Towards aircraft trajectory prediction using LSTM networks

Trajectory prediction allows for better predictability, security and efficiency in the operations of the modern Air Traffic Management. LSTM networks have been successfully applied to make short-term trajectory predictions. However, the criticality of the supervision of these operations in high density traffic zones, such as the Terminal Maneuvering Area (TMA) around the airports, require methods that provide long-term, precise predictions. In this paper, we propose a LSTM-based architecture for trajectory prediction using surveillance data (ADS-B). We conduct our experiments on the case study of flights arriving at the Madrid Barajas-Adolfo Suárez airport (Spain), using nine months worth of data. In particular, we focus on longer-term predictions than the state of the art, predicting the next 150 seconds at any point in the trajectory. This model provides an increased accuracy for 2D positioning, with mean absolute errors of 0.0238 and 0.0544 degrees for latitude and longitude, respectively, in the TMA of the destination airport.