EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 680 (2025) E3S Web Conf., 680 (2025) 00098 References
Open Access
Issue
E3S Web Conf.
Volume 680, 2025
The 4th International Conference on Energy and Green Computing (ICEGC’2025)
Article Number 00098
Number of page(s) 16
DOI https://doi.org/10.1051/e3sconf/202568000098
Published online 19 December 2025
  1. S. Degaugue, Vers un outil adaptatif d’aide à la résolution de conflits pour le contrôle aé rien, Ph.D. thesis, École Nationale de l’Aviation Civile (ENAC), Toulouse, France (2024). Available: https://theses.fr/s354602 [Google Scholar]
  2. Y. Guleria, Enhancing Air Traffic Conflict Resolution through Machine Learning, Conformal Automation, and Flow-Centric Paradigms, Ph.D. thesis, Nanyang Technological University, Singapore (2024). Available: https://dr.ntu.edu.sg/handle/10356/177541 [Google Scholar]
  3. Y. Pang, Artificial Intelligence-Enhanced Predictive Modeling in Air Traffic Management, Ph.D. thesis, Arizona State University, USA (2023). Available: https://www.researchgate.net/publication/376554422 [Google Scholar]
  4. K. Kim, Data-Driven Decision Supporting Tools for Aircraft Conflict Resolution and Conformance Monitoring, Ph.D. thesis, Purdue University, USA (2021). Available: https://hammer.purdue.edu/articles/thesis/14831580 [Google Scholar]
  5. Y. Guleria, Y. Liu, V.N. Duong, Towards conformal automation in air traffic control: learning conflict resolution strategies through behavior cloning. Inf. Fusion 93, 74–88 (2024). [Google Scholar]
  6. D. Sui, C. Ma, C. Wei, Tactical conflict solver assisting air traffic controllers using deep reinforcement learning. Aerospace 10(2) (2023). https://doi.org/10.3390/aerospace10020182 [Google Scholar]
  7. Q. Xu, X. Wang, J. Liu, Z. Chen, An efficient aircraft conflict detection and resolution method based on an improved reinforcement learning framework. Int. J. Aerosp. Eng. (2023). https://doi.org/10.1155/2023/6643903 [Google Scholar]
  8. A. Bastas, G.A. Vouros, Data-driven prediction of air traffic controllers’ reactions to resolving conflicts. Inf. Sci. 626, 218–236 (2022). https://doi.org/10.1016/j.ins.2022.09.015 [Google Scholar]
  9. X. Huang, J. Li, H. Zhou, L. Sun, Autonomous air traffic separation assurance through machine learning. J. Ind. Manag. Optim. 20(10), 3195–3204 (2024). [Google Scholar]
  10. R.E. Abdillah, H. Lee, Y. Han, K. Kim, Implementation of artificial intelligence on air traffic control – a systematic literature review. In: Proc. 2024 Int. Conf. on Information and Communication Technology Convergence (IMCOM) (2024). https://doi.org/10.1109/IMCOM60618.2024.10418350 [Google Scholar]
  11. X. Gariel, A. Chen, T. Weiss, Framework for certification of AI-based systems. arXiv preprint (2023). Available: https://arxiv.org/abs/2302.00000 [Google Scholar]
  12. A. Bikir, O. Idrissi, K. Mansouri, M. Qbadou, Hybrid approach for minimizing departure air traffic delays following standard instrument departures. Stat. Optim. Inf. Comput. 13(1) (2024). https://doi.org/10.19139/soic-2310-5070-1861 [Google Scholar]
  13. O. Idrissi, A. Bikir, K. Mansouri, Efficient management of aircraft taxiing phase by adjusting speed through conflict-free routes. Stat. Optim. Inf. Comput. 10(1), 12–24 (2022). [Google Scholar]
  14. S. Chougdali, K. Mansouri, M. Youssfi, Air traffic management system using intelligent computing. ARPN J. Eng. Appl. Sci. 14(2), 518–524 (2019). [Google Scholar]
  15. ICAO, Procedures for Air Navigation Services (PANS) – Air Traffic Management (Doc 4444) (International Civil Aviation Organization, Montréal, 2022). Available: https://store.icao.int/en/procedures-for-air-navigation-services-air-trafficmanagement-doc-4444 [Google Scholar]
  16. Y. Xie, L. Zhao, M. Zheng, T. Hu, Explanation of machine-learning solutions in air-traf fic management. Aerospace 8(8) (2021). https://doi.org/10.3390/aerospace8080224 [Google Scholar]
  17. A. Bastas, G. Vouros, N. Karkaletsis, Automating the resolution of flight conflicts: deep reinforcement learning in service of air traffic controllers. arXiv preprint (2022). Available: https://arxiv.org/abs/2206.00000 [Google Scholar]
  18. M. IJtsma, D. van Gent, P. Groen, Evaluation of a decision-based invocation strategy for adaptive support for air traffic control. J. Air Traffic Manag. (2022). [Google Scholar]
  19. C. Westin, T. Eriksson, A. Lundberg, J. Nilsson, Personalized and transparent AI support for ATC conflict detection and resolution: an empirical study. In: Proc. 12th SESAR Innovation Days (SIDs) (2022). https://doi.org/10.61009/SID.2022.1.15 [Google Scholar]
  20. J. Laskowski, M. Mazurek, P. Nowak, M. Wozniak, AI-based method of air traffic controller workload assessment. In: Proc. IEEE MetroAeroSpace 2024 (2024). https://doi.org/10.1109/MetroAeroSpace61015.2024.10591524 [Google Scholar]
  21. S.J. van Rooijen, A. de Haan, P. Groen, Toward individual-sensitive automation for air traffic control using convolutional neural networks. J. Air Transp. 28(3), 105–113 (2020). https://doi.org/10.2514/1.D0180 [Google Scholar]
  22. P.N. Tran, C.E. Schmidt, A.R. Baxter, An interactive conflict solver for learning air traf fic conflict resolutions. J. Aerosp. Inf. Syst. 17(6), 271–277 (2020). https://doi.org/10.2514/1.I010807 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.