PREDICTION PERFORMANCE OF AIRPORT TRAFFIC USING BILSTM AND CNN-BI-LSTM MODELS
Abstract
The COVID-19 pandemic has had a significant and enduring impact on the aviation industry, necessitating the accurate prediction of airport traffic. This study compares the predictive accuracy of biLSTM (Bidirectional Long Short-Term Memory) and CNN-biLSTM (Convolutional Neural Network-Bidirectional Long Short-Term Memory) models using various optimization techniques such as RMSProp, Stochastic Gradient Descent (SGD), Adam, Nadam, and Adamax. The evaluation is based on Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) indices. In the United States, the biLSTM model utilizing the Nadam optimizer achieved an MAPE score of 9.76%. On the other hand, the CNN-biLSTM model utilizing the Nadam optimizer demonstrated a slightly improved MAPE score of 9.62%. For Australia, the biLSTM model using the Nadam optimizer obtained an MAPE score of 31.52%. However, the CNN-biLSTM model employing the RMSprop optimizer had a marginally higher MAPE score of 33.33%. In Chile, the biLSTM model using the Adam optimizer obtained an MAPE score of 44.04%. Conversely, the CNN-biLSTM model using the RMSprop optimizer had a slightly higher MAPE score of 44.09%. Lastly, in Canada, the biLSTM model using the Nadam optimizer achieved a comparatively low MAPE score of 14.99%. Similarly, the CNN-biLSTM model utilizing the Adam optimizer demonstrated a slightly better MAPE score of 14.75%. These results highlight that the choice of optimization technique, model architecture, and balanced dataset can significantly influence the prediction accuracy of airport traffic.
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References
Z. Yang, Y. Wang, J. Li, L. Liu, J. Ma, and Y. Zhong, “Airport Arrival Flow Prediction considering Meteorological Factors Based on Deep-Learning Methods,” Complexity, vol. 2020, pp. 1-11, 2020, doi: 10.1155/2020/6309272.
S. V. Gudmundsson, M. Cattaneo, and R. Redondi, “Forecasting temporal world recovery in air transport markets in the presence of large economic shocks: The case of COVID-19,” J. Air Transp. Manag., vol. 91, no. June 2020, p. 102007, 2021, doi: 10.1016/j.jairtraman.2020.102007.
X. Zhang, H. Liu, Y. Zhao, and X. Zhang, “Multifractal detrended fluctuation analysis on air traffic flow time series: A single airport case,” Phys. A Stat. Mech. its Appl., vol. 531, p. 121790, 2019, doi: 10.1016/j.physa.2019.121790.
H. Liu, X. Zhang, and X. Zhang, “Multiscale multifractal analysis on air traffic flow time series: A single airport departure flight case,” Phys. A Stat. Mech. its Appl., vol. 545, p. 123585, 2020, doi: https://doi.org/10.1016/j.physa.2019.123585.
D. C. Tascón and O. Díaz Olariaga, “Air traffic forecast and its impact on runway capacity. A System Dynamics approach,” J. Air Transp. Manag., vol. 90, no. April 2020, p. 101946, 2021, doi: 10.1016/j.jairtraman.2020.101946.
H. Abbasimehr and R. Paki, “Improving time series forecasting using LSTM and attention models,” J. Ambient Intell. Humaniz. Comput., vol. 13, no. 1, pp. 673–691, 2022, doi: 10.1007/s12652-020-02761-x.
P. B. Weerakody, K. W. Wong, G. Wang, and W. Ela, “A review of irregular time series data handling with gated recurrent neural networks,” Neurocomputing, vol. 441, pp. 161–178, 2021, doi: 10.1016/j.neucom.2021.02.046.
B. Lim and S. Zohren, “Time-series forecasting with deep learning: a survey,” Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., vol. 379, no. 2194, p. 20200209, 2021, doi: 10.1098/rsta.2020.0209.
I. E. Livieris, E. Pintelas, and P. Pintelas, “A CNN–LSTM model for gold price time-series forecasting,” Neural Comput. Appl., vol. 32, no. 23, pp. 17351–17360, 2020, doi: 10.1007/s00521-020-04867-x.
Y. Chen et al., “Stock Price Forecast Based on CNN-BiLSTM-ECA Model,” Sci. Program., vol. 2021, p. 2446543, 2021, doi: 10.1155/2021/2446543.
W. Zhuang and Y. Cao, “Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information,” Applied Sciences, vol. 12, no. 17. p. 8714, 2022. doi: 10.3390/app12178714.
H. Wang, J. Wang, L. Cao, Y. Li, Q. Sun, and J. Wang, “A Stock Closing Price Prediction Model Based on CNN-BiSLSTM,” Complexity, vol. 2021, p. 5360828, 2021, doi: 10.1155/2021/5360828.
Y. Cao, J. Cao, Z. Zhou, and Z. Liu, “Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM,” Electronics, vol. 10, no. 9. p. 1007, 2021. doi: 10.3390/electronics10091007.
D. ZHOU, X. ZHUANG, and H. ZUO, “A hybrid deep neural network based on multi-time window convolutional bidirectional LSTM for civil aircraft APU hazard identification,” Chinese J. Aeronaut., vol. 35, no. 4, pp. 344–361, 2022, doi: 10.1016/j.cja.2021.03.031.
W. Zeng, J. Li, Z. Quan, and X. Lu, “A deep graph-embedded LSTM neural network approach for airport delay prediction,” J. Adv. Transp., vol. 2021, pp. 1-15, 2021, doi: 10.1155/2021/6638130.
L. Wang, X. Li, and J. Mao, “Integrating ARIMA and Bidirectional LSTM to Predict ETA in Multi-Airport Systems,” in 2020 Integrated Communications Navigation and Surveillance Conference (ICNS), 2020, pp. 3F2-1-3F2-15. doi: 10.1109/ICNS50378.2020.9222874.
T. Shin, “COVID-19’s Impact on Airport Traffic.” https://www.kaggle.com/datasets/terenceshin/covid19s-impact-on-airport-traffic (accessed Jan. 10, 2023).
S. M. Kasongo and Y. Sun, “Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset,” J. Big Data, vol. 7, no. 1, pp. 1-20, 2020, doi: 10.1186/s40537-020-00379-6.
T. Peng, C. Zhang, J. Zhou, and M. S. Nazir, “An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting,” Energy, vol. 221, p. 119887, 2021, doi: 10.1016/j.energy.2021.119887.
M. Méndez, M. G. Merayo, and M. Núñez, “Long-term traffic flow forecasting using a hybrid CNN-BiLSTM model,” Eng. Appl. Artif. Intell., vol. 121, no. July 2022, p. 106041, 2023, doi: 10.1016/j.engappai.2023.106041.
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