Air-traffic Flow Prediction with Deep Learning: A Case Study for Diyarbakır Airport

Year 2023, Volume: 7 Issue: 2, 196 - 203, 25.07.2023

Ömer Osman Dursun

https://doi.org/10.30518/jav.1307741

Cited By: 1

Abstract

Aviation industry develops rapidly. So the continuous growth of the aviation, accurate predictions play a crucial role in managing air traffic and optimizing airport operations. The prediction process involves various factors such as weather conditions, airport traffic, flight schedules, and historical data. Advanced techniques like machine learning contribute to enhancing the accuracy of predictions. In this context, air traffic data belonging to Diyarbakır province were utilized to predict the number of arrival aircraft to the airport using both traditional Autoregressive (AR) model and deep learning architecture, specifically the stacked Long Short-Term Memory (LSTM) model. The results indicate that the stacked LSTM model outperformed the AR model in terms of air traffic estimation. The AR model had a quite poorly MSE value of 48043.35 and an RMSE value of 219.18, while the stacked LSTM model achieved a significantly higher MSE value of 0.03 and an RMSE value of 0.17. The lower MSE values obtained by the stacked LSTM model indicate its ability to make more accurate predictions compared to the AR model. The stacked LSTM model's predictions were closer to the actual values, resulting in a more realistic estimation of air traffic. Accurate predictions enable efficient resource management, passenger planning, and airport security measures. Continuous efforts in predicting aircraft landings are necessary for the effective functioning of the aviation industry. In this study highlights the importance of predicting the number of aircraft landings at airports.

Keywords

Aviation, Deep learning, Air-traffic flow prediction, Long-short term memory

References

• Aygun, H., Dursun, O. O., & Toraman, S. (2023). Machine learning based approach for forecasting emission parameters of mixed flow turbofan engine at high power modes. Energy, 271(January), 127026.
• Bakreen, S., Markovskaya, E., Merzlikin, I., & Mottaeva, A. (2022). Development of the approach to the analysis of aviation industry’s adaptation to seasonal disruptions. Transportation Research Procedia, 63, 1431–1443.
• Bombelli, A., Santos, B. F., & Tavasszy, L. (2020). Analysis of the air cargo transport network using a complex network theory perspective. Transportation Research Part E: Logistics and Transportation Review, 138(April), 101959.
• Dalmau, R. (2022). Predicting the likelihood of airspace user rerouting to mitigate air traffic flow management delay. Transportation Research Part C: Emerging Technologies, 144(August), 103869.
• DHMİ. (n.d.). DHMİ. Retrieved December 13, 2016, from http://www.dhmi.gov.tr/istatistik.aspx
• Di Gravio, G., Mancini, M., Patriarca, R., & Costantino, F. (2015). Overall safety performance of Air Traffic Management system: Forecasting and monitoring. Safety Science, 72, 351–362.
• Dursun, Ö. O., & Toraman, S. (2021). Uzun Kısa Vadeli Bellek Yöntemi ile Havayolu Yolcu Tahmini. Journal of Aviation, 5(1), 241–248.
• Guo, J., Lao, Z., Hou, M., Li, C., & Zhang, S. (2021). Mechanical fault time series prediction by using EFMSAE-LSTM neural network. Measurement: Journal of the International Measurement Confederation, 173 (October 2020), 108566.
• Jiang, W., & Luo, J. (2022). Graph neural network for traffic forecasting: A survey. Expert Systems with Applications, 207(December 2021), 117921.
• Jo, A. H., & Chang, Y. T. (2023). The effect of airport efficiency on air traffic, using DEA and multilateral resistance terms gravity models. Journal of Air Transport Management, 108(January), 102364.
• Kızrak, M. A., & Bolat, B. (2019). Uçak Motoru Sağlığı için Uzun-Kısa Süreli Bellek Yöntemi ile Öngörücü Bakım. Bilişim Teknolojileri Dergisi, 103–109.
• Kotegawa, T., DeLaurentis, D. A., & Sengstacken, A. (2010). Development of network restructuring models for improved air traffic forecasts. Transportation Research Part C: Emerging Technologies, 18(6), 937–949.
• Li, X., & Zhao, Y. (2023). Evaluation of sound environment in departure lounges of a large hub airport. Building and Environment, 232(January).
• Méndez, M., Merayo, M. G., & Núñez, M. (2023). Long-term traffic flow forecasting using a hybrid CNN-BiLSTM model. Engineering Applications of Artificial Intelligence, 121(March), 106041.
• Mondoloni, S., & Rozen, N. (2020). Aircraft trajectory prediction and synchronization for air traffic management applications. Progress in Aerospace Sciences, 119(20).
• Shakeel, A., Tanaka, T., & Kitajo, K. (2020). Time-series prediction of the oscillatory phase of eeg signals using the least mean square algorithm-based ar model. Applied Sciences (Switzerland), 10(10).
• Solvoll, G., Mathisen, T. A., & Welde, M. (2020). Forecasting air traffic demand for major infrastructure changes. Research in Transportation Economics, 82(September 2019), 100873.
• Song, X., Liu, Y., Xue, L., Wang, J., Zhang, J., Wang, J., Cheng, Z. (2020). Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model. Journal of Petroleum Science and Engineering, 186(November 2019), 106682.
• Standfuss, T., Fricke, H., & Whittome, M. (2022). Forecasting European Air Traffic Demand - How deviations in traffic affect ANS performance. Transportation Research Procedia, 59, 105–116.
• Tanrıverdi, G., Ecer, F., & Durak, M. Ş. (2022). Exploring factors affecting airport selection during the COVID-19 pandemic from air cargo carriers’ perspective through the triangular fuzzy Dombi-Bonferroni BWM methodology. Journal of Air Transport Management, 105(June).
• Tascón, D. C., & Díaz Olariaga, O. (2021). Air traffic forecast and its impact on runway capacity. A System Dynamics approach. Journal of Air Transport Management, 90(September 2020).