# Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

![Diffusion Convolutional Recurrent Neural Network](figures/model_architecture.jpg "Model Architecture")

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](https://arxiv.org/abs/1707.01926), 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:
```bash
pip install -r requirements.txt
```

## Data Preparation
The traffic data files for Los Angeles and the Bay Area, i.e., `metr-la.h5` and `pems-bay.h5`, are available at [Google Drive](https://drive.google.com/open?id=10FOTa6HXPqX8Pf5WRoRwcFnW9BrNZEIX) or [Baidu Yun](hbttps://pan.baidu.com/s/14Yy9isAIZYdU__OYEQGa_g), and should be
put into the `data/` folder.
Besides, the locations of sensors Los Angeles are available at [data/sensor_graph/graph_sensor_locations.csv](https://github.com/liyaguang/DCRNN/blob/master/data/sensor_graph/graph_sensor_locations.csv).
```bash
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
```
The generated train/val/test dataset will be saved at `data/{METR-LA,PEMS-BAY}/{train,val,test}.npz`.


## Run the Pre-trained Model on METR-LA

```bash
python run_demo.py
```
The generated prediction of DCRNN of METR-LA is in `data/results/dcrnn_predictions_[1-12].h5`.


## Model Training
```bash
# METR-LA
python dcrnn_train.py --config_filename=data/model/dcrnn_la.yaml

# PEMS-BAY
python dcrnn_train.py --config_filename=data/model/dcrnn_bay.yaml
```
Each epoch takes about 5min or 10 min on a single GTX 1080 Ti for METR-LA or PEMS-BAY respectively.

## 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`).

```bash
python -m scripts.gen_adj_mx  --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}
}
```