A Generalized Approach to Aircraft Trajectory Prediction via Supervised Deep Learning

As research advances diverse forms and missions of aircraft, the National Airspace System (NAS) will become increasingly crowded, limiting the current aviation spectrum to accommodate future air operations. To tackle this challenge, the concept of intelligent spectrum management is proposed for autonomous and dynamic resource allocations, where accurate aircraft position estimation is one of the crucial tasks. However, current research on flight trajectory prediction has been limited in its scope of efforts, frequently utilizing a unique flight route, architecture, set of weather data, and date range. In this paper, we propose a generalized hybrid-recurrent predictive model for flight trajectory prediction. Our generalizable deep learning approach not only improves trajectory prediction accuracy, but also can be contextualized by exploring a large amount of data. Experimental results illustrate a tradeoff between horizontal and vertical errors as flight data are generalized across dates and routes.