M²FlightNet: Multiscale Modeling Network for Short-Term Flight Trajectory Prediction

Accurate short-term flight trajectory prediction plays an important role in Air Traffic Control, which can ensure the safety of air transportation. Two main issues limit the prediction performance of existing works. The first issue is non-discriminative feature representation for flight trajectory attributes. The second issue is that real-world flight trajectories involve complex temporal dependencies, yet most existing methods extract features from a single time scale, failing to capture intricate temporal patterns. In this paper, we propose a framework (M2FlightNet) to address the above issues. M2FlightNet employs a differential representation for longitude and latitude to provide discriminative features and reflect the underlying temporal variations in trajectories. A mixture of experts trend extractor is adopted to capture trend features in vertical flight state series. To fully explore the diverse temporal patterns in horizontal flight state series, a multi-scale patch network is delicately designed to serve as the backbone. The multi-scale patch network exploits stacked patch mixer blocks to capture inter- and intra-patch dependencies under different time scales, and further integrates multi-scale temporal features across different scales and variables. After that, M2FlightNet ensembles multiple predictors to make direct multi-step prediction and reconstruct the trajectories. Extensive experiments on six real-world datasets demonstrate that M2FlightNet outperforms the state-of-the-art methods across various settings.