  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.
