This is a PyTorch implementation of Decomposition Dynamic Graph Conolutional Recurrent Network for Traffic Forecasting, as described in our paper: Weng, Wenchao, Fan Jin ,Wu Huifeng and Hu Yujie ,Tian Hao, Zhu Fu, Wu Jia, A Decomposition Dynamic Graph Convolutional Recurrent Network for Traffic Forecasting,Pattern Recognition 2023.

• configs: training Configs and model configs for each dataset

• lib: contains self-defined modules for our work, such as data loading, data pre-process, normalization, and evaluate metrics.

• model: implementation of our model

• pre-trained: pre-trained model parameters

For convenience, we package these datasets used in our model in Google Drive.

Unzip the downloaded dataset files to the main file directory, the same directory as run.py.

Python 3.6.5, Pytorch 1.9.0, Numpy 1.16.3, argparse and configparser

python run.py --datasets {DATASET_NAME} --mode {MODE_NAME}

Replace {DATASET_NAME} with one of PEMSD3, PEMSD4, PEMSD7, PEMSD8, PEMSD7(L), PEMSD7(M)

such as python run.py --datasets PEMSD4

There are two options for {MODE_NAME} : train and test

Selecting train will retrain the model and save the trained model parameters and records in the experiment folder.

With test selected, run.py will import the trained model parameters from {DATASET_NAME}.pth in the 'pre-trained' folder.

If you find the paper useful, please cite as following:

@article{weng2023decomposition,
  title={A Decomposition Dynamic Graph Convolutional Recurrent Network for Traffic Forecasting},
  author={Weng, Wenchao and Fan, Jin and Wu, Huifeng and Hu, Yujie and Tian, Hao and Zhu, Fu and Wu, Jia},
  journal={Pattern Recognition},
  pages={109670},
  year={2023},
  publisher={Elsevier}
}