• Shukurova Sabohat Tashkent State Transport University
  • Rustamov Nozimzhon Tashkent State Transport University


Air traffic, air traffic control, aircraft, flow, runway, taxiway, efficiency, network, topology, globalization, coordination


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.


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-

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.

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.

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

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.

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.

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,

, 106845.

Rodríguez-Sanz Á., Andrada L.R. Managing airport capacity and demand: An economic

approach. IOP Conf. Ser. Mater. Sci. Eng. 2022, 1226, 012024

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

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

Lun Li. Topologies of Complex Networks: Functions and Structures. California Institute of

Technology. 2005, doi: 10.7907/9G3P-7F13.

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.

Hamilton W., Ying R., Leskovec J. Inductive Representation Learning on Large Graphs.

archive 2017, archive:1706.02216




How to Cite

Shukurova, S., & Rustamov, N. (2024). ANALYSIS OF THE ISSUES OF MODELING THE AIR TRAFFIC CONTROL PROCESS WITH INCREASING FLIGHT INTENSITY. Innovatsion Texnologiyalar , 52(04). Retrieved from