Spatial-Temporal Graph Attention Networks:A Deep Learning Approach for Traffic Forecasting

This repository provides an open source implementation of the Spatio-Temporal GAT introduced by Zhang et al in "Spatial-Temporal Graph Attention Networks:A Deep Learning Approach for Traffic Forecasting" https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8903252

Running the code

We recommend creating and activating a virtual environment to run this repo. Use the following steps:

python3 -m venv env
source env/bin/activate
python3 -m pip install -r requirements.txt

To start training, simple run:

python3 main.py

We also provide .launch files in the .vscode folder for launching main through the python debugger.

Folder structure

Dataloading and preprocessed datasets are available in data_loader and dataset. The model is available in models/st_gat.py. Training functions are provided in models/trainer.py.

├── data_loader
│   ├── dataloader.py
│   └── __init__.py
├── dataset
│   ├── PeMSD7_V_228.csv
│   └── PeMSD7_W_228.csv
├── models
│   ├── __init__.py
│   ├── st_gat.py
│   └── trainer.py
├── runs
│   ├── model_final_200epochs.pt
│   └── model_final_60epochs.pt
├── utils
│   ├── __init__.py
|   └── math_utils.py
├── main.py
├── requirements.txt
└── README.md

Citation

This repository was based on that provided by Bing Yu*, Haoteng Yin*, Zhanxing Zhu. Spatio-temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), 2018

@inproceedings{yu2018spatio,
    title={Spatio-temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting},
    author={Yu, Bing and Yin, Haoteng and Zhu, Zhanxing},
    booktitle={Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI)},
    year={2018}
}

The model architecture came from C. Zhang, J. J. Q. Yu and Y. Liu, “Spatial-Temporal Graph Attention Networks: A Deep Learning Approach for Traffic Forecasting,” in IEEE Access, vol. 7, pp. 166246–166256, 2019, doi: 10.1109/ACCESS.2019.2953888