Hostname: page-component-5cf477f64f-mgq6s Total loading time: 0 Render date: 2025-03-31T01:41:45.602Z Has data issue: false hasContentIssue false
  • English
  • Français

A fuzzy-hybrid SHELL framework for predicting air traffic conflict detection and resolution performance: a human-factors perspective

Published online by Cambridge University Press:  27 March 2025

Fitri Trapsilawati Open the ORCID record for Fitri Trapsilawati [Opens in a new window]
Fitri Trapsilawati*
Affiliation:
Department of Mechanical and Industrial Engineering, Faculty of Engineering, Universitas Gadjah Mada, Jalan Grafika no. 2, Yogyakarta 55281, Indonesia
*
Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The complex tasks of air traffic control (ATC) and the various factors affecting its operation have shed light on the need to build a model to predict conflict detection and resolution (CDR) performance within a traffic situation. This study aimed at developing a fuzzy-hybrid framework for quantifying various aspects in ATC consisting of the software, hardware, environment, liveware and organisation (i.e. the SHELL model) to predict CDR performance. The proposed fuzzy-hybrid SHELL framework in this study was tested using metadata from 10 prior studies in ATC. The results showed a highly accurate prediction, as indicated by the RMSE and MAPE values of 0⋅09 and 5⋅36%, respectively, indicating a high consistency of 90⋅92% for predicting the CDR performance. This framework offers a promising approach for Air Navigation Service Providers (ANSPs) to maintain air traffic safety and improve ATC operations efficiency.

Keywords

fuzzy logicSHELLconflict detection and resolution
Type
Research Article
Information
The Journal of Navigation , First View , pp. 1 - 18
Check for updates
Copyright
Copyright © The Author(s), 2025. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ayhan, S., Costas, P. and Samet, H. (2019). A data-driven framework for long-range aircraft conflict detection and resolution. ACM Transactions on Spatial Algorithms and Systems (TSAS), 5(4), 1–23.CrossRefGoogle Scholar
Bernhardt, K. A., Poltavski, D., Petros, T., Ferraro, F. R., Jorgenson, T., Carlson, C., Drechsel, P. and Iseminger, C. (2019). The effects of dynamic workload and experience on commercially available EEG cognitive state metrics in a high-fidelity air traffic control environment. Applied Ergonomics, 77, 83–91.CrossRefGoogle Scholar
Brudnicki, D. J., Lindsay, K. S. and McFarland, A. L. (1997) Assessment of Field Trials, Algorithmic Performance, and Benefits of the User Request Evaluation Tool (URET) Conflict Probe. Proceedings 16th DASC. AIAA/IEEE Digital Avionics Systems Conference. Reflections to the Future, Vol. 2, 3–9.Google Scholar
Carvalho, J. G. Jr and Costa, C. T. Jr. (2017). Identification method for fuzzy forecasting models of time series. Applied Soft Computing, 50, 166–182.CrossRefGoogle Scholar
Chang, Y.-H. and Yeh, C.-H. (2010). Human performance interfaces in air traffic control. Applied Ergonomics, 41(1), 123–129.CrossRefGoogle ScholarPubMed
Chiappe, D., Vu, K.-P. L. and Strybel, T. (2012). Situation awareness in the NextGen air traffic management system. International Journal of Human-Computer Interaction, 28(2), 140–151.CrossRefGoogle Scholar
Edwards, T., Homola, J., Mercer, J. and Claudatos, L. (2017). Multifactor interactions and the air traffic controller: The interaction of situation awareness and workload in association with automation. Cognition, Technology & Work, 19(4), 687–698.CrossRefGoogle Scholar
Endsley, M. R. (2020). The divergence of objective and subjective situation awareness: A meta-analysis. Journal of Cognitive Engineering and Decision Making, 14(1), 34–53.CrossRefGoogle Scholar
Fedorov, A., Holovniak, D., Khudov, H. and Misiyuk, G. (2019). Method of Radar Adjustment with Automatic Dependent Surveillance Technology Use. 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T), 402–406.CrossRefGoogle Scholar
Fothergill, S. and Neal, A. (2008). The effect of workload on conflict decision making strategies in air traffic control. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 52(1), 39–43.CrossRefGoogle Scholar
ICAO. (2012). Safety Management Manual. https://www.icao.int/sam/documents/rst-smsssp-13/smm_3rd_ed_advance.pdfGoogle Scholar
ICAO. (2019). Application of Separation Minima - North Atlantic Region. International Civil Aviation Organisation. https://www.icao.int/EURNAT/EUR and NAT Documents/NAT Documents/NAT Documents/NAT Doc 008 - NAT ASM/NAT Doc 008 (EN) - Edition 01, Amd 9.pdfGoogle Scholar
Imbert, J.-P., Hodgetts, H. M., Parise, R., Vachon, F., Dehais, F. and Tremblay, S. (2014). Attentional costs and failures in air traffic control notifications. Ergonomics, 57(12), 1817–1832.Google ScholarPubMed
Jiménez-Martín, A., Tello, F. and Mateos, A. (2020). A variation of the ATC work shift scheduling problem to deal with incidents at airport control centres. Mathematics, 8(3), 321.CrossRefGoogle Scholar
Jouffe, L. (1998). Fuzzy inference system learning by reinforcement methods. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 28(3), 338–355.CrossRefGoogle Scholar
Kallus, K. W., Van Damme, D. and Dittman, A. (1999). Integrated job and task analysis of air traffic controllers: Phase 2. Task Analysis of En-Route Controllers (European Air Traffic Management Programme Rep. No. HUM. ET1. ST01. 1000-REP-04). Brussels: EUROCONTROL.Google Scholar
Kang, Z. and Landry, S. J. (2014). Using scanpaths as a learning method for a conflict detection task of multiple target tracking. Human Factors, 56(6), 1150–1162.CrossRefGoogle ScholarPubMed
Kaur, J. and Khehra, B. S. (2022). Fuzzy logic and hybrid based approaches for the risk of heart disease detection: State-of-the-art review. Journal of The Institution of Engineers (India): Series B, 103, 681–697.Google Scholar
Kearney, P., Li, W.-C. and Lin, J. J. H. (2016). The impact of alerting design on air traffic controllers’ response to conflict detection and resolution. International Journal of Industrial Ergonomics, 56, 51–58.CrossRefGoogle Scholar
Kearney, P., Li, W.-C., Yu, C.-S. and Braithwaite, G. (2019). The impact of alerting designs on air traffic controller's eye movement patterns and situation awareness. Ergonomics, 62(2), 305–318.CrossRefGoogle ScholarPubMed
Klomp, R., Borst, C., van Paassen, R. and Mulder, M. (2015). Expertise level, control strategies, and robustness in future air traffic control decision aiding. IEEE Transactions on Human-Machine Systems, 46(2), 255–266.CrossRefGoogle Scholar
Lewis, C. (1982). International and Business Forecasting Methods. London: Butterworths.Google Scholar
Lin, Y.-C., Lai, H.-H. and Yeh, C.-H. (2007). Consumer-oriented product form design based on fuzzy logic: A case study of mobile phones. International Journal of Industrial Ergonomics, 37(6), 531–543.CrossRefGoogle Scholar
Liu, W. and Hwang, I. (2011). Probabilistic trajectory prediction and conflict detection for air traffic control. Journal of Guidance, Control, and Dynamics, 34(6), 1779–1789.CrossRefGoogle Scholar
Loft, S., Sanderson, P., Neal, A. and Mooij, M. (2007). Modeling and predicting mental workload in en route air traffic control: Critical review and broader implications. Human Factors, 49(3), 376–399.CrossRefGoogle ScholarPubMed
Loft, S., Finnerty, D. and Remington, R. W. (2011). Using spatial context to support prospective memory in simulated air traffic control. Human Factors, 53(6), 662–671.CrossRefGoogle ScholarPubMed
Lovato, A. V., Fontes, C. H., Embiruçu, M. and Kalid, R. (2018). A fuzzy modeling approach to optimize control and decision making in conflict management in air traffic control. Computers & Industrial Engineering, 115, 167–189.CrossRefGoogle Scholar
Lupu, M., Feron, E. and Mao, Z.-H. (2010). Traffic Complexity of Intersecting Flows of Aircraft Under Variations of Pilot Preferences in Maneuver Choice. 49th IEEE Conference on Decision and Control (CDC), 1189–1194.CrossRefGoogle Scholar
Marchitto, M., Benedetto, S., Baccino, T. and Cañas, J. J. (2016). Air traffic control: Ocular metrics reflect cognitive complexity. International Journal of Industrial Ergonomics, 54, 120–130.Google Scholar
Mateos, A., Tello, F., Jiménez-Martín, A. and de Pozo, J. A. F. (2018). The ATC Work Shift Scheduling Problem Based on Multistart Simulated Annealing and Regular Expressions. 2018 5th International Conference on Control, Decision and Information Technologies (CoDIT), 152–157.CrossRefGoogle Scholar
Maués, L. M. F., Sá, J. A. S. D., Costa, C. T. D., Kern, A. P. and Duarte, A. A. A. M. (2019). Construction duration predictive model based on factorial analysis and fuzzy logic. Ambiente Construído, 19, 115–133.CrossRefGoogle Scholar
Mercer, J., Gomez, A., Gabets, C., Bienert, N., Edwards, T., Martin, L., Gujral, V. and Homola, J. (2016). Impact of automation support on the conflict resolution task in a human-in-the-loop air traffic control simulation. IFAC-PapersOnLine, 49(19), 36–41.CrossRefGoogle Scholar
Mirchi, T., Vu, K.-P., Miles, J., Sturre, L., Curtis, S. and Strybel, T. Z. (2015). Air traffic controller trust in automation in NextGen. Procedia Manufacturing, 3, 2482–2488.Google Scholar
Nolan, M. S. (2010). Fundamentals of air traffic control (5th ed.). Delmar Cengage Learning.Google Scholar
Nunes, A. and Laursen, T. (2004). Identifying the factors that contributed to the Überlingen Midair collision. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 48(1), 195–198.Google Scholar
Paglione, M. and Oaks, R. (2007). Implementation and Metrics for a Trajectory Prediction Validation Methodology. AIAA Guidance, Navigation and Control Conference and Exhibit, p. 6517.Google Scholar
Pandey, M. M. and Shukla, D. (2019). Evaluating the human performance factors of air traffic control in Thailand using fuzzy multi criteria decision making method. Journal of Air Transport Management, 81, 101708.Google Scholar
Pekela, W. and Hilburn, B. (1997). Air Traffic Controller Strategies in Resolving Free Flight Traffic Conflicts-The Effect of Enhanced Controller Displays for Situation Awareness. AIAA and SAE, 1998 World Aviation Conference, p. 5583.Google Scholar
Pérez-Castán, J. A., Rodríguez-Sanz, Á, Gómez Comendador, V. F. and Arnaldo Valdés, R. M. (2019). Atc separation assurance for rpass and conventional aircraft in en-route airspace. Safety, 5(3), 41.Google Scholar
Reva, A. M. and Borsuk, S. P. (2021). Fuzzy model of air traffic controller attitude to the risk during decision making. Advances in Human Aspects of Transportation: Part II, 16, 468.Google Scholar
Ruiz, S., Piera, M. A. and Del Pozo, I. (2013). A medium term conflict detection and resolution system for terminal maneuvering area based on spatial data structures and 4D trajectories. Transportation Research Part C: Emerging Technologies, 26, 396–417.Google Scholar
Sanda, M.-A. (2018). Relevance of air-traffic controllers’ tacit knowledge in enhancing air-traffic control and safety in Ghanaian airspace. International Journal of Human Factors Modelling and Simulation, 6(2–3), 103–118.CrossRefGoogle Scholar
Shcherban, A. and Ieremenko, V. (2020). Uav flight safety system based on fUzzy logic. Transactions on Aerospace Research, 2020(4), 71–80.CrossRefGoogle Scholar
Son, L. H. (2017). Measuring analogousness in picture fuzzy sets: From picture distance measures to picture association measures. Fuzzy Optimization and Decision Making, 16(3), 359–378.CrossRefGoogle Scholar
Thomas, L. C. and Wickens, C. D. (2005). Effects of CDTI Display Dimensionality and Conflict Geometry on Conflict Resolution Performance. 2005 International Symposium on Aviation Psychology, p. 747.Google Scholar
Thomas, L. C. and Wickens, C. D. (2008). Display dimensionality and conflict geometry effects on maneuver preferences for resolving in-flight conflicts. Human Factors, 50(4), 576–588.CrossRefGoogle ScholarPubMed
Trapsilawati, F., Qu, X., Wickens, C. D. and Chen, C.-H. (2015). Human factors assessment of conflict resolution aid reliability and time pressure in future air traffic control. Ergonomics, 58(6), 897–908. doi:10.1080/00140139.2014.997301CrossRefGoogle ScholarPubMed
Trapsilawati, F., Wickens, C. D., Qu, X. and Chen, C.-H. (2016). Benefits of imperfect conflict resolution advisory AIDS for future air traffic control. Human Factors, 58(7), 1007–1019. doi:10.1177/0018720816655941Google ScholarPubMed
Trapsilawati, F., Nugraheni, A. S. A. N. and Herliansyah, M. K. (2018). Analysis of Air Traffic Conflict Geometry on Brain Activity. Proceedings - 2018 4th International Conference on Science and Technology, ICST 2018. doi:10.1109/ICSTC.2018.8528635CrossRefGoogle Scholar
Trapsilawati, F., Herliansyah, M. K., Nugraheni, A. S. A. N. S., Fatikasari, M. P. and Tissamodie, G. (2020). EEG-based analysis of air traffic conflict: Investigating controllers’ situation awareness, stress level and brain activity during conflict resolution. The Journal of Navigation, 73(3), 678–696.Google Scholar
Trapsilawati, F., Chen, C.-H., Qu, X. and Wickens, C. D. (2021). Integration of conflict resolution automation and vertical situation display for on-ground air traffic control operations. The Journal of Navigation, 74(3), 619–632.Google Scholar
Trapsilawati, F., Wickens, C. D., Herliansyah, M. K., Sari, M. P. F. and Tissamodie, G. (2022). Why do controllers choose the conflict resolution maneuvers that they do? The International Journal of Aerospace Psychology, 32(1), 24–38. doi:10.1080/24721840.2021.1925119Google Scholar
Vuckovic, A., Sanderson, P., Neal, A., Gaukrodger, S. and Wong, B. L. W. (2013). Relative position vectors: An alternative approach to conflict detection in air traffic control. Human Factors, 55(5), 946–964.Google ScholarPubMed
Wang, X. and Kerre, E. E. (2001). Reasonable properties for the ordering of fuzzy quantities (I). Fuzzy Sets and Systems, 118(3), 375–385.Google Scholar
Wang, Y., Wang, L., Lin, S., Cong, W., Xue, J. and Ochieng, W. (2021). Effect of working experience on air traffic controller eye movement. Engineering, 7(4), 488–494.CrossRefGoogle Scholar