Awesome

STPGCN

This is an implementation of Spatial-Temporal Position-Aware Graph Convolution Networks for Traffic Flow Forecasting (TITS).

Requirements

Mxnet version

• mxnet>=1.5.0
• easydict

Use nvcc -V to check the cuda version and install mxnet with the corresponding version. For example, use pip install mxnet-cu101 to install mxnet for cuda version 10.1.

Pytorch version

• torch>=2.0
• easydict

Thanks to Dr. Wen for the pytorch version.

Data

• PEMS: Refer to https://github.com/Davidham3/STSGCN
• Metro：Refer to https://github.com/yijizhao/MR-STN

Usage

• python main.py --rid=1 --seed=1 --L=3 --a=4 --b=2 --d=8 --data=PEMS08 --batch=32 --C=64 --workname=STPGCN-PEMS08

Citing

If our paper benefits to your research, please cite our paper using the bitex below:

@article{STPGCN,
    title={Spatial-Temporal Position-Aware Graph Convolution Networks for Traffic Flow Forecasting},
    author={Zhao, Yiji and Lin, Youfang and Wen, Haomin and Wei, Tonglong and Jin, Xiyuan and Wan, Huaiyu},
    journal={IEEE Transactions on Intelligent Transportation Systems},
    volume={24},
    number={8},
    pages={8650-8666},
    year={2023},
    publisher={IEEE}
}