Performance Prediction of Airport Traffic Using LSTM and CNN-LSTM Models
DOI:
https://doi.org/10.30812/matrik.v22i3.3032Keywords:
Accuracy Prediction, Airport Traffic, CNN-LSTM, COVID-19, LSTMAbstract
During the COVID-19 pandemic, airports faced a significant drop in passenger numbers, impacting the vital hub of the aircraft transportation industry. This study aimed to evaluate whether Long Short-Term Memory Network (LSTM) and Convolutional Neural Network - Long Short-Term Memory Network (CNN-LSTM) offer more accurate predictions for airport traffic during the COVID-19 pandemic from March to December 2020. The studies involved data filtering, applying min-max scaling, and dividing the dataset into 80% training and 20% testing sets. Parameter adjustment was performed with different optimizers such as RMSProp, Stochastic Gradient Descent (SGD), Adam, Nadam, and Adamax. Performance evaluation uses metrics that include Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-squared (R2). The best LSTM model achieved an impressive MAPE score of 0.0932, while the CNN-LSTM model had a slightly higher score of 0.0960. In particular, the inclusion of a balanced data set representing a percentage of the base period for each airport had a significant impact on improving prediction accuracy. This research contributes to providing stakeholders with valuable insights into the effectiveness of predicting airport traffic patterns during these unprecedented times.
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of large economic shocks: The case of COVID-19,†Journal of Air Transport Management, vol. 91, p. 102007, 2021.
[3] X. Zhang, H. Liu, Y. Zhao, and X. Zhang, “Multifractal detrended fluctuation analysis on air traffic flow time series: A single
airport case,†Physica A: Statistical Mechanics and its Applications, vol. 531, p. 121790, 2019.
[4] H. Liu, X. Zhang, and X. Zhang, “Multiscale multifractal analysis on air traffic flow time series: A single airport departure flight
case,†Physica A: Statistical Mechanics and its Applications, vol. 545, p. 123585, 2020.
[5] D. C. Tasc´on and O. D´ıaz Olariaga, “Air traffic forecast and its impact on runway capacity. A System Dynamics approach,â€
Journal of Air Transport Management, vol. 90, p. 101946, 2021.
[6] H. Abbasimehr and R. Paki, “Improving time series forecasting using LSTM and attention models,†Journal of Ambient Intelligence
and Humanized Computing, vol. 13, no. 1, pp. 673–691, 2022.
[7] 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.
[8] I. E. Livieris, E. Pintelas, and P. Pintelas, “A CNNLSTM model for gold price time-series forecasting,†Neural Computing and
Applications, vol. 32, no. 23, pp. 17 351–17 360, 2020.
[9] J. Yu, A. de Antonio, and E. Villalba-Mora, “Deep Learning (CNN, RNN) Applications for Smart Homes: A Systematic
Review,†Computers, vol. 11, no. 2, 2022.
[10] A´ . Rodr´ıguez-Sanz, F. G. Comendador, R. A. Valde´s, J. Pe´rez-Casta´n, R. B. Montes, and S. C. Serrano, “Assessment of airport
arrival congestion and delay: Prediction and reliability,†Transportation Research Part C: Emerging Technologies, vol. 98, pp.
255–283, 2019.
[11] M. Lambelho, M. Mitici, S. Pickup, and A. Marsden, “Assessing strategic flight schedules at an airport using machine learningbased
flight delay and cancellation predictions,†Journal of Air Transport Management, vol. 82, p. 101737, 2020.
[12] W. Zeng, J. Li, Z. Quan, and X. Lu, “A Deep Graph-Embedded LSTM Neural Network Approach for Airport Delay Prediction,â€
Journal of Advanced Transportation, vol. 2021, p. 6638130, 2021.
[13] Q. Li, X. Guan, and J. Liu, “A CNN-LSTM framework for flight delay prediction,†Expert Systems with Applications, vol. 227,
p. 120287, 2023.
[14] J. Qu, M. Xiao, L. Yang, and W. Xie, “Flight Delay Regression Prediction Model Based on Att-Conv-LSTM,†2023.
[15] L. Ma and S. Tian, “A Hybrid CNN-LSTM Model for Aircraft 4D Trajectory Prediction,†IEEE Access, vol. 8, pp. 134 668–
134 680, 2020.
[16] A. Vidal and W. Kristjanpoller, “Gold volatility prediction using a CNN-LSTM approach,†Expert Systems with Applications,
vol. 157, p. 113481, 2020.
[17] T. Y. Kim and S. B. Cho, “Predicting residential energy consumption using CNN-LSTM neural networks,†Energy, vol. 182,
pp. 72–81, 2019.
[18] K. He, L. Ji, C. W. D. Wu, and K. F. G. Tso, “Using SARIMACNNLSTM approach to forecast daily tourism demand,†Journal
of Hospitality and Tourism Management, vol. 49, pp. 25–33, 2021.
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[20] S. M. Kasongo and Y. Sun, “Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the
UNSW-NB15 Dataset,†Journal of Big Data, vol. 7, no. 1, p. 105, 2020.
[21] A. Agga, A. Abbou, M. Labbadi, and Y. El Houm, “Short-term self consumption PV plant power production forecasts based on
hybrid CNN-LSTM, ConvLSTM models,†Renewable Energy, vol. 177, pp. 101–112, 2021.
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IEEE Access, vol. 8, pp. 180 544–180 557, 2020.
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Published
2023-07-28
Issue
Section
Articles
How to Cite
[1]
W. Riyadi and J. Jasmir, “Performance Prediction of Airport Traffic Using LSTM and CNN-LSTM Models”, MATRIK, vol. 22, no. 3, pp. 627–638, Jul. 2023, doi: 10.30812/matrik.v22i3.3032.
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