Identyfikatory
Warianty tytułu
Artificial intelligence and the safety of airport operations management
Języki publikacji
Abstrakty
Nowoczesne lotniska to takie, które korzystają z inteligentnych systemów w celu zwiększenia wydajności operacyjnej, personelu, optymalizacji przepływów pasażerów, poprawy zrównoważonego rozwoju, a także zwiększenia bezpieczeństwa lotnisk. Celem artykułu jest zweryfikowanie aktualnego stanu wiedzy na temat wdrożenia systemów inteligentnego zarządzania operacjami lotniczymi w portach lotniczych. Jednym z istotniejszych aspektów poruszonych w artykule jest etyczne podejście do AI co ma bardzo ważne znaczenie w zakresie budowania zaufania człowieka do rozwoju cyfrowego. Żeby zrozumieć złożoność procesu zarządzania operacjami lotniczymi analizie zostaną poddane istotne dokumenty normatywne a także dostępna literatura w tym obszarze.
Modern airports are those that use intelligent systems to increase operational efficiency, personnel, optimize passenger flows, improve sustainability, and enhance airport security. The purpose of the article is to verify the current state of knowledge on the implementation of intelligent systems for airport operations management. One of the most important aspects addressed in the article is the ethical approach to AI which is very important in terms of building human trust in digital development. To understand the complexity of the process of aviation operations management, relevant normative documents will be analyzed, as well as the available literature in this area.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
777--794
Opis fizyczny
Bibliogr. 36 poz., tab.
Twórcy
autor
- Polish Air Force University, Dęblin, Poland
Bibliografia
- 1. Campesato O. (2020). Artificial Intelligence, Machine Learning, and Deep Learning. Dulles, VA, USA: Mercury Learning & Information, https://books.google.com.au/books?id=.
- 2. Chutiphongdech. T., Vongsaroj. Rugphong. V. (2019), The Success behind the World’s Best Airport: The Rise of the Changi, SSRN Electronic Journal, https://www.internationalairportreview.com/news/172112/jfkiat-launches-new-apronai-turnaround-control-solution-at-jfk-airport/Doc_4444.
- 3. Donadio F., Frejaville J., Larnier S. (2018). Show All, Artificial Intelligence and Collaborative Robot to Improve Airport Operations, Online Engineering & Internet of Things, Volume 22, ISBN : 978-3-319-64351-9.
- 4. Dožić, S. (2019). Multicriteria decision making methods: Application in the Easa Ai Roadmap 2.0. 2023.
- 5. EASA, https://www.easa.europa.eu.
- 6. Efthymiou, M., McCarthy, K., Markou, C., & O’Connell, J. F. Emha Abdillah R.; Moenaf H.; Fadullah Rasyid L.; Achmad S., Sutoyo R. (2022). An Im-plementation of Artificial Intelligence on Air Traffic Control - A Systematic Literature Review, 2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM), DOI: 10.1109/IMCOM60618.2024.10418350.
- 7. European Aviation Environmental Report 2019.
- 8. European Union Aviation Safety Agency (2020) Artificial Intelligence Roadmap. Exploratory research on blockchain in aviation: the case of maintenance, repair and overhaul (MRO) organizations. Sustainability, 14(5), 2643. Gaps in Artificial Intelligence Innovation, p.79-87.
- 9. Guo X, Grushka-CockayneY., Bert De Reyck (2020), London Heathrow Airport Uses Real-Time Analytics for Improving Operations, DOI:10.1287/inte.2020.1044.
- 10. Horkay. J., Tymofii. V., Al-Rabeei. S. (2022), Using Biometrics for Facial Recognition at Airports, Acta Avionica Journal, DOI:10.35116/aa.2022.0029.
- 10. Komisja Europejska. Inteligentna i godna zaufania automatyzacja europejskiego lotnictwa. https://cordis.europa.eu/article/id/442211-bringing-intelligent-and-trustworthy-automation-to-europe-s-aviation-sector/pl.
- 11. EASA, DATA4SAFETY. (2024). 2024 – An existing year ahead of Da-ta4Safety. https://www.easa.europa.eu/en/domains/safety-management/data4safety#group-easa-downloads.
- 12. Patrik KY. (2020). Artificial Intelligence Roadmap. A human-centric approach to AI in aviation. EASA Publishing. https://www.easa.europa.eu/sites/default/files/dfu/EASA-AI-Roadmap-v1.0.pdf.
- 13. Shiphol, Netherlands. (bd). Using AI to improve turnarounds and colaboration. https://www.schiphol.nl/en/aviation-solutions/case-study-amsterdam-airport-schiphol-deep-turnaround/.
- 15. Abdillah R.E., Moenaf H., Rasyid R.F., Achmad S., Sutoyo R. (2024). Implementation of Artificial Intelligence on Air Traffic Control - A Systematic Literature Review, 18th International Conference on Ubiquitous Information Management and Communication (IMCOM), DOI:10.1109/IMCOM60618.2024.10418350.
- 16. Informacje prasowe PLL LOT, 2024 r.
- 17. Jiang, Hieu Tran, Williams, (2023) Machine learning and mixed reality for smart aviation: Applications and challenges, Journal of Air Transport Management, Elsevier.
- 18. Koenig, F., Found, P.A., Kumar, M. and Rich, N. (2021), Conditionbased maintenance for major airport baggage systems, Journal of Manufacturing Technology Management, Vol. 32 No. 3, pp. 722-741. https://doi.org/10.1108/JMTM-04-2019-0144.
- 19. Kongres Rynku Lotniczego, Polska, Warszawa, 2024 r.
- 20. Lim Ji, Jeong-Eun Seo and Hun-Yeong Kwon, (2022). The Role of Higher Education for the Ethical AI Society, p.2-4., https://doi.org/10.32473/flairs.v35i.130609.
- 21. MDPI, https://www.mdpi.com.
- 22. Merlo T.R., (2024), Emerging Role of Artificial Intelligence (AI) in Aviation: Using Predictive Maintenance for Operational Efficiency, Harnessing Digital Innovation for Air Transportation, DOI: 10.4018/979-8-3693-0732-8.ch002.
- 23. NATCA. (2024). AI’s role in aviation safety: insights from FAA expert at CFS 2024. https://www.natca.org/2024/09/19/ais-role-in-aviationsafety-insights-from-faa-expert-at-cfs-2024/.
- 24. Nersessian D., Mancha R., (2021), From Automation to Autonomy: Legal and Ethical Responsibility onomy: Legal and Ethical Responsibility New Report from AIA, Accenture Details Strategic Plan for the Future of Sustainable Aviation, 2022.
- 25. Nguyen B, Sonnenfeld N., Jentsch F. (2023), Using AI Tools to Develop Training Materials for Aviation: Ethical, Technical, and Practical Concerns, Human Factors and Ergonomics Society, https://doi.org/10.1177/21695067231192904.
- 26. Olaganathan R., Miller M., Mrusek B.M. (2020), Managing Safety Risks in Airline Maintenance Outsourcing, Journal of Embry-Riddle Aeronautical University - Worldwide College of Aviation, art 7.
- 27. Pinska-Chauvin E., Helmke E.,DokicJ. , Hartikainen P.,Ohneiser O., Lashe-ras R.G. (2023). Ensuring Safety for Artificial-Intelligence-Based Automatic Speech Recognition in Air Traffic Control Environment, Aerospace 2023, 10(11), 941; https://doi.org/10.3390/aerospace10110941pqnNDwAAQBAJ.
- 28. Shmelova T., Sikirda Y, Kasatkin M. (2019). Applied artificial intelligence for air navigation sociotechnical system development, ICTERI, pp. 454-459.
- 29. Siau K., Wang W., (2020). Artificial Intelligence (AI) Ethics: Ethics of AI and Ethical AI, Journal of Database Management (JDM)31(2), DOI:10.4018/JDM.2020040105.
- 30. Sharma, R., Agarwal, P., & Arya, A. (2022). Natural language processing and big data: a strapping combination. In New Trends and Applications in Internet of Things (IoT) and Big Data Analytics (pp. 255-271). Cham: Springer International Publishing.
- 31. Tatineni S., Mustyala A.(2024), Leveraging AI for Predictive Upkeep: Optimizing Operational Efficiency, ESP International Journal of Advancements in Computational Technology [ESP-IJACT], doi:10.56472/25838628/IJACT-V2I1P110.
- 32. Weryfikacja_i_walidacja_nowego_algorytmu_planowani.pdf
- 33. Yan H. Zuo H., Tang J., Wang R. Ma X. (2020). Predictive maintenance framework of the aircraft system based on PHM information, Asia Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM), Vancouver, BC, Canada, 2020, pp. 1-6, doi:10.1109/APARM49247.2020.9209454.
- 34. Yasuda J.D.V. ,Cappabianco F.AM. , Eduardo G, Martins L., Gripp J.A.B., (2022), Aircraft visual inspection: A systematic literature review, Computers in Industry, Volume 141.
- 35. Yiğitol B. (2024). AI, Robotics, and Autonomous Systems [w:] Smart and Sustainable Operations Management in the Aviation Industry A Supply Chain 4.0 Perspective, T&F. https://doi.org/10.1201/978100338918.
- 36. Z. Liu, E. Blasch, M. Liao, Ch. Yang, K. Tsukada, N. Meyendorf, (2023), Digital twin for predictive maintenance, Proceedings Volume 12489, NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE; 1248907 (2023) https://doi.org/10.1117/12.2660270.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1df98409-895d-4287-851b-cf3049fbb16b
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