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Year 2025, Volume: 26 Issue: 3, 305 - 316, 25.09.2025
https://doi.org/10.18038/estubtda.1713749

Abstract

References

  • [1] Bellasio R. Analysis of wind data for airport runway design. J. Airl. Airport Manag. 2014; 4(2): 1–15.
  • [2] Maslovara A, Mirkovic B. Impact of tailwind on airport capacity and delay at Zurich Airport. Transp. Res. Procedia 2021; 59: 117–126.
  • [3] Hernández-Romero E, Valenzuela A, Rivas D. Probabilistic multi-aircraft conflict detection and resolution considering wind forecast uncertainty. Aerosp. Sci. Technol. 2020; 105: 105973.
  • [4] Dönmez K, Cetek C, Kaya O. Air traffic management in parallel-point merge systems under wind uncertainties. J. Air Transp. Manag. 2022; 104: 102268.
  • [5] Gu Y, Rhudy MB. Stochastic wind modeling and estimation for unmanned aircraft systems. In: AIAA Aviation Forum; 2019.
  • [6] Rodionova O, Sridhar B, Ng HK. Conflict resolution for wind-optimal aircraft trajectories in North Atlantic Oceanic Airspace with wind uncertainties. In: IEEE/AIAA Digital Avionics Systems Conference; 2016. pp. 1–10.
  • [7] Vela AE, Salaun E, Solak S, Feron E. A two-stage stochastic optimization model for air traffic conflict resolution under wind uncertainty. In: IEEE/AIAA Digital Avionics Systems Conference; 2009. pp. 1–10.
  • [8] Saeed A, Li C, Gan Z, Xie Y, Liu F. A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution. Energy 2022; 238: 122012.
  • [9] Chen G, Tang B, Zeng X, Zhou P, Kang P, Long H. et al. Short-term wind speed forecasting based on long short-term memory and improved BP neural network. Int. J. Electr. Power Energy Syst. 2022; 134: 107365.
  • [10] Wang Y, Zhang N, Wu L, Wang Y. Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method. Renew. Energy 2016; 94: 629–636.
  • [11] Zhang Y, Chen B, Pan G, Zhao Y. A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting. Energy Convers. Manag. 2019; 195: 180–197.
  • [12] Valdivia-Bautista SM, Domínguez-Navarro J. A, Pérez-Cisneros M, Vega-Gómez CJ, Castillo-Téllez B. Artificial intelligence in wind speed forecasting: A review. Energies 2023; 16(5): 2457.
  • [13] Dong X, Li C, Shi H, Zhou P. Short-term probabilistic wind speed predictions integrating multivariate linear regression and generative adversarial network methods. Atmosphere 2024; 15(3): 294.
  • [14] Chang SW. Crosswind-based optimization of multiple runway orientations. J. Adv. Transp. 2015; 49(1): 1–9.
  • [15] Barea A, Celis R, Cadarso L. An integrated model for airport runway assignment and aircraft trajectory optimisation. Transp. Res. C Emerg. Technol. 2024; 160: 104498.
  • [16] Kalyanam KM, Memarzadeh M, Crissman J, Yang, R, Tejasen KT Applying machine learning tools for runway configuration decision support. In: International Conference on Research in Air Transportation; 2024.
  • [17] Herrema F, Curran R, Hartjes S, Ellejmi M, Bancroft S, Schultz M. A machine learning model to predict runway exit at Vienna airport. Transp. Res. E Logist. Transp. Rev. 2019; 131: 329–342.
  • [18] Oktal H, Yildirim N. New model for the optimization of runway orientation. J. Transp. Eng. 2014; 140(3): 04013020.
  • [19] Oktal H, Yıldırım N. Optimisation of runway orientations for three-runway configurations. Aeronaut. J. 2016; 120(1233): 1693–1709.
  • [20] Mousa RM, Mumayiz SA. Optimization of runway orientation. J. Transp. Eng. 2000; 126(3): 228–236.
  • [21] Ahmed MS, Alam S, Barlow M. A cooperative co-evolutionary optimisation model for best-fit aircraft sequence and feasible runway configuration in a multi-runway airport. Aerospace 2018; 5(3): 85.
  • [22] Provan CA, Atkins SC. Optimization models for strategic runway configuration management under weather uncertainty. In: AIAA Aviation Technology, Integration and Operations Conference; 2010.
  • [23] Li L, Clarke JP, Chien HHC, Melconian T. A probabilistic decision-making model for runway configuration planning under stochastic wind conditions. In: IEEE/AIAA Digital Avionics Systems Conference; 2009. pp. 3–A.
  • [24] Singh M, Chopra T. Use of computer applications for determining the best possible runway orientation using wind rose diagrams. In: International Conference on Recent Trends in Transportation, Environmental and Civil Engineering; 2012. pp. 1–6.
  • [25] Oktavia S, Syafriani D, Dwiridal L, Sudiar NY. Analysis of surface wind speed at Minangkabau International Airport for the period 2011–2020 using the windrose method. J. Phys. Conf. Ser. 2023; 2582(1): 012006.
  • [26] Han S, Park B, Lee H. Analysis of the impacts of wind on final approach overshoot using historical flight and weather data. In: AIAA SciTech Forum; 2024. pp. 1–10.
  • [27] Tatli A, Suzer AE, Filik T, Karakoc TH. A case study on investigating probabilistic characteristics of wind speed data for green airport. In: Solutions for Maintenance Repair and Overhaul, ISATECH 2021; Springer, Cham; 2024. pp. 1–15.
  • [28] Sardjono W, Kusnoputranto H, Soesilo TEB, Utama DN, Sudirwan J. Study of runway crosswind and tailwind potential for airport sustainability: A study of Soekarno Hatta airport, Cengkareng, Indonesia. In: IOP Conf. Ser. Earth Environ. Sci. 2021; 729(1): 012012.
  • [29] Maslovara A, Mirković B. Impact of tailwind on airport capacity and delay at Zurich Airport. Transp. Res. Procedia 2021; 59: 117–126.
  • [30] O'Connor A, Kearney D. Evaluating the effect of turbulence on aircraft during landing and take-off phases. Int. J. Aviat. Aeronaut. Aerosp. 2018; 5(4): 10.
  • [31] Le Y, Li Y, Zhang Y. Impact of runway surface wind on afternoon and night flight operation at Linzhi Airport. Ind. Eng. Innov. Manag. 2022; 56: 48–58.
  • [32] Prema V, Rao KU. Time series decomposition model for accurate wind speed forecast. Renew Wind Water Sol 2015; 2: 18.
  • [33] Schlink U, Tetzlaff G. Wind speed forecasting from 1 to 30 minutes. Theor Appl Climatol 1998; 60: 191–198.
  • [34] Dhal R, Roy S, Tien SL, Taylor C, Wanke C. Operationally structured model for strategic runway configuration predictions. J Air Transp 2019; 27(2): 96–108.
  • [35] Wang Y, Zhang Y. Prediction of runway configurations and airport acceptance rates for multi-airport system using gridded weather forecast. Transp Res C Emerg Technol 2021; 125: 103049.
  • [36] Lau MEC, Lam AJG, Alam S. Predicting runway configuration transition timings using machine learning methods. In: 2021 Winter Simulation Conference (WSC); 13–15 Dec 2021; Phoenix, AZ, USA. New York, NY, USA: IEEE. pp. 1–12.
  • [37] Alves D, Mendonça F, Mostafa SS, Morgado-Dias F. Deep learning enhanced wind speed and direction forecasting for airport regions. Weather Forecast 2025; 40(1): 207–221.
  • [38] Taufiq LC, Putri A, Apriandy F. Simplified spatial wind vector interpolation method for airport runway orientation analysis. E3S Web Conf 2024; 476: 01057.
  • [39] Shankar A, Sahana BC. Efficient prediction of runway visual range by using a hybrid CNN-LSTM network architecture. Theor Appl Climatol 2024; 155(3): 2215–2232.
  • [40] Andy LJG, Alam S, Lilith N, Piplani R. A deep reinforcement learning approach for runway configuration management. J Air Transp Manag 2024; 120: 102672.
  • [41] International Civil Aviation Organization. Aerodromes—Annex 14 to the Convention on International Civil Aviation: Aerodrome Design and Operations, 3rd ed., vol. 1, 1999.
  • [42] Ashford N, Wright PH. Airport Engineering, 3rd ed. Hoboken, NJ, USA: Wiley; 1992.
  • [43] International Civil Aviation Organization. Annex 14 to the Convention on International Civil Aviation: Aerodrome Design and Operations, 7th ed., 2018.
  • [44] Baimukhametov G, White G. Review and improvement of runway friction and aircraft skid resistance regulation. Appl Sci 2025; 15(2): 548.
  • [45] Federal Aviation Administration. Advisory Circular 150/5300-13A: Airport Design. Washington, DC, USA: U.S. Department of Transportation; 2022.
  • [46] https://acukwik.com/Airport-Info/icao/epwa last accessed 2025/06/02

RUNWAY CONFIGURATION ANALYSIS BASED ON WIND DATA: A CASE STUDY OF WARSAW CHOPIN AIRPORT

Year 2025, Volume: 26 Issue: 3, 305 - 316, 25.09.2025
https://doi.org/10.18038/estubtda.1713749

Abstract

Runway configuration at airports is determined based on the prevailing wind direction to ensure the safety of flight operations. In this process, wind direction, crosswind component, and wind coverage are of critical importance. In this study, Warsaw Chopin Airport, which has intersecting runways, is examined. Using 12 years of METAR data for the airport, wind speed, wind direction, and wind coverage were analyzed. Finally, monthly wind contour models were generated for the airport, and the relationship between wind and runway configuration was evaluated seasonally.

Based on the wind data from Warsaw Chopin Airport, it was found that the prevailing wind directions are west, northwest, and east-southeast; the average wind direction is 250°, while the maximum and minimum wind directions are 270° and 360°, respectively. Additionally, the wind coverage was calculated as 99.77% with the combined use of the existing intersecting runways, indicating that the current runway configurations are operationally highly sufficient.

References

  • [1] Bellasio R. Analysis of wind data for airport runway design. J. Airl. Airport Manag. 2014; 4(2): 1–15.
  • [2] Maslovara A, Mirkovic B. Impact of tailwind on airport capacity and delay at Zurich Airport. Transp. Res. Procedia 2021; 59: 117–126.
  • [3] Hernández-Romero E, Valenzuela A, Rivas D. Probabilistic multi-aircraft conflict detection and resolution considering wind forecast uncertainty. Aerosp. Sci. Technol. 2020; 105: 105973.
  • [4] Dönmez K, Cetek C, Kaya O. Air traffic management in parallel-point merge systems under wind uncertainties. J. Air Transp. Manag. 2022; 104: 102268.
  • [5] Gu Y, Rhudy MB. Stochastic wind modeling and estimation for unmanned aircraft systems. In: AIAA Aviation Forum; 2019.
  • [6] Rodionova O, Sridhar B, Ng HK. Conflict resolution for wind-optimal aircraft trajectories in North Atlantic Oceanic Airspace with wind uncertainties. In: IEEE/AIAA Digital Avionics Systems Conference; 2016. pp. 1–10.
  • [7] Vela AE, Salaun E, Solak S, Feron E. A two-stage stochastic optimization model for air traffic conflict resolution under wind uncertainty. In: IEEE/AIAA Digital Avionics Systems Conference; 2009. pp. 1–10.
  • [8] Saeed A, Li C, Gan Z, Xie Y, Liu F. A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution. Energy 2022; 238: 122012.
  • [9] Chen G, Tang B, Zeng X, Zhou P, Kang P, Long H. et al. Short-term wind speed forecasting based on long short-term memory and improved BP neural network. Int. J. Electr. Power Energy Syst. 2022; 134: 107365.
  • [10] Wang Y, Zhang N, Wu L, Wang Y. Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method. Renew. Energy 2016; 94: 629–636.
  • [11] Zhang Y, Chen B, Pan G, Zhao Y. A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting. Energy Convers. Manag. 2019; 195: 180–197.
  • [12] Valdivia-Bautista SM, Domínguez-Navarro J. A, Pérez-Cisneros M, Vega-Gómez CJ, Castillo-Téllez B. Artificial intelligence in wind speed forecasting: A review. Energies 2023; 16(5): 2457.
  • [13] Dong X, Li C, Shi H, Zhou P. Short-term probabilistic wind speed predictions integrating multivariate linear regression and generative adversarial network methods. Atmosphere 2024; 15(3): 294.
  • [14] Chang SW. Crosswind-based optimization of multiple runway orientations. J. Adv. Transp. 2015; 49(1): 1–9.
  • [15] Barea A, Celis R, Cadarso L. An integrated model for airport runway assignment and aircraft trajectory optimisation. Transp. Res. C Emerg. Technol. 2024; 160: 104498.
  • [16] Kalyanam KM, Memarzadeh M, Crissman J, Yang, R, Tejasen KT Applying machine learning tools for runway configuration decision support. In: International Conference on Research in Air Transportation; 2024.
  • [17] Herrema F, Curran R, Hartjes S, Ellejmi M, Bancroft S, Schultz M. A machine learning model to predict runway exit at Vienna airport. Transp. Res. E Logist. Transp. Rev. 2019; 131: 329–342.
  • [18] Oktal H, Yildirim N. New model for the optimization of runway orientation. J. Transp. Eng. 2014; 140(3): 04013020.
  • [19] Oktal H, Yıldırım N. Optimisation of runway orientations for three-runway configurations. Aeronaut. J. 2016; 120(1233): 1693–1709.
  • [20] Mousa RM, Mumayiz SA. Optimization of runway orientation. J. Transp. Eng. 2000; 126(3): 228–236.
  • [21] Ahmed MS, Alam S, Barlow M. A cooperative co-evolutionary optimisation model for best-fit aircraft sequence and feasible runway configuration in a multi-runway airport. Aerospace 2018; 5(3): 85.
  • [22] Provan CA, Atkins SC. Optimization models for strategic runway configuration management under weather uncertainty. In: AIAA Aviation Technology, Integration and Operations Conference; 2010.
  • [23] Li L, Clarke JP, Chien HHC, Melconian T. A probabilistic decision-making model for runway configuration planning under stochastic wind conditions. In: IEEE/AIAA Digital Avionics Systems Conference; 2009. pp. 3–A.
  • [24] Singh M, Chopra T. Use of computer applications for determining the best possible runway orientation using wind rose diagrams. In: International Conference on Recent Trends in Transportation, Environmental and Civil Engineering; 2012. pp. 1–6.
  • [25] Oktavia S, Syafriani D, Dwiridal L, Sudiar NY. Analysis of surface wind speed at Minangkabau International Airport for the period 2011–2020 using the windrose method. J. Phys. Conf. Ser. 2023; 2582(1): 012006.
  • [26] Han S, Park B, Lee H. Analysis of the impacts of wind on final approach overshoot using historical flight and weather data. In: AIAA SciTech Forum; 2024. pp. 1–10.
  • [27] Tatli A, Suzer AE, Filik T, Karakoc TH. A case study on investigating probabilistic characteristics of wind speed data for green airport. In: Solutions for Maintenance Repair and Overhaul, ISATECH 2021; Springer, Cham; 2024. pp. 1–15.
  • [28] Sardjono W, Kusnoputranto H, Soesilo TEB, Utama DN, Sudirwan J. Study of runway crosswind and tailwind potential for airport sustainability: A study of Soekarno Hatta airport, Cengkareng, Indonesia. In: IOP Conf. Ser. Earth Environ. Sci. 2021; 729(1): 012012.
  • [29] Maslovara A, Mirković B. Impact of tailwind on airport capacity and delay at Zurich Airport. Transp. Res. Procedia 2021; 59: 117–126.
  • [30] O'Connor A, Kearney D. Evaluating the effect of turbulence on aircraft during landing and take-off phases. Int. J. Aviat. Aeronaut. Aerosp. 2018; 5(4): 10.
  • [31] Le Y, Li Y, Zhang Y. Impact of runway surface wind on afternoon and night flight operation at Linzhi Airport. Ind. Eng. Innov. Manag. 2022; 56: 48–58.
  • [32] Prema V, Rao KU. Time series decomposition model for accurate wind speed forecast. Renew Wind Water Sol 2015; 2: 18.
  • [33] Schlink U, Tetzlaff G. Wind speed forecasting from 1 to 30 minutes. Theor Appl Climatol 1998; 60: 191–198.
  • [34] Dhal R, Roy S, Tien SL, Taylor C, Wanke C. Operationally structured model for strategic runway configuration predictions. J Air Transp 2019; 27(2): 96–108.
  • [35] Wang Y, Zhang Y. Prediction of runway configurations and airport acceptance rates for multi-airport system using gridded weather forecast. Transp Res C Emerg Technol 2021; 125: 103049.
  • [36] Lau MEC, Lam AJG, Alam S. Predicting runway configuration transition timings using machine learning methods. In: 2021 Winter Simulation Conference (WSC); 13–15 Dec 2021; Phoenix, AZ, USA. New York, NY, USA: IEEE. pp. 1–12.
  • [37] Alves D, Mendonça F, Mostafa SS, Morgado-Dias F. Deep learning enhanced wind speed and direction forecasting for airport regions. Weather Forecast 2025; 40(1): 207–221.
  • [38] Taufiq LC, Putri A, Apriandy F. Simplified spatial wind vector interpolation method for airport runway orientation analysis. E3S Web Conf 2024; 476: 01057.
  • [39] Shankar A, Sahana BC. Efficient prediction of runway visual range by using a hybrid CNN-LSTM network architecture. Theor Appl Climatol 2024; 155(3): 2215–2232.
  • [40] Andy LJG, Alam S, Lilith N, Piplani R. A deep reinforcement learning approach for runway configuration management. J Air Transp Manag 2024; 120: 102672.
  • [41] International Civil Aviation Organization. Aerodromes—Annex 14 to the Convention on International Civil Aviation: Aerodrome Design and Operations, 3rd ed., vol. 1, 1999.
  • [42] Ashford N, Wright PH. Airport Engineering, 3rd ed. Hoboken, NJ, USA: Wiley; 1992.
  • [43] International Civil Aviation Organization. Annex 14 to the Convention on International Civil Aviation: Aerodrome Design and Operations, 7th ed., 2018.
  • [44] Baimukhametov G, White G. Review and improvement of runway friction and aircraft skid resistance regulation. Appl Sci 2025; 15(2): 548.
  • [45] Federal Aviation Administration. Advisory Circular 150/5300-13A: Airport Design. Washington, DC, USA: U.S. Department of Transportation; 2022.
  • [46] https://acukwik.com/Airport-Info/icao/epwa last accessed 2025/06/02
There are 46 citations in total.

Details

Primary Language English
Subjects Air-Space Transportation
Journal Section Articles
Authors

Özlem Şahin 0000-0002-9632-5533

Ali Tatlı 0000-0001-8925-8312

Publication Date September 25, 2025
Submission Date June 4, 2025
Acceptance Date July 14, 2025
Published in Issue Year 2025 Volume: 26 Issue: 3

Cite

AMA Şahin Ö, Tatlı A. RUNWAY CONFIGURATION ANALYSIS BASED ON WIND DATA: A CASE STUDY OF WARSAW CHOPIN AIRPORT. Estuscience - Se. September 2025;26(3):305-316. doi:10.18038/estubtda.1713749