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
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.
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
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