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

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

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