Prediction of Traffic Take-Off Times at Out-stations

Abstract

Reducing uncertainty in air traffic flow management is crucial for maintaining safety and efficiency in modern aviation. Additionally, forecasting Actual Take-Off Times (ATOT) for flights across Europe is particularly challenging due to the diverse flight-specific variables and operational conditions. This study focuses on enhancing ATOT prediction for flights arriving at Amsterdam Schiphol Airport from European out-stations by leveraging machine learning techniques, specifically a Long Short-Term Memory (LSTM) neural network, augmented with a Multihead Attention mechanism. A model capable of capturing complex temporal dependencies and operational factors influencing the ATOT is developed utilizing data from Electronic Flight Data (EFD) messages, weather reports and a EUROCONTROL dataset. The model’s performance is evaluated against traditional ensemble methods and the current Decision Support Tool (DST) system used by Luchtverkeersleiding Nederland (LVNL). Results indicate that the LSTM model outperforms existing models including a reproduction of the DST, achieving a Mean Absolute Error (MAE) of 12.05 minutes at a forecast horizon of 4 hours, demonstrating significant improvements. This assessment underscores the importance of factors such as the knock-on effect in delay prediction and suggests that integrating advanced machine learning models can significantly enhance demand forecasting, leading to more efficient air traffic management and reduced delays at Schiphol Airport.