78 lines
2.8 KiB
Markdown
78 lines
2.8 KiB
Markdown
# Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
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This is a TensorFlow implementation of Diffusion Convolutional Recurrent Neural Network in the following paper: \
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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.
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## Requirements
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- scipy>=0.19.0
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- numpy>=1.12.1
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- pandas>=0.19.2
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- tensorflow>=1.3.0
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- pyaml
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Dependency can be installed using the following command:
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```bash
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pip install -r requirements.txt
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```
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## Data Preparation
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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
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put into the `data/` folder.
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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).
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```bash
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mkdir -p data/{METR-LA,PEMS-BAY}
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# METR-LA
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python -m scripts.generate_training_data --output_dir=data/METR-LA --traffic_df_filename=data/metr-la.h5
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# PEMS-BAY
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python -m scripts.generate_training_data --output_dir=data/PEMS-BAY --traffic_df_filename=data/pems-bay.h5
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```
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The generated train/val/test dataset will be saved at `data/{METR-LA,PEMS-BAY}/{train,val,test}.npz`.
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## Run the Pre-trained Model on METR-LA
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```bash
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python run_demo.py
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```
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The generated prediction of DCRNN of METR-LA is in `data/results/dcrnn_predictions_[1-12].h5`.
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## Model Training
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```bash
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# METR-LA
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python dcrnn_train.py --config_filename=data/model/dcrnn_la.yaml
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# PEMS-BAY
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python dcrnn_train.py --config_filename=data/model/dcrnn_bay.yaml
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```
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Each epoch takes about 5min or 10 min on a single GTX 1080 Ti for METR-LA or PEMS-BAY respectively.
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## Graph Construction
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As the currently implementation is based on pre-calculated road network distances between sensors, it currently only
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supports sensor ids in Los Angeles (see `data/sensor_graph/sensor_info_201206.csv`).
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```bash
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python -m scripts.gen_adj_mx --sensor_ids_filename=data/sensor_graph/graph_sensor_ids.txt --normalized_k=0.1\
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--output_pkl_filename=data/sensor_graph/adj_mx.pkl
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```
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More details are being added ...
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## Citation
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If you find this repository useful in your research, please cite the following paper:
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```
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@inproceedings{li2018dcrnn_traffic,
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title={Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting},
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author={Li, Yaguang and Yu, Rose and Shahabi, Cyrus and Liu, Yan},
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booktitle={International Conference on Learning Representations (ICLR '18)},
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year={2018}
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}
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```
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