Phased Flight Trajectory Prediction with Deep Learning

• URL: http://arxiv.org/abs/2203.09033v1
• Date: Thu, 17 Mar 2022 02:16:02 GMT
• Title: Phased Flight Trajectory Prediction with Deep Learning
• Authors: Kai Zhang, Bowen Chen
• Abstract summary: The unprecedented increase of commercial airlines and private jets over the past ten years presents a challenge for air traffic control. Precise flight trajectory prediction is of great significance in air transportation management, which contributes to the decision-making for safe and orderly flights. We propose a phased flight trajectory prediction framework that can outperform state-of-the-art methods for flight trajectory prediction for large passenger/transport airplanes.
• Score: 8.898269198985576
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The unprecedented increase of commercial airlines and private jets over the next ten years presents a challenge for air traffic control. Precise flight trajectory prediction is of great significance in air transportation management, which contributes to the decision-making for safe and orderly flights. Existing research and application mainly focus on the sequence generation based on historical trajectories, while the aircraft-aircraft interactions in crowded airspace especially the airspaces near busy airports have been largely ignored. On the other hand, there are distinct characteristics of aerodynamics for different flight phases, and the trajectory may be affected by various uncertainties such as weather and advisories from air traffic controllers. However, there is no literature fully considers all these issues. Therefore, we proposed a phased flight trajectory prediction framework. Multi-source and multi-modal datasets have been analyzed and mined using variants of recurrent neural network (RNN) mixture. To be specific, we first introduce spatio temporal graphs into the low-altitude airway prediction problem, and the motion constraints of an aircraft are embedded to the inference process for reliable forecasting results. In the en-route phase, the dual attention mechanism is employed to adaptively extract much more important features from overall datasets to learn the hidden patterns in dynamical environments. The experimental results demonstrate our proposed framework can outperform state-of-the-art methods for flight trajectory prediction for large passenger/transport airplanes.

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