Advances in aircraft and spacecraft science

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Automatic terminal information service: Key element for increasing safety and efficiency

  • Tomas Hoika (Faculty of Military Technology, University of Defence) ;
  • Jiri Jansky (Faculty of Military Technology, University of Defence) ;
  • Sarka Hoskova-Mayerova (Faculty of Military Technology, University of Defence)
  • Received : 2024.10.26
  • Accepted : 2025.07.30
  • Published : 2025.09.25
⟨ Previous Next ⟩

Abstract

Determining sector capacity is a fundamental pillar for ensuring safe and effective air traffic management. Sector capacity is usually expressed through the number of aircraft that are allowed to enter a given sector in one hour. Controlling the flow of aircraft and regulating their number in the space ensures that the air traffic controller is able to maintain safe distance between the aircraft and does not exceed the maximum level of permissible workload. Each air navigation services provider is thus looking for ways to increase sector capacity. One of the innovative approaches to optimizing sector capacity is the use of the Automatic Terminal Information Service (ATIS). When comparing the controller availability factor and terminal area capacity with and without ATIS, it was found that the controller availability factor increased by 2.5% after the introduction of ATIS, and the sector capacity increased by 36.2%. This evidence confirms that implementing ATIS has a positive effect on the overall capacity of the Terminal maneuvering area, with a probability greater than 95% that these changes did not arise due to random variation. Such an integrative approach is proving to be a promising path to more efficient and safer air traffic. The reduction in transmission duration means air traffic controllers spend less time communicating ATIS information, allowing more time for traffic control and separation planning. This increased efficiency translates to a higher sector capacity, enabling controllers to manage more aircraft simultaneously.

Keywords

Acknowledgement

This work was supported by the Project for the Development of the Organization DZRO "Military Autonomous and Robotic Systems" and "AIROPS" under the Ministry of Defence and Armed Forces of Czech Republic.

References

  1. Bauer, M. and Kalvoda, J. (2020), "Workload features inside air traffic control electronic transfer environment", Adv. Milit. Technol., 15(1), 191-199. https://doi.org/10.3849/aimt.01356.
  2. Behl, P. and Charulatha, S. (2024), "Enhancing air traffic management efficiency through edge computing and image-aided navigation", Adv. Aircraft Spacecraft Sci., 11(1), 33-53. https://doi.org/10.12989/aas.2024.11.1.033.
  3. Cary, L. (2024), Capacity: Factors to Consider, Federal Aviation Administration. https://www.icao.int/Meetings/AMC/MA/2006/atfm/pres12.pdf.
  4. Di Mascio, P., Pontillo, A., Ponziani, A., Dinu, R. and Moretti, L. (2023), "Entry count vs occupancy count to assess sector capacity with fast time simulation", Eur. Transp./Trasporti Europei. 95, 1-16. https://doi.org/10.48295/ET.2023.95.3.
  5. Jaurena, R.A. (2009), "Guide for the application of a common methodology to estimate airport and ATC sector capacity for the SAM region", Regional Project: ICAO RLA/06/901.
  6. Juricic, B., Babic, R., Škurla, M. and Francetic, I. (2011), "Zagreb terminal airspace capacity analysis", PROMET-Traff. Transp., 23(5), 367-375. https://doi.org/10.7307/ptt.v23i5.155.
  7. Korecki, Z., Janošek M. and Pecháček, T. (2021), "Use of unmanned aerial systems in airport operations", 2021 International Conference on Military Technologies (ICMT), Brno, Czech Republic. https://doi.org/10.1109/ICMT52455.2021.9502756.
  8. Mikula, B., Kalavský, P. and Klir, R. (2021), "Proactive mechanisms against occurrences in civil aviation", 2021 New Trends in Aviation Development (NTAD), Košice, Slovakia. https://doi.org/10.1109/NTAD54074.2021.9746496.
  9. Pejovic, T., Netjasov, F. and Crnogorac, D. (2020), "Relationship between air traffic demand, safety and complexity in high-density airspace in Europe", Risk Assess. Air Traff. Manage., 19. https://doi.org/10.5772/intechopen.88801.
  10. Radisic, T., Andraši, P., Novak, D., Juričic, B. and Antulov-Fantulin, B. (2020), "Air traffic complexity as a source of risk in ATM", Risk Assess. Air Traff. Manage., 63. https://doi.org/10.5772/intechopen.90310.
  11. Rydin, A. (2013), Stockholm TMA Capacity–A Study of The Landing Rate and Its Effects on Arrival Outcome, Linköping University, Sweden.
  12. Sesar (2013), Airports–the ATM bottleneck?, http://www.sesarju.eu/programme/highlights/sesar-focus-airports-atm-bottleneck.
  13. Skybrary (2024), Workload (OGHFA BN), Skybrary Aviation Safety. https://skybrary.aero/articles/workload-oghfa-bn.
  14. Suárez, M.Z., Valdés, R.M.A., Moreno, F.P., Jurado, R.D.A., de Frutos, P.M.L. and Comendador, V.F.G. (2024), "Understanding the research on air traffic controller workload and its implications for safety: A science mapping-based analysis", Saf. Sci., 176, 106545. https://doi.org/10.1016/j.ssci.2024.106545.
  15. Tomaszewska, J. (2023), "Application of Markov chains, MTBF and machine learning in air transport reliability", Aviat. Secur. Issue., 4(2), 83-106. https://doi.org/10.55676/asi.v4i2.81.
  16. Triyanti, V., Azis, H.A. and Iridiastadi, H. (2020), "Workload and fatigue assessment on air traffic controller", IOP Conf. Ser.: Mater. Sci. Eng., 847(1), 012087. https://doi.org/10.1088/1757-899X/847/1/012087.
  17. Wided, A. and Fatima, B. (2022), "Effective simulation-based optimization algorithm for the aircraft runway scheduling problem", Adv. Aircraft Spacecraft Sci., 9(4), 335. https://doi.org/10.12989/aas.2022.9.4.335.
  18. Yazgan, E., Sert, E. and Şimsek, D. (2021), "Overview of studies on the cognitive workload of the air traffic controller", Int. J. Aviat. Sci. Technol., 2(1), 28-36. https://doi.org/10.23890/IJAST.vm02is01.0104.