Non-Parametric Model for Curvature Classification of Departure Flight Trajectory Segments

This study introduces a novel approach for classifying flight trajectory curvature, focusing on early-stage flight characteristics to detect anomalies and deviations. The method intentionally avoids direct coordinate data and instead leverages a combination of trajectory-derived and meteorological features. This research analysed 9849 departure flight trajectories originating from 14 different airports. Two distinct trajectory classes were established through manual visual inspection, differentiated by curvature patterns. This categorisation formed the ground truth for evaluating trained machine learning (ML) classifiers from different families. The comparative analysis demonstrates that the Random Forest (RF) algorithm provides the most effective classification model. RF excels at summarising complex trajectory information and identifying non-linear relationships within the early-flight data. A key contribution of this work is the validation of specific predictors. The theoretical definitions of direction change (using vector values to capture dynamic movement) and diffusion distance (using scalar values to represent static displacement) proved highly effective. Their selection as primary predictors is supported by their ability to represent the essential static and dynamic properties of the trajectory, enabling the model to accurately classify flight paths and potential deviations before the flight is complete. This approach offers significant potential for enhancing real-time air traffic monitoring and safety systems.