ²^{, 3}

¹ Information Technology Institute, TUBITAK BILGEM, Kocaeli, Turkiye

² Department of Industrial Engineering, Yildiz Technical University, ˙ Istanbul, Turkiye

³ Presidency of the Republic of T¨urkiye, The Undersecretariat for Defence Industries, Ankara, Turkiye

https://doi.org/10.22111/ijfs.2025.9347

Abstract

The rapid growth of global air traffic increases the complexity of airspace management, especially around risk manage
ment. To address related safety challenges, this study presents an integrated risk analysis model consists of Functional
Hazard Analysis (FHA), Decomposed Fuzzy Sets (DFS), Z-numbers, and Fuzzy Inference System (FIS). The model
systematically accounts for uncertainty in risk parameters and integrates confidences for experts’ judgments. DFS
assesses the consistency of experts’ evaluations, while Z-numbers represent their reliability. Severity, probability, and
detectability are evaluated within this fuzzy framework, and risks are classified by using IFS-based approach aligned
with the ICAO risk matrix. The model is applied on 11 critical hazard scenarios from the Advanced Surface Movement
Guidance and Control System (A-SMGCS) based on high traffic and low visibility. Results obtained confirm the model’s
ability to identify hazards and prioritize risks, offering a transparent, adaptable, and uncertainty-aware decision-support
tool for aviation safety management.

Keywords

References

[1] V. Aditya, D. S. Aswin, S. V. Dhaneesh, S. Chakravarthy, B. S. Kumar, M. Venkadavarahan, A review on air
traffic flow management optimization: Trends, challenges, and future directions, Discover Sustainability, 5(1)
(2024). http://dx.doi.org/10.1007/s43621-024-00781-7

[2] S. C¸ebi, F. Kutlu G¨undo˘gdu, C. Kahraman, Operational risk analysis in business processes using decomposed
fuzzy sets, Journal of Intelligent and Fuzzy Systems, 43(3) (2022), 2485-2502. http://dx.doi.org/10.3233/
JIFS-213385

[3] C. Chen et al., A new approach for failure mode and effect analysis based on Fermatean fuzzy Z-number weighted
Muirhead mean operator, Engineering Applications of Artificial Intelligence, 143 (2025). http://dx.doi.org/10.
1016/j.engappai.2025.110080

[4] Y. Chen, Y. Zhao, Y. Wu, Recent progress in air traffic flow management: A review, Journal of Air Transport
Management, 116 (2024). http://dx.doi.org/10.1016/j.jairtraman.2024.102573

[5] E. Dudek, K. Krzykowska-Piotrowska, Does free route implementation influence air traffic management system?
Case study in Poland, Sensors, 21(4) (2021). http://dx.doi.org/10.3390/s21041422

[6] EUROCONTROL, EUROCONTROL Specification for Advanced-Surface Movement Guidance and Control Sys
tem (A-SMGCS) Services, Apr. 2020. [Online]. Available: https://www.eurocontrol.int/publication/
eurocontrol-specification-smgcs-services

[7] FlightRadar24, FlightRadar24, 2025. [Online]. Available: https://www.flightradar24.com

[8] A. Florowski, J. Skorupski, Quality assessment of the traffic flow management process in the vicinity of the airport,
Proc. 25th Eur. Saf. Reliab. Conf. ESREL 2015, (2015), 745-751. http://dx.doi.org/10.1201/b19094-100

[9] ICAO, Doc 9830- Advanced Surface Movement Guidance and Control Systems (A-SMGCS) Manual, 2004.

[10] ICAO, Safety Management Manual (SMM), 2018.

[11] ICAO International Civil Aviation Organization, Safety Report 2024 Edition, (2024), 29. [Online]. Available:
https://www.icao.int/safety/Pages/Safety-Report.aspx

[12] International Civil Aviation Organization (ICAO), Doc 9859– Safety Management Manual (SMM), 2012, no.
Third Edition, 2013. [Online]. Available: http://www.icao.int/fsix/_Library/SMM-9859_1ed_en.pdf

[13] W. Kaleta, J. Skorupski, A fuzzy inference approach to analysis of LPV-200 procedures influence on air traffic
safety, Transportation Research Part C: Emerging Technologies, 106 (2019), 264-280. https://doi.org/10.1016/
j.trc.2019.07.001

[14] B. Kang, D. Wei, Y. Li, Y. Deng, A method of converting Z-number to classical fuzzy number, Journal of Information
and Computational Science, 9(3) (2012), 703-709.

[15] A. Kwasiborska, A. Stelmach, Identification of threats and risk assessment in air transport with the use of selected
models and methods, Zeszyty Naukowe SGSP, 86(86) (2023), 77-94. http://dx.doi.org/10.5604/01.3001.0053.
7147

[16] M. Lower, J. Magott, J. Skorupski, Air traffic incidents analysis with the use of fuzzy sets, Lecture Notes in
Computer Science, 7894 LNAI (2013), 306-317. https://doi.org/10.1007/978-3-642-38658-9_28

[17] M. Lower, J. Magott, J. Skorupski, Analysis of air traffic incidents using event trees with fuzzy probabilities, Fuzzy
Sets and Systems, 293 (2016), 50-79. https://doi.org/10.1016/j.fss.2015.11.004

[18] L. Meyer, M. Vogel, H. Fricke, Functional hazard analysis of virtual control towers, IFAC Proceedings Volumes,
43(13) (2010), 146-151. https://doi.org/10.3182/20100831-4-FR-2021.00027

[19] S. Moslem, F. K. G¨undo˘gdu, S. Saylam, F. Pilla, A hybrid decomposed fuzzy multi-criteria decision-making model
for optimizing parcel lockers location, Applied Soft Computing, 154 (2024). https://doi.org/10.1016/j.asoc.
2024.111321

[20] P. Ortner, R. Steinh¨ofler, E. Leitgeb, H. Fl¨uhr, Augmented air traffic control system-AI to predict air traffic
conflicts, AI, 3(3) (2022), 623-644. https://doi.org/10.3390/ai3030036

[21] D. A. Pamplona, C. J. P. Alves, Does a fighter pilot live in the danger zone? A risk assessment applied to military
aviation, Transportation Research Interdisciplinary Perspectives, 5 (2020). https://doi.org/10.1016/j.trip.
2020.100114

[22] R. Rishabh, K. N. Das, A fusion of decomposed fuzzy based decision-making and metaheuristic optimization for
urban transport, Knowledge-Based Systems, 324 (2025). https://doi.org/10.1016/j.knosys.2025.113823

[23] P. Rutkowska, M. Okulicz, J. Skorupski, Comparison of FRAM and CPN approaches for analysis of incidents,
Lecture Notes in Intelligent Transportation and Infrastructure, Part F1380 (2020), 18-28. https://doi.org/10.
1007/978-3-030-38666-5_3

[24] J. Skorupski, Fuzzy risk matrix as a tool for the analysis of air traffic safety, Proc. 26th ESREL 2016, (2017), 455.
https://doi.org/10.1201/9781315374987-423

[25] C. L. Tafur, R. G. Camero, D. A. Rodr´ıguez, J. C. D. Rinc´on, E. R. Saenz, Applications of artificial intelligence
in air operations: A systematic review, Results in Engineering, 25 (2025). https://doi.org/10.1016/j.rineng.
2024.103742

[26] J. Tang, D. Wang, W. Ye, B. Dong, H. Yang, Safety risk assessment of air traffic control system based on game
theory, Sustainability, 14(10) (2022). https://doi.org/10.3390/su14106258

[27] N. T¨uys¨uz, C. Kahraman, A novel decomposed Z-fuzzy TOPSIS method for transfer center selection, Engineering
Applications of Artificial Intelligence, 127 (2024). https://doi.org/10.1016/j.engappai.2023.107221

[28] A. Volpe Lovato, C. Hora Fontes, M. Embiru¸cu, R. Kalid, A fuzzy modeling approach to optimize decision making
in ATC, Computers and Industrial Engineering, 115 (2018), 167-189. https://doi.org/10.1016/j.cie.2017.
11.008

[29] L. A. Zadeh, A note on Z-numbers, Information Sciences, 181(14) (2011), 2923-2932. https://doi.org/10.1016/
j.ins.2011.02.022

Iranian Journal of Fuzzy Systems

A hybrid fuzzy modeling framework based on decomposed fuzzy sets and Z-numbers for risk prioritization in air traffic safety with a real case application

References

References

Volume 22, Issue 4
July and August 2025
Pages 37-55

A hybrid fuzzy modeling framework based on decomposed fuzzy sets and Z-numbers for risk prioritization in air traffic safety with a real case application

References

References

Volume 22, Issue 4July and August 2025Pages 37-55

Volume 22, Issue 4
July and August 2025
Pages 37-55