Aircraft Take-off Weight Prediction with Operational Data and Supervised Learning
A.I. Gheorghe (Student TU Delft)
Junzi Sun (TU Delft - Operations & Environment)
Marta Ribeiro (TU Delft - Operations & Environment)
Pascal Hop (EUROCONTROL)
Benjamin Cramet (EUROCONTROL)
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Abstract
Predicting aircraft Take-Off Weight (TOW) has been a long-standing goal for aviation stakeholders, especially for operational and regulatory bodies involved in flight planning. Accurate TOW values would enable better emissions computation, leading to more effective regulation of aviation’s climate impact. However, aircraft operators prefer to keep TOWs confidential because they are sensitive to operational trends and cost indices. Consequently, many works have attempted to circumvent this gap by predicting TOW values. Unfortunately, limited success has been achieved primarily due to the lack of accurate real-world operational data. This study is unique in utilizing operational TOW data provided by airlines. We predict TOW before takeoff based solely on Flight Plan and Terminal Aerodrome Forecast parameters, primarily focusing on flights at Amsterdam Airport Schiphol. The accuracy of several Machine Learning algorithms is directly compared. The best Mean Absolute Percentage Error of 2.17% on the Schiphol testing dataset is achieved. The model is further validated on flights at Paris- Charles de Gaulle Airport and Brussels South Charleroi Airport with errors of 4.07% and 3.41%. We found that the distribution of flights in the training dataset, particularly aircraft and airline types, significantly influenced the model’s applicability. Recommendations are also made on how to improve the model further.