This is a method for long-term ship trajectory prediction. It mainly improves the trajectory prediction performance by data augmentation, dynamic-aware attention, and knowledge-inspired loss function.

geopy==2.2.0
ipdb==0.13.9
matplotlib==3.5.1
numpy==1.22.4
numpy_ext==0.9.8
pandas==1.4.3
scipy==1.8.1

A simple dataset is provided for testing the code, or you can generate your own by rewriting data_loader.py.This project provides some commented out code that we use to process the data for reference.

After configuring the environment, just run main_MSTFomer.py directly. Also, you can test different data by changing the parameters inside. The file where the logs are saved can be changed by changing the path in log.py.

If you find this repository useful in your research, please consider citing the following paper:

@misc{qiang2023mstformer,
      title={MSTFormer: Motion Inspired Spatial-temporal Transformer with Dynamic-aware Attention for long-term Vessel Trajectory Prediction}, 
      author={Huimin Qiang and Zhiyuan Guo and Shiyuan Xie and Xiaodong Peng},
      year={2023},
      eprint={2303.11540},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

If you have any questions, feel free to contact Huimin Qiang through Email ([email protected]) or Github issues. Pull requests are highly welcomed!

Thanks to NOAA for providing the raw data (ttps://coast.noaa.gov/htdata/CMSP/AISDataHandler/2021/), and thanks to everyone for their interest in this work!