The air traffic control (ATC) process is a complex and dynamic system that ensures the safe and efficient operation of aircraft in the airspace. The ATC process involves various actors, such as pilots, controllers, airports, airlines and regulators, who communicate and coordinate their actions through various systems and procedures. The ATC process is influenced by many factors, such as weather, traffic, technical conditions, human factors and others. The ATC process is also subject to changes and uncertainties, such as increasing demand for air travel, technological innovations, environmental regulations and security threats. The main contribution of this article is to provide a comprehensive and integrated approach for modeling the ATC process with an increase in the intensity of flights. The article demonstrates the applicability and usefulness of the proposed models for supporting decision-making and policy making in the field of air traffic management. The article also identifies the challenges and limitations of the current ATC process and suggests directions for future research and development.
The air traffic control (ATC) process is a complex and dynamic system that ensures the safe and efficient operation of aircraft in the airspace. The ATC process involves various actors, such as pilots, controllers, airports, airlines and regulators, who communicate and coordinate their actions through various systems and procedures. The ATC process is influenced by many factors, such as weather, traffic, technical conditions, human factors and others. The ATC process is also subject to changes and uncertainties, such as increasing demand for air travel, technological innovations, environmental regulations and security threats. The main contribution of this article is to provide a comprehensive and integrated approach for modeling the ATC process with an increase in the intensity of flights. The article demonstrates the applicability and usefulness of the proposed models for supporting decision-making and policy making in the field of air traffic management. The article also identifies the challenges and limitations of the current ATC process and suggests directions for future research and development.
| № | Author name | position | Name of organisation |
|---|---|---|---|
| 1 | Shukurova S.M. | texnika fanlari bо‘yicha falsafa doktori (PhD), dotsent | Toshkent davlat transport universiteti |
| 2 | Rustamov N.S. | doktorant | Toshkent davlat transport universiteti |
| № | Name of reference |
|---|---|
| 1 | 1. Zanin M., Lillo F., Patelli A. Structure and properties of the European Air Transport Network: a complex network analysis. // Journal of Transport Systems. 2013. - Vol. 17, No. 4. - pp. 180- 196. |
| 2 | 2. A.A. Akhmedov. Modeling of the air traffic control process with an increase in the intensity of flights. - Tashkent: Tashkent State Technical University, 2023. - 180 p. |
| 3 | 3. Wang Z., Zhang Y., Du G. Analysis of the resilience of Chinese air traffic management network using complex network theory. // Journal of Physics A: Mathematical and Theoretical. 2016. - Vol. 49, No. 22. - Article 225101. |
| 4 | 4. Zhang R., Li M., Wang H. Multi-objective optimization of US air traffic management network based on complex network theory. // Expert Systems with Applications. 2019. - vol. 115. - pp. 462-474 |
| 5 | 5. Rytter A., Skorupski J. The concept of initial air traffic situation assessment as a stage of medium-term conflict detection. Procedia Eng. 2017, 187, pp. 420–424. |
| 6 | 6. Gomes H.M., Barddal J.P., Enembreck F., Bifet A. A survey on ensemble learning for data stream classification. ACM Comput. Surv. 2017, 50, pp. 1–36 |
| 7 | 7. Mehmood Z., Asghar S. Customizing SVM as a base learner with adaboost ensemble to learn from multi-class problems: A hybrid approach adaboost-MSVM. Knowl. Based Syst. 2021, 217, 106845. |
| 8 | 8. Rodríguez-Sanz Á., Andrada L.R. Managing airport capacity and demand: An economic approach. IOP Conf. Ser. Mater. Sci. Eng. 2022, 1226, 012024 |
| 9 | 9. Angélica Sousa da Mata. Complex Networks: a Mini-review. Brazilian Journal of Physics, Vol. 50, 2020, pp. 658-672. DOI: 10.1007/s13538-020-00772-9 |
| 10 | 10. Zhiyong Sun, Xiangyu Meng, Brian D.O. Coordination and Control of Complex Network Systems With Switching Topologies: A Survey. IEEE Transactions on Systems Man and Cybernetics Systems. 2020, pp. 1-16. DOI: 10.1109/TSMC.2019.2961753 |
| 11 | 11. Lun Li. Topologies of Complex Networks: Functions and Structures. California Institute of Technology. 2005, doi: 10.7907/9G3P-7F13. |
| 12 | 12. Ranjan, E., Sanyal S., Talukdar P. ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations. Proc. AAAI Conf. Artif. Intell. 2020, 34, 5470–5477. |
| 13 | 13. Hamilton W., Ying R., Leskovec J. Inductive Representation Learning on Large Graphs. archive 2017, archive:1706.02216 |