Predicting Flight Trajectory in Convective Weather through Boosted Spatiotemporal Deep Learning Ensemble

Flight trajectory prediction is one of the key issues in ensuring the safety of air traffic, providing the air traffic controller with the foresight of flight conflicts so that control instructions for pilots can be preconceived. In a complicated mechanism, flight trajectories can be severely affected by convective weather, making accurately predicting trajectories challenging. To address this problem, we propose a boosted spatiotemporal deep learning ensemble for mining the law of how convective weather affects flight trajectory stretching. Instead of conventionally representing trajectory data in a geographic coordinate system, we design a relative coordinate system for gaining new trajectory features which tangibly reflect trajectory's relations with planned route and convective weather. Besides, we raise a boosted ensemble framework of spatiotemporal deep learning models, trained by the samples pairing sequential trajectory with graphical weather, dedicating to strengthen the mining of the high-value training samples that involve explicit flight deviations caused by convective weather. The experiments using actual flight and weather data demonstrate our method's superiority in predicting flight trajectory affected by convective weather.