Airport complexity and environmental efficiency metrics for air traffic management evaluation
DOI: https://doi.org/10.3846/aviation.2025.24893Abstract
The aviation industry is experiencing significant growth due to the growing global demand for air travel. The International Civil Aviation Organization predicts that air passenger volumes will quadruple by 2040, putting pressure on airport infrastructure and airspace capacity. This growth is causing environmental challenges, particularly concerning emissions from aircraft operations and airport activities. These emissions contribute to local air pollution and global climate change. Airports are complex operational hubs, requiring sophisticated planning and efficient operations management to mitigate emissions and maximize throughput. This thesis investigates how airport complexity and air traffic management strategies influence inefficiencies in fuel use, time, cost, and environmental impact. Traffic scenarios were generated and analysed using MATLAB code, calculating emissions and fuel consumption across all phases of the landing and take-off (LTO) cycle. The results show significant differences in operational efficiency and environmental impact, offering insights into the effectiveness of modern traffic control methods.
Keywords:
emissions, airport complexity, inefficiency, air traffic management, fuel consumption, CO2 emissions, flight phasesHow to Cite
Share
License
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Bagdi, Z., Csámer, L., & Bakó, G. (2023). The green light for air transport: Sustainable aviation at present. Cognitive Sustainability, 2(2). https://doi.org/10.55343/CogSust.55
Burkhardt, U., & Kärcher, B. (2011). Global radiative forcing from contrail cirrus. Nature Climate Change, 1(1), 54–58. https://doi.org/10.1038/nclimate1068
Cereijo, I. M. (2024). Exploring strategies for streamlining security procedures without compromising safety: Airport planning and logistical considerations. Cognitive Sustainability, 3(2). https://doi.org/10.55343/CogSust.107
Dalmau, R., & Attia, J. (2025). A collection of machine learning models for improved airport operations amidst adverse weather conditions. European Journal of Transport and Infrastructure Research, 25(1), 133–159. https://doi.org/10.59490/ejtir.2025.25.1.7487
Delahaye, D., Puechmorel, S., Mongeau, M., & Schoenauer, M. (2014). Air traffic complexity map based on nonlinear dynamical systems. Air Traffic Control Quarterly, 22(4), 329–356.
EASA Eco. (n.d.). Climate change. https://www.easa.europa.eu/en/domains/environment/eaer
Eleimat, M., & Őszi, A. (2025). Cybersecurity in aviation: Exploring the significance, applications, and challenges of cybersecurity in the aviation sector. Periodica Polytechnica Transportation Engineering, 53(2), 169–183. https://doi.org/10.3311/PPtr.37153
European Commission. (2022). Reducing emissions from aviation. https://climate.ec.europa.eu/eu-action/transport/reducing-emissions-aviation_en
EUROCONTROL. (2021). XMAN implementation progress report. https://www.eurocontrol.int
EUROCONTROL. (2022, December). Advanced emissions model flight emissions assessments. In EUROCONTROL Innovation Hub Brétigny-sur-Orge (pp. 8–9), France. https://www.eurocontrol.int/sites/default/files/2022-12/ectl_env_impact_assessment_forum_-_aem.pdf
Federal Aviation Administration. (2022). Time-based flow management (TBFM) overview. https://www.faa.gov
Hilburn, B. (2004). Cognitive complexity in air traffic control: A literature review. Eurocontrol Experimental Centre. https://www.eurocontrol.int/sites/default/files/library/007_Cognitive_Complexity_in_ATC.pdf
International Civil Aviation Organization. (2025). ICAO Aircraft engine emissions databank. https://www.easa.europa.eu/en/domains/environment/icao-aircraft-engine-emissions-databank
International Civil Aviation Organization. (2015). Manual on advanced surface movement guidance and control systems (A-SMGCS) (Doc 9830). ICAO.
Jung, Y. C., Gupta, G., & Hoang, T. (2016). Surface operations optimization with the Spot and Runway Departure Advisor (SARDA). In AIAA Aviation Forum. Ames Research Center. https://ntrs.nasa.gov/citations/20160004987
Laudeman, I. V., Shelden, S. G., Branstrom, R., & Brasil, C. L. (1998). Dynamic density: An air traffic management metric. In NASA Technical Report TM-1998 112226. NASA. https://ntrs.nasa.gov/citations/19980210764
Lee, D. S., Fahey, D. W., Skowron, A., Allen, M. R., Burkdardt, U., Chen, Q., Doherty, S. J., Freeman, S., Forster, P. M., Fuglestvedt, J., Gettelman, A., De León, R. R., Lim, L. L., Lund, M. T., Millar, R. J., Owen, B., Penner, J. E., Pitari, G., Prather, M. J., Sausen, R., ...& Wilcox, L. J. (2021). The contribution of global aviation to climate change for 2000 to 2018. Atmospheric Environment, 244, Article 117834. https://doi.org/10.1016/j.atmosenv.2020.117834
Lee, D., Pitari, G., Grewe, V., Gierens, K., Penner, J., Petzold, A., Prather, M., Schumann, U., Bais, A., Berntsen, T., Iachetti, D., Lim, L. L., & Sausen, R. (2009). Transport impacts on atmosphere and climate: Aviation. Atmospheric Environment, 44(37), 4678–4734. https://doi.org/10.1016/j.atmosenv.2009.06.005
Moreno, F. P., Comendador, V. F. G., Jurado, R. D., Suárez, M. Z., Antulov-Fantulin, B., & Valdés, R. M. A. (2024a). How has the concept of air traffic complexity evolved? Review and analysis of the state of the art of air traffic complexity. Applied Sciences, 14(9), Article 3604. https://doi.org/10.3390/app14093604
Memarzadeh, M., Puranik, T. G., Kalyanam, K. M., & Ryan, W. (2023). Airport runway configuration management with offline model-free reinforcement learning. In AIAA SCITECH 2022 Forum (AIAA 2023-0504, Session: Learning, Reasoning, and Data Driven Systems II). ARC. https://doi.org/10.2514/6.2023-0504
Moreno, F. P., Rodríguez, F. I., Comendador, V. F. G., Jurado, R. D., Suárez, M. Z., & Valdés, R. M. A. (2024b). Prediction of air traffic complexity through a dynamic complexity indicator and machine learning models. Journal of Air Transport Management, 119, Article 102632. https://doi.org/10.1016/j.jairtraman.2024.102632
Netjasov, F., & Babić, O. (2020). Kontrola letenja [Air traffic control]. Univerzitet u Beogradu.
Nguyen, A.-D., Pham, D.-T., Lilith, N., & Alam, S. (2022). Model generalization in arrival runway occupancy time prediction by feature equivalences. arXiv. https://arxiv.org/abs/2201.11654
Olive, X., Waltert, M., & Schultz, M. (2025). Assessing airport surface traffic performance from open sources of aviation data. In The 1st USA–Europe Air Transportation R&D Symposium. ResearchGate. https://www.researchgate.net/publication/391150085_Assessing_Airport_Surface_Traffic_Performance_from_Open_Sources_of_Aviation_Data
Our World in Data. (2020). Global aviation emissions. https://ourworldindata.org/global-aviation-emissions
Overton, J. (2019). Fact sheet: The growth in greenhouse gas emissions from commercial aviation. EESI. https://www.eesi.org/papers/view/fact-sheet-the-growth-in-greenhouse-gas-emissions-from-commercial-aviation
Poškuvienė, L., Čižiūnienė, K., & Matijošius, J. (2022). Analysis of customer service quality models and for their approbation opportunities in aviation. Periodica Polytechnica Transportation Engineering, 50(3), 285–292. https://doi.org/10.3311/PPtr.15213
Reynolds, T. G. (2009). Development of flight inefficiency metrics for environmental performance assessment of ATM. In Eighth USA/Europe Air Traffic Management Research and Development Seminar (ATM2009) (pp. 1–10). http://www.atslab.org/wp-content/uploads/2017/01/ATM2009_Reynoldsv2.pdf
Simić, T. K., & Babić, O. (2015). Airport traffic complexity and environment efficiency metrics for evaluation of ATM measures. Journal of Air Transport Management, 42, 260–271. https://doi.org/10.1016/j.jairtraman.2014.11.008
Transport & Environment. (2021). Airplane pollution. https://www.transportenvironment.org/topics/planes/airplane-pollution
Wang, H., Huang, J., Deng, T., & Song, Z. (2023). Evaluation and optimization of air traffic complexity based on resilience metrics. Journal of Advanced Transportation, 2023, 1–16. https://doi.org/10.1155/2023/5692934
Wasiuk, D. K., Khan, A. H., Shallcross, D. E., & Lowenberg, M. H. (2016). A commercial aircraft fuel burn and emissions inventory for 2005–2011. Atmosphere, 7(6), Article 78. https://doi.org/10.3390/atmos7060078
Wei, M., Yang, S., Wu, W., & Sun, B. (2024). A multi-objective fuzzy optimization model for multi-type aircraft flight scheduling problem. Transport, 39(4), 313–322. https://doi.org/10.3846/transport.2024.20536
Yin, J., Zhang, M., Ma, Y., Wu, W., Li, H., & Chen, P. (2024). Prediction and analysis of airport surface taxi time: Classification, features, and methodology. Applied Sciences, 14(3), Article 1306. https://doi.org/10.3390/app14031306
View article in other formats
CrossMark check
Published
Issue
Section
Copyright
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.