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Uzun Kısa Vadeli Bellek Yöntemi ile Havayolu Yolcu Tahmini

Yıl 2021, Cilt: 5 Sayı: 2, 241 - 248, 20.12.2021
https://doi.org/10.30518/jav.1009331

Öz

Havayolu taşımacılığında uçuş operasyonlarının planlanması önemlidir. Uçuş operasyoları planlanırken en önemli unsur yolcu sayısıdır. Yolcu sayısını belirli bir zaman dilimi için tahmin etmek, havayolu firmasının planlamalarını daha uygun bir şekilde gerçekleştirmelerine yardımcı olabileceği gibi maliyetten de tasarruf etmelerini sağlayacaktır. Çalışmada, uzun kısa vadeli bellek (LSTM) yöntemi kullanılarak havayolu yolcu sayısı tahmin edilmiştir. Elazığ Havalimanına ait yolcu sayısı Vanilla LSTM yöntemi kullanılarak tahminleme gerçekleştirilmiştir. Önerilen yöntem ile Elazığ Havalimanına ait yolcu sayısı tahminlemesinde ortalama kare hata (MSE) sıfıra yakın ve karekök ortalama karesel hata (RMSE) 0.02 olarak bulunmuştur. Deneysel sonuçlar önerilen yaklaşımın havayolu yolcu tahminine katkı sağlayabileceğini göstermiştir.

Kaynakça

  • S. Kim and D. H. Shin, “Forecasting short-term air passenger demand using big data from search engine queries,” Autom. Constr., vol. 70, pp. 98–108, 2016.
  • A. Kanavos, F. Kounelis, L. Iliadis, and C. Makris, “Deep learning models for forecasting aviation demand time series,” Neural Comput. Appl., vol. 0123456789, 2021.
  • W. H. K. Tsui, H. Ozer Balli, A. Gilbey, and H. Gow, “Forecasting of Hong Kong airport’s passenger throughput,” Tour. Manag., vol. 42, no. 2014, pp. 62–76, 2014.
  • B. Flyvbjerg, M. K. Skamris Holm, and S. L. Buhl, “How (In)accurate are demand forecasts in public works projects?: The case of transportation,” J. Am. Plan. Assoc., vol. 71, no. 2, pp. 131–146, 2005.
  • Y. Xiao, J. J. Liu, Y. Hu, Y. Wang, K. K. Lai, and S. Wang, “A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting,” J. Air Transp. Manag., vol. 39, no. January 2019, pp. 1–11, 2014.
  • A. Maheshwari, N. Davendralingam, and D. A. Delaurentis, “A comparative study of machine learning techniques for aviation applications,” 2018 Aviat. Technol. Integr. Oper. Conf., no. July, 2018.
  • X. Yang et al., “A novel prediction model for the inbound passenger flow of urban rail transit,” Inf. Sci. (Ny)., vol. 566, pp. 347–363, 2021.
  • J. Tang, J. Liang, F. Liu, J. Hao, and Y. Wang, “Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network,” Transp. Res. Part C Emerg. Technol., vol. 124, no. January, p. 102951, 2021.
  • X. Zhu and L. Li, “Flight time prediction for fuel loading decisions with a deep learning approach,” Transp. Res. Part C Emerg. Technol., vol. 128, no. March, p. 103179, 2021.
  • DHMİ, “DHMİ,” 2021. [Online]. Available: https://www.dhmi.gov.tr/Sayfalar/Istatistikler.aspx.
  • X. Song et al., “Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model,” J. Pet. Sci. Eng., vol. 186, no. November 2019, p. 106682, 2020.
  • J. Guo, Z. Lao, M. Hou, C. Li, and S. Zhang, “Mechanical fault time series prediction by using EFMSAE-LSTM neural network,” Meas. J. Int. Meas. Confed., vol. 173, no. October 2020, p. 108566, 2021.
  • M. A. KIZRAK and B. BOLAT, “Uçak Motoru Sağlığı için Uzun-Kısa Süreli Bellek Yöntemi ile Öngörücü Bakım,” Bilişim Teknol. Derg., pp. 103–109, 2019.
  • A. Shakeel, T. Tanaka, and K. Kitajo, “Time-series prediction of the oscillatory phase of eeg signals using the least mean square algorithm-based ar model,” Appl. Sci., vol. 10, no. 10, 2020.
  • Y. Tian, K. Zhang, J. Li, X. Lin, and B. Yang, “LSTM-based traffic flow prediction with missing data,” Neurocomputing, vol. 318, pp. 297–305, 2018.
  • B. Yang, S. Sun, J. Li, X. Lin, and Y. Tian, “Traffic flow prediction using LSTM with feature enhancement,” Neurocomputing, vol. 332, pp. 320–327, 2019.

Air Passenger Forecasting with Long Short Term Memory Method

Yıl 2021, Cilt: 5 Sayı: 2, 241 - 248, 20.12.2021
https://doi.org/10.30518/jav.1009331

Öz

Flight operations planning is fundamental in air transport. The most important factor when flight operations planning is the number of passengers. Estimating the number of passengers for a specific period can help the airline plan more conveniently and save costs. In the study, the number of airline passengers is estimated using the long short-term memory (LSTM) method. The passenger number dataset of Elazig Airport is predicted using the Vanilla LSTM method. In the proposed method, Mean Square Error (MSE) is close to zero and Root Mean Square Error (RMSE) is found 0.02 in estimating the number of passengers belonging to Elazig Airport. Experimental results showed that the proposed approach could contribute to airline passenger estimation.

Kaynakça

  • S. Kim and D. H. Shin, “Forecasting short-term air passenger demand using big data from search engine queries,” Autom. Constr., vol. 70, pp. 98–108, 2016.
  • A. Kanavos, F. Kounelis, L. Iliadis, and C. Makris, “Deep learning models for forecasting aviation demand time series,” Neural Comput. Appl., vol. 0123456789, 2021.
  • W. H. K. Tsui, H. Ozer Balli, A. Gilbey, and H. Gow, “Forecasting of Hong Kong airport’s passenger throughput,” Tour. Manag., vol. 42, no. 2014, pp. 62–76, 2014.
  • B. Flyvbjerg, M. K. Skamris Holm, and S. L. Buhl, “How (In)accurate are demand forecasts in public works projects?: The case of transportation,” J. Am. Plan. Assoc., vol. 71, no. 2, pp. 131–146, 2005.
  • Y. Xiao, J. J. Liu, Y. Hu, Y. Wang, K. K. Lai, and S. Wang, “A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting,” J. Air Transp. Manag., vol. 39, no. January 2019, pp. 1–11, 2014.
  • A. Maheshwari, N. Davendralingam, and D. A. Delaurentis, “A comparative study of machine learning techniques for aviation applications,” 2018 Aviat. Technol. Integr. Oper. Conf., no. July, 2018.
  • X. Yang et al., “A novel prediction model for the inbound passenger flow of urban rail transit,” Inf. Sci. (Ny)., vol. 566, pp. 347–363, 2021.
  • J. Tang, J. Liang, F. Liu, J. Hao, and Y. Wang, “Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network,” Transp. Res. Part C Emerg. Technol., vol. 124, no. January, p. 102951, 2021.
  • X. Zhu and L. Li, “Flight time prediction for fuel loading decisions with a deep learning approach,” Transp. Res. Part C Emerg. Technol., vol. 128, no. March, p. 103179, 2021.
  • DHMİ, “DHMİ,” 2021. [Online]. Available: https://www.dhmi.gov.tr/Sayfalar/Istatistikler.aspx.
  • X. Song et al., “Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model,” J. Pet. Sci. Eng., vol. 186, no. November 2019, p. 106682, 2020.
  • J. Guo, Z. Lao, M. Hou, C. Li, and S. Zhang, “Mechanical fault time series prediction by using EFMSAE-LSTM neural network,” Meas. J. Int. Meas. Confed., vol. 173, no. October 2020, p. 108566, 2021.
  • M. A. KIZRAK and B. BOLAT, “Uçak Motoru Sağlığı için Uzun-Kısa Süreli Bellek Yöntemi ile Öngörücü Bakım,” Bilişim Teknol. Derg., pp. 103–109, 2019.
  • A. Shakeel, T. Tanaka, and K. Kitajo, “Time-series prediction of the oscillatory phase of eeg signals using the least mean square algorithm-based ar model,” Appl. Sci., vol. 10, no. 10, 2020.
  • Y. Tian, K. Zhang, J. Li, X. Lin, and B. Yang, “LSTM-based traffic flow prediction with missing data,” Neurocomputing, vol. 318, pp. 297–305, 2018.
  • B. Yang, S. Sun, J. Li, X. Lin, and Y. Tian, “Traffic flow prediction using LSTM with feature enhancement,” Neurocomputing, vol. 332, pp. 320–327, 2019.
Toplam 16 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Uzay Mühendisliği
Bölüm Araştırma Makaleleri
Yazarlar

Ömer Osman Dursun 0000-0001-5605-0419

Suat Toraman 0000-0002-7568-4131

Yayımlanma Tarihi 20 Aralık 2021
Gönderilme Tarihi 13 Ekim 2021
Kabul Tarihi 29 Kasım 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 5 Sayı: 2

Kaynak Göster

APA Dursun, Ö. O., & Toraman, S. (2021). Uzun Kısa Vadeli Bellek Yöntemi ile Havayolu Yolcu Tahmini. Journal of Aviation, 5(2), 241-248. https://doi.org/10.30518/jav.1009331

Journal of Aviation - JAV 


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