Airway network control through the application of Braess' paradox
- Authors: Gasparyan G.A.1, Drachenko E.A.1
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Affiliations:
- Moscow state technical university of civil aviation
- Issue: No 4 (2025)
- Pages: 20-43
- Section: Air traffic surveillance and management systems
- URL: https://ogarev-online.ru/2312-1327/article/view/360056
- DOI: https://doi.org/10.51955/2312-1327_2025_4_20
- ID: 360056
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Abstract
The paper explores the application of Braess’s Paradox to the optimization of air traffic networks. Building upon the model proposed earlier, it is confirmed that removing specific edges from the route structure can reduce overall flight time. However, the original static formulation limits its applicability under real-world dynamic traffic conditions. An extended framework incorporating Dynamic Traffic Assignment and robust removal methods that account for demand uncertainty are proposed. Simulation results demonstrate that eliminating certain edges consistently leads to reduced delays, even under fluctuating traffic scenarios. The developed approach offers a practical tool for strategic airspace management, enabling resilient network reconfiguration under dynamic and capacity-constrained environments.
About the authors
G. A. Gasparyan
Moscow state technical university of civil aviation
Author for correspondence.
Email: grigory.rw@gmail.com
ORCID iD: 0009-0007-3917-6256
Moscow, 125493, Russia
E. A. Drachenko
Moscow state technical university of civil aviation
Email: egordrachenko@icloud.com
ORCID iD: 0009-0004-2434-8594
Moscow, 125493, Russia
References
- Bertsimas D., Patterson S.S. (2000). The air traffic flow management problem with enroute capacities. Operations Research.48(1): 156-168.
- Bittihn S., Schadschneider A. (2021). The effect of modern traffic information on Braess’ paradox. Physica A: Statistical Mechanics and its Applications.571: 125829.
- Burov M., Kizilkale C., Kurzhanskiy A., Arcak M. (2021). Detecting Braess Routes: an Algorithm Accounting for Queuing Delays With an Extended Graph. IEEE Intelligent Transportation Systems Conference (ITSC). 2125-2130.
- Cook A. J., Blom H., Lillo F., Mantegna R., Miccichè S., Rivas S., Vázquez R., Zanin M. (2015). Applying complexity science to air traffic management. Journal of Air Transport Management.42: 149-158.
- Cook A., Tanner G., Williams V., Meise G. (2009). Dynamic cost indexing – Managing airline delay costs. Journal of Air Transport Management.15(1): 26-35.
- Eliseev B.P., Vorobyev V.V., Kharlamov A. S. (2016). Influence of air traffic on flight delays. Mir transporta.14 (4):168-175.
- Eurocontrol manual for airspace planning. Common guidelines. Second Edition / European organization for the safety of air navigation. 2003. 432 p.
- ICAO. Air traffic services planning manual, 1st ed. International Civil Aviation Organization. 1984.
- Mahmoud N. A., Al Hindawi B. H., Hasan M. Y. (2021). A Modified Dynamic Programming Approach for 4D Minimum Fuel and Emissions Trajectory Optimization // Aerospace. 8(5). Article 135.
- Manik D., Witthaut D., Timme M. (2022). Predicting Braess' Paradox in Supply and Transport Networks. ArXiv preprint. arXiv:2203.10062.
- Pechenezhsky V.K., Chuvikovskaya E.K.(2023).Features of airspace planning organization in the Russian Federationon the example of the Moscow airspace. Scientific Bulletin of MSTU CA. 26(6): 47–57.
- Rosenberger J.M., Johnson E.L., Nemhauser G.L. (2004). Rerouting aircraft for airline recovery. Transportation Science.38(2): 162-182.
- Tcheukam S. A., Tembine H. (2016). Mean field type games on airline networks and airport queues: Braess paradox, its negation, and crowd effect. Dynamic Games and Applications.11(1): 83-109.
- Veremey E.I., Sotnikova M.V .(2016).Optimal routing based on weather forecast. International Journal of Open Information Technologies. 4(3): 45-53.
- Vickrey W.S. (1969). Congestion theory and transport investment. The American Economic Review.59(2): 251-260.
- Wang A., Tang Y., Mohmand Y.T., Xu P. (2022). Modifying link capacity to avoid Braess Paradox considering elastic demand. Physica A: Statistical Mechanics and its Applications.605: 128002.
- Zhao J., Gao Z., Jia B., Guo X., Sun H. (2014). Dynamic traffic network model and time-dependent Braess’ paradox. Discrete Dynamics in Nature and Society. 1-10: Article ID 802129.
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