The Use of Artificial Intelligence in Air Traffic Control: A Theoretical Review

Abstract

Aviation is a growing industry critical to commercial and military operations worldwide. This growth brings with it more complex and intensive air traffic management requirements. Air traffic control is a critical service operating within airports and airspace to ensure aircraft's safe, efficient and orderly routing. However, as the traffic density in the airspace increases, human operators are more likely to make mistakes and existing air traffic management systems may be inadequate. At this point, applying artificial intelligence (AI) technologies in air traffic control offers a significant innovation to the sector. This paper examines how AI technologies are used in air traffic control. The opportunities offered by technologies such as machine learning, deep learning, unmanned aerial vehicles (UAVs) and blockchain in air traffic control are discussed, and the role of these technologies in reducing human errors, increasing efficiency and optimizing decision-making processes is discussed. In particular, the integration of UAVs into the airspace and the contributions of AI in managing this process were emphasized. In addition, the benefits of blockchain technology in terms of data security and traceability are evaluated.

Keywords

• Air traffic control
• artificial intelligence
• air transportation

Hava Trafik Kontrolünde Yapay Zeka Kullanimi: Teorik Bir İnceleme

Abstract

Havacılık, dünya çapında ticari ve askeri operasyonlar için kritik öneme sahip, büyüyen bir sektördür. Bu büyüme, daha karmaşık ve yoğun hava trafik yönetimi gereksinimlerini de beraberinde getirmektedir. Hava trafik kontrolü, uçakların güvenli, verimli ve düzenli bir şekilde yönlendirilmesini sağlamak için havaalanları ve hava sahası içinde faaliyet gösteren kritik bir hizmettir. Ancak hava sahasındaki trafik yoğunluğu arttıkça, insan operatörlerin hata yapma olasılığı artmakta ve mevcut hava trafik yönetim sistemleri yetersiz kalabilmektedir. Bu noktada, hava trafik kontrolünde yapay zekâ teknolojilerinin uygulanması sektöre önemli bir yenilik sunmaktadır. Bu makale, yapay zekâ teknolojilerinin hava trafik kontrolünde nasıl kullanıldığını incelemektedir. Makine öğrenimi, derin öğrenme, insansız hava araçları (İHA) ve blok zinciri gibi teknolojilerin hava trafik kontrolünde sunduğu fırsatlar ele alınmakta ve bu teknolojilerin insan hatalarının azaltılması, verimliliğin artırılması ve karar alma süreçlerinin optimize edilmesindeki rolü tartışılmaktadır. Özellikle İHA'ların hava sahasına entegrasyonu ve bu sürecin yönetilmesinde yapay zekânın katkıları üzerinde durulmuştur. Ayrıca blockchain teknolojisinin veri güvenliği ve izlenebilirlik açısından faydaları değerlendirilmiştir.

Keywords

• Hava trafik kontrolü
• yapay zeka
• hava taşımacılığı

References

1. Abdillah, R. E., Moenaf, H., Rasyid, L. F., Achmad, S., & Sutoyo, R. (2024). Implementation of Artificial Intelligence on Air Traffic Control-A Systematic Literature Review. (s. 1-7). 18th International Conference on Ubiquitous Information Management and Communication (IMCOM) IEEE.
2. Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019). Applications of artificial intelligence in transport: An overview. Sustainability, 11(189), 1-24.
3. Al Radi, M., AlMallahi, M. N., Al-Sumaiti, A. S., Semeraro, C., Abdelkareem, M. A., & Olabi, A. G. (2024). Progress in artificial intelligence-based visual servoing of autonomous unmanned aerial vehicles (UAVs). International Journal of Thermofluids, 21(100590), 1-15.
4. Alam, M. S., Deb, J. B., Al Amin, A., & Chowdhury, S. (2024). An artificial neural network for predicting air traffic demand based on socio-economic parameters. Decision Analytics Journal, 10(100382), 1-13.
5. Allouch, A., Cheikhrouhou, O., Koub, A., Toumi, K., Khalgui, M., & Nguyen Gia, T. (2021). UTM-Chain: Blockchain-Based Secure Unmanned Traffic Management for Internet of Drones. Sensors, 21(3049), 1-20.
6. Astarita, V., Giofrè, V. P., Mirabelli, G., & Solina, V. (2019). A review of blockchain-based systems in transportation. Information, 11(1), 1-24.
7. Bernsmed, K., Bour, G., Lundgren, M., & Bergström, E. (2022). An evaluation of practitioners’ perceptions of a security risk assessment methodology in air traffic management projects. Journal of Air Transport Management, 102(102223), 1-18.
8. Blasch, E., Xu, R., Chen, Y., Chen, G., & Shen, D. (2019). Blockchain methods for trusted avionics systems . In 2019 IEEE National Aerospace and Electronics Conference (NAECON) (pp. 192-199). IEEE.

9. Carramiñana, D., Campaña, I., Bergesio, L., Bernardos, A., & Besada, J. (2021). Sensors and Communication Simulation for Unmanned Traffic Management. Sensors, 21(927), 1-29.
10. Dave, G., Choudhary, G., Sihag, V., You, I., & Choo, K. K. (2022). Cyber security challenges in aviation communication, navigation, and surveillance. Computers & Security, 112(102516), 1-14.
11. Degas, A., Islam, M., Hurter, C., Barua, S., Rahman, H., Poudel, M., . . . Aricó, P. (2022). A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Futuure Research Trajectory. Appl. Sci., 12(1295), 1-47.
12. IATA. (2023). Air Passenger Market Analysis. 1-5. https://www.iata.org/en/iata-repository/publications/economic-reports/air-passenger-market-analysis-december-2023/ adresinden alındı
13. Kabashkin, I., Misnevs, B., & Zervina, O. (2023). Artificial Intelligence in Aviation: New Professionals for New Technologies. Applied Sciences, 13(11660), 1-33.
14. Kanat, Ö. Ö. (2023). The Significance of Unmanned Aerial Vehicles (UAVs) in Strategic Contexts. Anadolu Strateji Dergisi, 5(2), 75-87.
15. Karger, E., Jagals, M., & Ahlemann, F. (2021). Blockchain for Smart Mobility—Literature Review and Future Research Agenda. Sustainability, 13(13268), 1-32.
16. Keith, A., Sangarapillai, T., Almehmadi, A., & El-Khatib, K. (2023). A Blockchain-Powered Traffic Management System for Unmanned Aerial Vehicles. Applied Sciences, 13(10950), 1-27.
17. Kim, J.-H., Lee, S., & Hong, S. (2021). Autonomous Operation Control of IoT Blockchain Networks. Electronics, 10(204), 1-16.
18. Kimani, K., Oduol, V., & Langat, K. (2019). Cyber security challenges for IoT-based smart grid networks. International journal of critical infrastructure protection, 25, 36-49.
19. Li, X., Lai, P. L., Yang, C. C., & Yuen, K. F. (2021). Determinants of blockchain adoption in the aviation industry: Empirical evidence from Korea. Journal of Air Transport Management, 97(102139), 1-11.
20. Lu, X., Dong, R., Wang, Q., & Zhang, L. (2023). Information Security Architecture Design for Cyber-Physical Integration System of Air Traffic Management. Electronics , 12(1665), 1-29.
21. Nikitas, A., Michalakopoulou, K., Njoya, E. T., & Karampatzakis, D. (2020). Artificial intelligence, transport and the smart city: Definitions and dimensions of a new mobility era. Sustainability, 12(2789), 1-19.
22. Ogunsina, K., & DeLaurentis, D. (2022). Enabling integration and interaction for decentralized artificial intelligence in airline disruption management. Engineering Applications of Artificial Intelligence, 109(104600), 1-18.
23. Ortner, P., Steinhöfler, R., Leitgeb, E., & Flühr, H. (2022). Augmented Air Traffic Control System—Artificial Intelligence as Digital Assistance System to Predict Air Traffic Conflicts. AI, 3, 623–644.
24. Pérez-Castán, J., Pérez Sanz, L., Fernández-Castellano, M., Radiši´c, T., Samardži´c, K., & Tukari´c, I. (2022). Learning Assurance Analysis for Further Certification Process of Machine Learning Techniques: Case-Study Air Traffic Conflict Detection Predictor. Sensors , 22(7680), 1-14.
25. Samir Labib, N., Danoy, G., Musial, J., Brust, M. R., & Bouvry, P. (2019). Internet of unmanned aerial vehicles—A multilayer low-altitude airspace model for distributed UAV traffic management. Sensors, 19(4779), 1-22.
26. Shah, Z., Ullah, I., Li, H., Levula, A., & Khurshid, K. (2022). Blockchain Based Solutions to Mitigate Distributed Denial of Service (DDoS) Attacks in the Internet of Things (IoT): A Survey. Sensors, 22(1094), 1-26.
27. SHGM. (2023). Sivil Havacılık Genel Müdürlüğü Faaliyet Raporu. 1-122. https://web.shgm.gov.tr/documents/sivilhavacilik/files/kurumsal/faaliyet/2023.pdf adresinden alındı
28. SHGM. (2024). https://web.shgm.gov.tr/tr/havacilik-personeli/2129-hava adresinden alındı
29. Sui, D., Liu, K., & Li, Q. (2022). Dynamic Prediction of Air Traffic Situation in Large-Scale Airspace. Aerospace, 9(568), 1-15.
30. Sui, D., Ma, C., & Wei, C. (2023). Tactical Conflict Solver Assisting Air Traffic Controllers Using Deep Reinforcement Learning. Aerospace, 10(182), 1-24.
31. Tang, J., Liu, G., & Pan, Q. (2022). Review on artificial intelligence techniques for improving representative air traffic management capability. Journal of Systems Engineering and Electronics, 33(5), 1123-1134.
32. Tsao, K. Y., Girdler, T., & Vassilakis, V. G. (2022). A survey of cyber security threats and solutions for UAV communications and flying ad-hoc networks. Ad Hoc Networks, 133(102894), 1-39.
33. Tselentis, D. I., Papadimitriou, E., & van Gelder, P. (2023). The usefulness of artificial intelligence for safety assessment of different transport modes. Accident Analysis & Prevention, 186(107034), 1-10.
34. Ukwandu, E., Ben-Farah, M., Hindy, H., Bures, M., Atkinson, R., Tachtatzis, C., . . . Bellekens, X. (2022). Cyber-Security Challenges in Aviation Industry: A Review of Current and Future Trends. Information, 13(146), 1-22.
35. Wu, Z., Dong, R., & Wang, P. (2022). Research on Game Theory of Air Traffic Management Cyber Physical System Security. Aerospace, 9(397), 1-19.
36. Xie, Y., Pongsakornsathien, N., Gardi, A., & Sabatini, R. (2021). Explanation of Machine-Learning Solutions in Air-Traffic Management. Aerospace, 8(224), 1-25.
37. Yazici, İ., Shayea, I., & Din, J. (2023). A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems. Engineering Science and Technology, an International Journal, 44(101455), 1-40.
38. Yetgin, M. A., & Baştuğ, M. (2023). Sivil Havacılık Şirketlerinin İnsansız Hava Aracı Stratejileri. Türkiye İnsansız Hava Araçları Dergisi, 5(2), 72-80.
39. Zaoui, A., Tchuente, D., Wamba, S. F., & Kamsu-Foguem, B. (2024). Impact of artificial intelligence on aeronautics: An industry-wide review. Journal of Engineering and Technology Management, 71(101800), 1-19.
40. Ziakkas, D., & Pechlivanis, K. (2023). Artificial intelligence applications in aviation accident classification: A preliminary exploratory study. Decision Analytics Journal, 9(100358), 1-14.