Araştırma Makalesi
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Çoklu terk ediş noktası bulunan pistler için stokastik sıralama planlama modeli

Yıl 2022, Cilt: 5 Sayı: 2, 89 - 101, 29.10.2022
https://doi.org/10.51513/jitsa.1138520

Öz

Havalimanlarında pisti terk ediş taksi yolları farklı tipteki uçakların operasyonel performansları göz önünde bulundurularak geçmiş verilere dayalı olarak tasarlanmaktadır. Sıralama planlaması yapılırken uçakların pisti beklenen bir noktadan terk edeceği varsayılmaktadır. Ancak gerçek performanslar farklılık gösterebilmektedir. Aynı tip uçaklar pisti farklı noktalardan terk edebilmektedir. Bunun yanı sıra pisti aynı noktadan terk eden uçakların pist meşguliyet süreleri de farklılaşabilmektedir. Bu durum birden fazla pisti terk ediş taksi yolu içeren havalimanlarında trafik planlaması yapılırken iki farklı belirsizlik unsurunu ortaya çıkarmaktadır. Bunlardan ilki pisti terk ediş noktası (REP) belirsizliği diğeri ise pist meşguliyet süresi (ROT) belirsizliğidir. Bu çalışmada REP ve ROT belirsizlikleri göz önüne alınarak birden fazla terk ediş noktasına sahip pisti bulunan havalimanlarında geliş-kalkış sıralaması için stokastik programlama modeli geliştirilmiştir. Türkiye’de bir havalimanına gerçekleşen 154 geliş operasyonun radar verileri incelenmiş ve matematiksel modele entegre edilmiştir. Gerçek verilere dayalı olarak üretilen çeşitli beklenen iniş ve kalkış senaryoları matematiksel modelde koşturulmuştur. Daha sonra önerilen stokastik model deterministik ve ilk gelen ilk hizmet alır (FCFS) yaklaşımları ile toplam gecikme açısından kıyaslanmıştır. Sonuç olarak önerilen modelin belirsizliklere karşı sağlam sıralamalar sunmayı başardığı ve diğer yaklaşımlarla kıyaslandığında önemli gecikme kazanımları sağladığı gözlenmiştir.

Kaynakça

  • AIP Turkey. (2022). Aeronautical Information Publication; https://www.dhmi.gov.tr/Sayfalar/aipturkey.aspx
  • Alonso, A., Escudero, L. F., & Teresa Ortuño, M. (2000). A stochastic 0–1 program based approach for the air traffic flow management problem. European Journal of Operational Research, 120(1), 47–62. https://doi.org/10.1016/S0377-2217(98)00381-6
  • Atkin, J. A. D., Burke, E. K., Greenwood, J. S., & Reeson, D. (2008). On-line decision support for take-off runway scheduling with uncertain taxi times at London Heathrow airport. Journal of Scheduling, 11(5), 323–346. https://doi.org/10.1007/s10951-008-0065-9
  • Cecen, R. K., Cetek, C., & Kaya, O. (2020). Aircraft sequencing and scheduling in TMAs under wind direction uncertainties. The Aeronautical Journal, April, 1–17. https://doi.org/10.1017/aer.2020.68
  • Dai, L., & Hansen, M. (2020). Real-Time Prediction of Runway Occupancy Buffers. 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT), 1–11. https://doi.org/10.1109/AIDA-AT48540.2020.9049165
  • Dönmez, K., Çetek, C., & Kaya, O. (2021). Aircraft Sequencing and Scheduling in Parallel-Point Merge Systems for Multiple Parallel Runways. Transportation Research Record: Journal of the Transportation Research Board, 036119812110494. https://doi.org/10.1177/03611981211049410
  • Hanbong Lee, & Balakrishnan, H. (2012). Fast-time simulations of Detroit Airport operations for evaluating performance in the presence of uncertainties. 2012 IEEE/AIAA 31st Digital Avionics Systems Conference (DASC), 4E2-1-4E2-13. https://doi.org/10.1109/DASC.2012.6382349
  • Hockaday, S. L. M., & Kanafani, A. K. (1974). Developments in airport capacity analysis. Transportation Research, 8(3), 171–180. https://doi.org/10.1016/0041-1647(74)90004-5
  • ICAO. (2005). Aerodrome Design Manual (Doc 9157) Part 2 Taxiways, Aprons and Holding Bays.
  • ICAO. (2017). Procedures for air navigations services Air traffic management (Doc. 4444).
  • Jeddi, B. G., Shortle, J. F., & Sherry, L. (2006). Statistics of the Approach Process at Detroit Metropolitan Wayne County Airport. 703, 1–8.
  • Martinez, D., Belkoura, S., Cristobal, S., Herrema, F., & Wächter, P. (2018). A boosted tree framework for runway occupancy and exit prediction. SESAR Innovation Days, December.
  • Matthews, M., Wolfson, M., DeLaura, R., Evans, J., Reiche, C., Balakrishnan, H., & Michalek, D. (2009). Measuring the Uncertainty of Weather Forecasts Specific to Air Traffic Management Operations,. Aviation, Range, and Aerospace Meteorology Special Symposium on Weather-Air Traffic Management Integration, 1–17. http://tinyurl.com/cg5ldwc
  • Meijers, N. P. (2019). Data-driven predictive analytics of runway occupancy time for improved capacity at airports. December. https://dspace.mit.edu/handle/1721.1/128034%0Afiles/300/Meijers - 2019 - Data-driven predictive analytics of runway occupan.pdf%0Afiles/301/128034.html
  • Nguyen, A., Pham, D., Lilith, N., & Alam, S. (2020). Model Generalization in Arrival Runway Occupancy Time Prediction by Feature Equivalences. Icrat.
  • Nikoleris, T., & Hansen, M. (2016). Effect of Trajectory Prediction and Stochastic Runway Occupancy Times on Aircraft Delays. Transportation Science, 50(1), 110–119. https://doi.org/10.1287/trsc.2015.0599
  • Rappaport, D., Yu, P., Griffin, K., & Daviau, C. (2009). Quantitative Analysis of Uncertainty in Airport Surface Operations. 9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO), September, 1–16. https://doi.org/10.2514/6.2009-6987
  • Shone, R., Glazebrook, K., & Zografos, K. G. (2021). Applications of stochastic modeling in air traffic management: Methods, challenges and opportunities for solving air traffic problems under uncertainty. European Journal of Operational Research, 292(1), 1–26. https://doi.org/https://doi.org/10.1016/j.ejor.2020.10.039
  • Stamatopoulos, M. A., Zografos, K. G., & Odoni, A. R. (2004). A decision support system for airport strategic planning. Transportation Research Part C: Emerging Technologies, 12(2), 91–117. https://doi.org/10.1016/j.trc.2002.10.001
  • Tielrooij, M., Borst, C., Mulder, M., & Nieuwenhuisen, D. (2013). Supporting arrival management decisions by visualising uncertainty. SIDs 2013 - Proceedings of the SESAR Innovation Days, November.
  • Trani AA, Hobeika AG, Sherali HD, Kim BJ, Sadam CK. (1990) Runway Exit Designs for Capacity Improvement Demonstrations.

A stochastic sequence planning model for the runways with multiple exits

Yıl 2022, Cilt: 5 Sayı: 2, 89 - 101, 29.10.2022
https://doi.org/10.51513/jitsa.1138520

Öz

The runway exit points (REPs) of the airport are constructed considering the operational performance of different types of aircraft based on historical flight data. In sequence planning, it is assumed that aircraft will vacate the runway from an expected exit point. However, real performance can be uncertain, and the same type of aircraft may vacate the runway from different exit points rather than the expected point. In addition, the runway occupancy times (ROTs) of aircraft that vacate the runway from the same exit point may not be equal. This situation brings two types of uncertainty when making traffic plans in an airport with several REPs. The first uncertainty is the REP of the aircraft, and the second is the ROT uncertainty considering the exit points. In this study, a two-stage stochastic programming model was developed for aircraft sequencing in an airport that has multiple runway exit points. In the model, both runway exit and ROT uncertainties are considered. A runway with multiple exit points at an airport in Turkey was selected and flight track data of 154 arrival flights to this runway was examined. Various expected time of arrival and departure (ETAD) scenarios were generated based on real data and integrated into the mathematical models. The proposed model was then compared with deterministic and first come first serve (FCFS) approaches in terms of total delay. As a result of the comparison and analyses, the presented stochastic programming model provided robust solutions and delay savings compared to the other approaches.

Kaynakça

  • AIP Turkey. (2022). Aeronautical Information Publication; https://www.dhmi.gov.tr/Sayfalar/aipturkey.aspx
  • Alonso, A., Escudero, L. F., & Teresa Ortuño, M. (2000). A stochastic 0–1 program based approach for the air traffic flow management problem. European Journal of Operational Research, 120(1), 47–62. https://doi.org/10.1016/S0377-2217(98)00381-6
  • Atkin, J. A. D., Burke, E. K., Greenwood, J. S., & Reeson, D. (2008). On-line decision support for take-off runway scheduling with uncertain taxi times at London Heathrow airport. Journal of Scheduling, 11(5), 323–346. https://doi.org/10.1007/s10951-008-0065-9
  • Cecen, R. K., Cetek, C., & Kaya, O. (2020). Aircraft sequencing and scheduling in TMAs under wind direction uncertainties. The Aeronautical Journal, April, 1–17. https://doi.org/10.1017/aer.2020.68
  • Dai, L., & Hansen, M. (2020). Real-Time Prediction of Runway Occupancy Buffers. 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT), 1–11. https://doi.org/10.1109/AIDA-AT48540.2020.9049165
  • Dönmez, K., Çetek, C., & Kaya, O. (2021). Aircraft Sequencing and Scheduling in Parallel-Point Merge Systems for Multiple Parallel Runways. Transportation Research Record: Journal of the Transportation Research Board, 036119812110494. https://doi.org/10.1177/03611981211049410
  • Hanbong Lee, & Balakrishnan, H. (2012). Fast-time simulations of Detroit Airport operations for evaluating performance in the presence of uncertainties. 2012 IEEE/AIAA 31st Digital Avionics Systems Conference (DASC), 4E2-1-4E2-13. https://doi.org/10.1109/DASC.2012.6382349
  • Hockaday, S. L. M., & Kanafani, A. K. (1974). Developments in airport capacity analysis. Transportation Research, 8(3), 171–180. https://doi.org/10.1016/0041-1647(74)90004-5
  • ICAO. (2005). Aerodrome Design Manual (Doc 9157) Part 2 Taxiways, Aprons and Holding Bays.
  • ICAO. (2017). Procedures for air navigations services Air traffic management (Doc. 4444).
  • Jeddi, B. G., Shortle, J. F., & Sherry, L. (2006). Statistics of the Approach Process at Detroit Metropolitan Wayne County Airport. 703, 1–8.
  • Martinez, D., Belkoura, S., Cristobal, S., Herrema, F., & Wächter, P. (2018). A boosted tree framework for runway occupancy and exit prediction. SESAR Innovation Days, December.
  • Matthews, M., Wolfson, M., DeLaura, R., Evans, J., Reiche, C., Balakrishnan, H., & Michalek, D. (2009). Measuring the Uncertainty of Weather Forecasts Specific to Air Traffic Management Operations,. Aviation, Range, and Aerospace Meteorology Special Symposium on Weather-Air Traffic Management Integration, 1–17. http://tinyurl.com/cg5ldwc
  • Meijers, N. P. (2019). Data-driven predictive analytics of runway occupancy time for improved capacity at airports. December. https://dspace.mit.edu/handle/1721.1/128034%0Afiles/300/Meijers - 2019 - Data-driven predictive analytics of runway occupan.pdf%0Afiles/301/128034.html
  • Nguyen, A., Pham, D., Lilith, N., & Alam, S. (2020). Model Generalization in Arrival Runway Occupancy Time Prediction by Feature Equivalences. Icrat.
  • Nikoleris, T., & Hansen, M. (2016). Effect of Trajectory Prediction and Stochastic Runway Occupancy Times on Aircraft Delays. Transportation Science, 50(1), 110–119. https://doi.org/10.1287/trsc.2015.0599
  • Rappaport, D., Yu, P., Griffin, K., & Daviau, C. (2009). Quantitative Analysis of Uncertainty in Airport Surface Operations. 9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO), September, 1–16. https://doi.org/10.2514/6.2009-6987
  • Shone, R., Glazebrook, K., & Zografos, K. G. (2021). Applications of stochastic modeling in air traffic management: Methods, challenges and opportunities for solving air traffic problems under uncertainty. European Journal of Operational Research, 292(1), 1–26. https://doi.org/https://doi.org/10.1016/j.ejor.2020.10.039
  • Stamatopoulos, M. A., Zografos, K. G., & Odoni, A. R. (2004). A decision support system for airport strategic planning. Transportation Research Part C: Emerging Technologies, 12(2), 91–117. https://doi.org/10.1016/j.trc.2002.10.001
  • Tielrooij, M., Borst, C., Mulder, M., & Nieuwenhuisen, D. (2013). Supporting arrival management decisions by visualising uncertainty. SIDs 2013 - Proceedings of the SESAR Innovation Days, November.
  • Trani AA, Hobeika AG, Sherali HD, Kim BJ, Sadam CK. (1990) Runway Exit Designs for Capacity Improvement Demonstrations.
Toplam 21 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Kadir Dönmez 0000-0002-1236-0498

Yayımlanma Tarihi 29 Ekim 2022
Gönderilme Tarihi 30 Haziran 2022
Kabul Tarihi 29 Eylül 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 5 Sayı: 2

Kaynak Göster

APA Dönmez, K. (2022). A stochastic sequence planning model for the runways with multiple exits. Akıllı Ulaşım Sistemleri Ve Uygulamaları Dergisi, 5(2), 89-101. https://doi.org/10.51513/jitsa.1138520