Data Driven Approach to Noise Abatement Procedure Classification at Amsterdam Airport Schiphol
M. Chou (TU Delft - Aerospace Engineering)
M.J. Ribeiro – Graduation committee member (TU Delft - Aerospace Engineering)
J. Ellerbroek – Mentor (TU Delft - Aerospace Engineering)
Maarten Zorgdrager – Mentor (Royal Schiphol Group)
J. Sun – Graduation committee member (TU Delft - Aerospace Engineering)
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Abstract
Accurate identification of aircraft noise abatement procedures (NAPs) is essential for reliable environmental impact assessment and noise modelling. However, current analyses of the implementation of noise abatement procedures are mainly based on interviews with airlines. Previous studies establish methods to model NADP procedures and estimate flap settings. However, only limited studies have proposed methods for classifying noise abatement procedures. This study presents a data-driven approach for the classification of Noise Abatement Departure Procedure (NADP), Continuous Descent Approaches (CDA) and Reduced Flap Setting Approach using flight trajectory data. The framework integrates energy-based performance, flight performance indicators and machine learning techniques to classify flight trajectories into specific noise abatement procedures. The methodology is applied to operations at Amsterdam Airport Schiphol, evaluated on a dataset comprising over 19,000 inbound and over 19,000 outbound flight data in May 2025, alongside a machine learning training dataset of over 7,000 inbound flights. The results indicate that the developed framework is capable of classifying flight trajectories from enhanced mode-S and radar data to specific noise abatement procedures. However, the developed framework is constrained mainly by data resolution and feature sensitivity. Future work should focus on improving temporal resolution, expanding the dataset and incorporating event-based validation for enhancing model reliability.