This is a PyTorch implementation of Diffusion Convolutional Recurrent Neural Network in the following paper: Rui Li, Fan Zhang, Tong Li, Ning zhang, Tingting Zhang, https://drive.google.com/file/d/1VMDLjTeYjSwsY35IISFAR3njDV-ImfYB/view, TKDE 2022.

@inproceedings{li2018dcrnn_traffic, title={Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting}, author={Li, Yaguang and Yu, Rose and Shahabi, Cyrus and Liu, Yan}, booktitle={International Conference on Learning Representations (ICLR '18)}, year={2018} }

The traffic data files for Los Angeles (METR-LA) and the Bay Area (PEMS-BAY), i.e., metr-la.h5 and pems-bay.h5, are available at Google Drive or Baidu Yun, and should be put into the data/ folder. The *.h5 files store the data in panads.DataFrame using the HDF5 file format. Here is an example:

Here is an article about Using HDF5 with Python.

Run the following commands to generate train/test/val dataset at data/{METR-LA,PEMS-BAY}/{train,val,test}.npz.

# Create data directories
mkdir -p data/{METR-LA,PEMS-BAY}

# METR-LA
python -m scripts.generate_training_data --output_dir=data/METR-LA --traffic_df_filename=data/metr-la.h5

# PEMS-BAY
python -m scripts.generate_training_data --output_dir=data/PEMS-BAY --traffic_df_filename=data/pems-bay.h5

--dmgagru_cell.py: The implementation of DMGA-GRUcell. --dmgan_model.py: The encoder-decoder framework of DMGAN. --Transformer.py: The implementation of global-attention or local attention. --dmgan_supervisor.py: The arichteriture of the Network for training and testing.

# METR-LA
python train.py --config_filename=data/DMGAN_la.yaml

# PEMS-BAY
python train.py --config_filename=data/DMGAN_bay.yaml