Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

This is a TensorFlow implementation of Diffusion Convolutional Recurrent Neural Network in the following paper:
Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu, Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting, ICLR 2018.

Requirements

• scipy>=0.19.0
• numpy>=1.12.1
• pandas>=0.19.2
• tensorflow>=1.3.0
• pyaml

Dependency can be installed using the following command:

pip install -r requirements.txt

Data Preparation

The traffic data file for Los Angeles, i.e., df_highway_2012_4mon_sample.h5, is available here, and should be put into the data/METR-LA folder. Besides, the locations of sensors are available at data/sensor_graph/graph_sensor_locations.csv.

python -m scripts.generate_training_data --output_dir=data/METR-LA

The generated train/val/test dataset will be saved at data/METR-LA/{train,val,test}.npz.

Run the Pre-trained Model

python run_demo.py

The generated prediction of DCRNN is in data/results/dcrnn_predictions_[1-12].h5.

Model Training

python dcrnn_train.py --config_filename=data/model/dcrnn_config.yaml

Each epoch takes about 5min with a single GTX 1080 Ti.

Graph Construction

As the currently implementation is based on pre-calculated road network distances between sensors, it currently only supports sensor ids in Los Angeles (see data/sensor_graph/sensor_info_201206.csv).

python -m scripts.gen_adj_mx.py  --sensor_ids_filename=data/sensor_graph/graph_sensor_ids.txt --normalized_k=0.1\
    --output_pkl_filename=data/sensor_graph/adj_mx.pkl

More details are being added ...

Citation

If you find this repository useful in your research, please cite the following paper:

@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}
}