# TrAISformer

**Repository Path**: lyonardo/TrAISformer

## Basic Information

- **Project Name**: TrAISformer
- **Description**:  AIS船舶轨迹预测论文的Pytorch实现代码《A Transformer Network with Sparse Augmented Data Representation and Cross Entropy Loss for AIS-based Vessel Trajectory Prediction》
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 1
- **Created**: 2024-07-13
- **Last Updated**: 2026-04-24

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# TrAISformer

Pytorch implementation of TrAISformer---A generative transformer for AIS trajectory prediction (https://arxiv.org/abs/2109.03958).

The transformer part is adapted from: https://github.com/karpathy/minGPT

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<p align="center">
  <img width="600" height="450" src="./figures/t18_3.png">
</p>


#### Requirements: 
See requirements.yml

### Datasets:

The data used in this paper are provided by the [Danish Maritime Authority (DMA)](https://dma.dk/safety-at-sea/navigational-information/ais-data). 
Please refer to [the paper](https://arxiv.org/abs/2109.03958) for the details of the pre-processing step. The code is available here: https://github.com/CIA-Oceanix/GeoTrackNet/blob/master/data/csv2pkl.py

A processed dataset can be found in `./data/ct_dma/`
(the format is `[lat, log, sog, cog, unix_timestamp, mmsi]`).

### Run

Run `trAISformer.py` to train and evaluate the model.
(Please note that the values given by the code are in km, while the values presented in the paper were converted to nautical mile.)


### License

See `LICENSE`

### Contact
For any questions, please open an issue and assign it to @dnguyengithub.