dcrnn's Introduction

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.

Requirements

hyperopt>=0.1
scipy>=0.19.0
numpy>=1.12.1
pandas==0.19.2
tensorflow>=1.3.0
peewee>=2.8.8
python 2.7

Dependency can be installed using the following command:

pip install -r requirements.txt

Traffic Data

The traffic data file for Los Angeles is available here, and should be put into the data/ folder.

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 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

Train the Model

python dcrnn_seq2seq_train.py --config_filename=data/model/dcrnn_config.json

Run the Pre-trained Model

python run_demo.py

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

More details are being added ...

Citation

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

@article{li2017dcrnn_traffic,
  title={Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting},
  author={Li, Yaguang and Yu, Rose and Shahabi, Cyrus and Liu, Yan},
  journal={arXiv preprint arXiv:1707.01926},
  year={2017}
}

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