  Accurate multi-step flight trajectory prediction plays an important role in
Air Traffic Control, which can ensure the safety of air transportation. Two
main issues limit the flight trajectory prediction performance of existing
works. The first issue is the negative impact on prediction accuracy caused by
the significant differences in data range. The second issue is that real-world
flight trajectories involve underlying temporal dependencies, and existing
methods fail to reveal the hidden complex temporal variations and only extract
features from one single time scale. To address the above issues, we propose
FlightPatchNet, a multi-scale patch network with differential coding for flight
trajectory prediction. Specifically, FlightPatchNet first utilizes the
differential coding to encode the original values of longitude and latitude
into first-order differences and generates embeddings for all variables at each
time step. Then, a global temporal attention is introduced to explore the
dependencies between different time steps. To fully explore the diverse
temporal patterns in flight trajectories, 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. Finally,
FlightPatchNet ensembles multiple predictors to make direct multi-step
prediction. Extensive experiments on ADS-B datasets demonstrate that our model
outperforms the competitive baselines. Code is available at:
https://github.com/FlightTrajectoryResearch/FlightPatchNet.
