A Monte Carlo Approach for Evaluating the Impact of RECAT on Runway Capacity

Year 2025, Volume: 1 Issue: 1, 24 - 52, 30.12.2025

Zekeriya Kaplan

Abstract

The constant increase in global air traffic demand continues to put serious pressure on airport runway capacity, particularly at major hubs, where arrival and departure movements occur in rapid succession. A key factor limiting runway throughput is the minimum separation required for consecutive aircraft due to wake turbulence. These conventional separations, which are static and based on broad aircraft weight categories and have often been shown to be overly conservative and operationally restrictive under many traffic and meteorological conditions. Therefore, the RECAT-EU program, developed by EUROCONTROL, has redefined wake turbulence categories and their associated minima. The aim is to establish more flexible and efficient separation schemes that maintain safety while improving capacity. In this study, a Monte Carlo–based simulation framework was developed to evaluate and compare runway capacities under ICAO and RECAT-EU separation standards. Simulations were performed for three aircraft fleets with different traffic compositions, as well as for single-mode and mixed-mode runway operations. The results show that applying RECAT-EU separation minima results in significant capacity improvements of between 14% and 27%, depending on the traffic mix and operational type. These findings confirm that adopting dynamic, optimized separation standards, such as those set out in RECAT-EU, can lead to more efficient runway utilization while maintaining the required safety margins.

Keywords

Air Traffic Management , Monte Carlo Simulation , Runway Capacity , Ultimate Capacity , RECAT

RECAT Uygulanmasının Pist Kapasitesine Etkisinin Monte Carlo Yaklaşımıyla Analizi

Abstract

Küresel hava trafiği talebindeki sürekli artış, özellikle yoğun merkez havalimanlarında, iniş ve kalkışların art arda gerçekleştiği durumlarda pist kapasitesi üzerinde ciddi bir baskı oluşturmaktadır. Pist verimliliğini sınırlayan temel etkenlerden biri, uçaklar arasındaki minimum türbülans ayırmalarıdır. Uçak ağırlık kategorilerine dayanan bu geleneksel ayırma standartları sabittir ve birçok trafik ve meteorolojik koşul altında gereğinden fazla temkinli ve operasyonel olarak kısıtlayıcı oldukları gösterilmiştir. Bu nedenle, EUROCONTROL tarafından geliştirilen RECAT-EU programı, türbülans kategorilerini ve bunlara karşılık gelen ayırma minimalarını yeniden tanımlamıştır. Amaç, emniyeti sürdürürken kapasiteyi artırabilecek daha esnek ve verimli ayırma düzenleri oluşturulmasıdır. Bu çalışmada, ICAO ve RECAT-EU ayırma standartları altında pist kapasitesini değerlendirmek ve karşılaştırmak amacıyla Monte Carlo temelli bir simülasyon modeli geliştirilmiştir. Simülasyonlar, farklı trafik karmalarına sahip üç farklı uçak filosu için, yalnızca iniş veya kalkışın yapıldığı tek mod ve her iki operasyonun bir arada yürütüldüğü karma mod pist senaryolarında gerçekleştirilmiştir. Sonuçlar, RECAT-EU ayırma minimumlarının uygulanmasıyla, trafik bileşimi ve operasyon türüne bağlı olarak %14 ila %27 arasında kapasite artışı sağlandığını göstermektedir. Bu bulgular, RECAT-EU kapsamında tanımlanan dinamik ve optimize edilmiş ayırma standartlarının, emniyet gereksinimlerini koruyarak pist kullanım verimliliğini önemli ölçüde artırabileceğini doğrulamaktadır.

Keywords

Hava Trafik Yönetimi , Monte Carlo Simülasyonu , Pist Kapasitesi , Maksimum Kapasite , RECAT

References

• Amar, J. G. (2006). The Monte Carlo method in science and engineering. Computing in Science and Engineering, 8(2), 9–19. https://doi.org/10.1109/MCSE.2006.34
• Baghal, S. R., & Khodashenas, S. R. (2022). Risk Assessment of Storm Sewers in Urban Areas Using Fuzzy Technique and Monte Carlo Simulation. Journal of Irrigation and Drainage Engineering, 148(8), 04022028. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001696
• Bang Huseby, A., Vanem, E., & Natvig, B. (2013). A new approach to environmental contours for ocean engineering applications based on direct Monte Carlo simulations. Ocean Engineering, 60, 124–135. https://doi.org/10.1016/J.OCEANENG.2012.12.034
• Blom, H. A. P., Stroeve, S. H., & De Jong, H. H. (2006). Safety risk assessment by monte carlo simulation of complex safety critical operations. Developments in Risk-Based Approaches to Safety - Proceedings of the 14th Safety-Critical Systems Symposium, SSS 2006, 48–67. https://doi.org/10.1007/1-84628-447-3_3/COVER
• Capel Lopez, R. P. (2019). Increasing single runway airport capacity without enlarging airports: case study and evaluation of innovative solutions [Master of Science, Politecnico di Milano]. https://www.politesi.polimi.it/handle/10589/150779
• Cetek, F. A., & Aydoğan, E. (2019). Air Traffic Flow Impact Analysis of RECAT for Istanbul New Airport using Discrete-Event Simulation. Düzce University Journal of Science & Technology, 7, 434.
• Chen, K., Graham, D. J., Bansal, P., Anderson, R. J., & Findlay, N. S. (2023). Understanding the capacity of airport runway systems. https://ssrn.com/abstract=4476333
• Chen, N., & Hong, L. J. (2007). Monte Carlo simulation in financial engineering. Proceedings - Winter Simulation Conference, 919–931. https://doi.org/10.1109/WSC.2007.4419688
• Choi, W.-J. (2021). Cost Optimization Modeling for Airport Capacity Expansion Problems in Metropolitan Areas [Embry-Riddle Aeronautical University]. https://commons.erau.edu/edt/588
• Condon, J. H., & Ogielski, A. T. (1998). Fast special purpose computer for Monte Carlo simulations in statistical physics. Review of Scientific Instruments, 56(9), 1691. https://doi.org/10.1063/1.1138125
• De Neufville, R., & Odoni, A. R. (2013). Airport systems : planning design, and management. McGraw-Hill. Decker, K. M. (1991). The Monte Carlo method in science and engineering: Theory and application. Computer Methods in Applied Mechanics and Engineering, 89(1–3), 463–483. https://doi.org/10.1016/0045-7825(91)90054-A
• EASA. (2022). Assignment of Aircraft Types to RECAT-EU Wake Turbulence Categories. https://www.icao.int/publications/DOC8643/Pages/default.aspx
• EUROCONTROL. (2018). European Aviation in 2040 -Challenges of Growth.
• EUROCONTROL. (2022). EUROCONTROL Aviation Outlook 2050.
• EUROCONTROL. (2024a). EUROCONTROL Aviation Long-Term Outlook: Flights and CO2 emissions forecast 2024-2050.
• EUROCONTROL. (2024b). European Wake Turbulence Categorisation and Separation Minima on Approach and Departure “RECAT-EU.” www.eurocontrol.int
• Gerz, T., Holzäpfel, F., & Darracq, D. (2002). Commercial aircraft wake vortices. Progress in Aerospace Sciences, 38(3), 181–208. https://doi.org/10.1016/S0376-0421(02)00004-0
• Giles, M., Kuo, F. Y., Sloan, I. H., & Waterhouse, B. J. (2008). Quasi-Monte Carlo for finance applications. The Proceedings of ANZIAM, 50, C308–C323. https://doi.org/10.21914/ANZIAMJ.V50I0.1440
• Haipeng, G., Jingwei, L., Zhiqiang, W., & Xinze, L. (2020). Research on the Methods of Aircraft Re-categorization Based on China Typical Airport Operation Conditions. Proceedings of 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2020, 61–67. https://doi.org/10.1109/ICCASIT50869.2020.9368548
• Harrison, R. L. (2010). Introduction to Monte Carlo simulation. AIP Conference Proceedings, 1204, 17–21. https://doi.org/10.1063/1.3295638
• Holzäpfel, F., Vechtel, D., Rotshteyn, G., Stephan, A., Holzäpfel, F., Vechtel, D., Rotshteyn, G., & Stephan, A. (2022). Plate lines to enhance wake vortex decay for reduced separations between landing aircraft. Flow, 2, E6. https://doi.org/10.1017/FLO.2021.16
• Horonjeff, R., McKelvey, F., Sproule, W., & Young, S. (2010). Planning and design of airports. McGraw-Hill Companies.
• Hu, J. (2022). Research on Influence of Airport Runway Capacity Based on RECAT. World Scientific Research Journal, 8, 451–460. https://doi.org/10.6911/WSRJ.202206_8(6).0058
• Hu, J., Mirmohammadsadeghi, N., & Trani, A. (2019). Runway occupancy time constraint and runway throughput estimation under reduced arrival wake separation rules. AIAA Aviation 2019 Forum, 1–12. https://doi.org/10.2514/6.2019-3046;CSUBTYPE:STRING:CONFERENCE
• ICAO. (2016). Doc 4444-Air Traffic Management Procedures for Air Navigation Services.
• Irvine, D., Budd, L. C. S., & Pitfield, D. E. (2015). A Monte-Carlo approach to estimating the effects of selected airport capacity options in London. Journal of Air Transport Management, 42, 1–9. https://doi.org/10.1016/j.jairtraman.2014.06.005
• Janic, M. (2014). Modeling effects of different air traffic control operational procedures, separation rules, and service disciplines on runway landing capacity. Journal of Advanced Transportation, 48(6), 556–574. https://doi.org/10.1002/atr.1208
• Janić, Milan. (2000). Air transport system analysis and modelling : capacity, quality of services and economics. Gordon and Breach Science Publishers.
• Janke, W. (2012). Monte Carlo simulations in statistical physics — From basic principles to advanced applications. Order, Disorder and Criticality: Advanced Problems of Phase Transition Theory, 3, 93–166. https://doi.org/10.1142/9789814417891_0003
• Kaplan, Z., & Çetek, C. (2024). A Monte Carlo approach for capacity and delay analyses of multiple interacting airports in Istanbul metroplex. Aeronautical Journal. https://doi.org/10.1017/aer.2024.24
• Kastner, M. (2010). Monte Carlo methods in statistical physics: Mathematical foundations and strategies. Communications in Nonlinear Science and Numerical Simulation, 15(6), 1589–1602. https://doi.org/10.1016/J.CNSNS.2009.06.011
• Koulinas, G. K., Demesouka, O. E., Sidas, K. A., & Koulouriotis, D. E. (2021). A TOPSIS—Risk Matrix and Monte Carlo Expert System for Risk Assessment in Engineering Projects. Sustainability, 13(20), 11277. https://doi.org/10.3390/SU132011277
• Kroese, D. P., Brereton, T., Taimre, T., & Botev, Z. I. (2014). Why the Monte Carlo method is so important today. Wiley Interdisciplinary Reviews: Computational Statistics, 6(6), 386–392. https://doi.org/10.1002/WICS.1314;SUBPAGE:STRING:FULL
• Landau, D. P., Tsai, S.-H., & Exler, M. (2004). A new approach to Monte Carlo simulations in statistical physics: Wang-Landau sampling. American Journal of Physics, 72(10), 1294. https://doi.org/10.1119/1.1707017
• L’Ecuyer, P. (2009). Quasi-Monte Carlo methods with applications in finance. Finance and Stochastics, 13(3), 307–349. https://doi.org/10.1007/S00780-009-0095-Y/METRICS
• Maniriho, E. A. (2019). The characteristics and variables Accounted by Operators in the Planning and Operation of Air-ports. ICAO Scientific Review: Analytics and Management Research, 1, 1, 115–130. http://isr.icao.int/amr/Volume01/v1p115-126Maniriho5113.pdf
• Pan, W., Zhang, H., Yin, H., Wu, T., & Yin, Z. (2021). Study on Capacity Improvement of Close Range Dual Runway of Shuangliu Airport under New Wake Classification Standard (RECAT). IOP Conference Series: Earth and Environmental Science, 693(1). https://doi.org/10.1088/1755-1315/693/1/012018
• Pitfield, D. E., Brooke, A. S., & Jerrard, E. A. (1998). A Monte-Carlo simulation of potentially conflicting ground movements at a new international airport. Journal of Air Transport Management, 4(1), 3–9.
• Pitfield, D. E., & Jerrard, E. A. (1999). Monte Carlo comes to Rome: a note on the estimation of unconstrained runway capacity at Rome Fiumucino International Airport. Journal of Air Transport Management, 5(4), 185–192.
• Rodríguez-Sanz, Á., & Andrada, L. R. (2022). Managing Airport Capacity and Demand: An Economic Approach. IOP Conference Series: Materials Science and Engineering, 1226(1), 012024. https://doi.org/10.1088/1757-899X/1226/1/012024
• Sarrut, D., Etxebeste, A., Muñoz, E., Krah, N., & Létang, J. M. (2021). Artificial Intelligence for Monte Carlo Simulation in Medical Physics. Frontiers in Physics, 9, 738112. https://doi.org/10.3389/FPHY.2021.738112/XML
• Senova, A., Tobisova, A., & Rozenberg, R. (2023). New Approaches to Project Risk Assessment Utilizing the Monte Carlo Method. Sustainability, 15(2), 1006. https://doi.org/10.3390/SU15021006
• Strawderman, R. L. (2001). Monte Carlo Methods in Statistical Physics. Journal of the American Statistical Association, 96, 778–778. https://doi.org/10.1198/jasa.2001.s394
• Stroeve, S. H., Blom, H. A. P., & (Bert) Bakker, G. J. (2009). Systemic accident risk assessment in air traffic by Monte Carlo simulation. Safety Science, 47(2), 238–249. https://doi.org/10.1016/J.SSCI.2008.04.003
• Thompson, S. D. (2002). Terminal Area Separation Standards: Historical Development, and Processes for Change.
• Tomov, T. E. (2019). Wake turbulence separation for Keflavik International Airport: Re-categorisation Requirements (RECAT-EU) [Reykjavík University]. https://skemman.is/handle/1946/34042
• Uddin, G. M., Arafat, S. M., Kazim, A. H., Farhan, M., Niazi, S. G., Hayat, N., Zeid, I., & Kamarthi, S. (2019). Artificial intelligence-based Monte-Carlo numerical simulation of aerodynamics of tire grooves using computational fluid dynamics. AI EDAM, 33(3), 302–316. https://doi.org/10.1017/S0890060419000039
• van Baren, G. B. (2016). Benefits analysis of RECAT-EU for Schiphol Airport. www.nlr.nl
• Van der Meijden, S. A. (2017). Improved Flexible Runway Use Modeling: A Multi-Objective Optimization Concerning Pairwise RECAT-EU Separation Minima, Reduced Noise Annoyance and Fuel Consumption at London Heathrow [Master of Science, Delft University ofTechnology]. http://repository.tudelft.nl/.
• Vechtel, D., Holzäpfel, F., & Rotshteyn, G. (2022). Assessment of Aircraft Separation Reduction Potential for Arrivals Facilitated by Plate Lines. AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022. https://doi.org/10.2514/6.2022-2010;WEBSITE:WEBSITE:AIAA-SITE;WGROUP:STRING:AIAA
• Walter, J. C., & Barkema, G. T. (2015). An introduction to Monte Carlo methods. Physica A: Statistical Mechanics and Its Applications, 418, 78–87. https://doi.org/10.1016/J.PHYSA.2014.06.014
• Zhang, Y., Zhao, Y., Shen, X., & Zhang, J. (2022). A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms. Applied Energy, 305, 117815. https://doi.org/10.1016/J.APENERGY.2021.117815