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$S^2TAT$

Synchronous Spatio-Temporal Graph Transformer: A New Framework for Traffic Data Prediction

[Paper]

Installation

Tensorflow > 2.0

This repo was tested with Ubuntu 18.04.1 LTS, Python 3.8, tensorflow 2.5.0, and CUDA 11. But it should be runnable with recent tensoflow versions > 2.0

Data Preparation

The dataset is PEMS data for traffic prediction. Download and extract them under S2TAT/data, and make them look like this:

S2TAT/
  ├──data/
    ├──PEMS03/
    ├──PEMS04/
    ├──PEMS07/
    ├──PeMSD7L/
    └──PeMSD7M/
  ├──main.py
  ├──Model.py
  ├──Layers.py
  ├──utils.py
  └── ...

Usage

cd S2TAT
python main.py --config $config_path$

Results

The results of our $S^2TAT$ is tabulated as below

Dataset MAE MAPE(%) MSE #Parameters(M)
PEMS03 15.12 15.38 25.98 3.2
PEMS04 19.08 12.58 30.79 2.9
PEMS07 21.06 8.72 34.02 6.8
PEMS08 15.41 9.85 24.36 2.0
PeMSD7M 2.67 6.61 5.41 2.4
PeMSD7L 2.85 7.25 5.83 7.7

For any questions, please contact Jiahui Chen ([email protected]).

Citation

If you find this repo useful for your research, please consider citing the paper

@article{wang2022synchronous,
  title={Synchronous Spatiotemporal Graph Transformer: A New Framework for Traffic Data Prediction},
  author={Wang, Tian and Chen, Jiahui and L{\"u}, Jinhu and Liu, Kexin and Zhu, Aichun and Snoussi, Hichem and Zhang, Baochang},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2022},
  publisher={IEEE}
}