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.
- 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
The traffic data file for Los Angeles is available here, and should be
put into the data/
folder.
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
python dcrnn_seq2seq_train.py --config_filename=data/model/dcrnn_config.json
python run_demo.py
The generated prediction of DCRNN is in data/results/dcrnn_predictions_[1-12].h5
.
More details are being added ...
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}
}