Abstract

This paper presents a flight trajectory data analytics framework for identifying spatial and temporal patterns in aircraft movement and providing a high-fidelity characterization of air traffic flows. The framework includes three modules : Collecting Data, Resampling trajectories, and Clustering air traffic flows at temporal and spacial scale. Different machine learning techniques are especially incorporated into the three modules to process aircraft trajectory data and enable the characterization of traffic flows.

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