96 lines
3.9 KiB
Markdown
96 lines
3.9 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 (METR-LA) and the Bay Area (PEMS-BAY), i.e., `metr-la.h5` and `pems-bay.h5`, are available at [Google Drive](https://drive.google.com/open?id=10FOTa6HXPqX8Pf5WRoRwcFnW9BrNZEIX) or [Baidu Yun](https://pan.baidu.com/s/14Yy9isAIZYdU__OYEQGa_g), and should be
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put into the `data/` folder.
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The `*.h5` files store the data in `panads.DataFrame` using the `HDF5` file format. Here is an example:
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| | sensor_0 | sensor_1 | sensor_2 | sensor_n |
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|:-------------------:|:--------:|:--------:|:--------:|:--------:|
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| 2018/01/01 00:00:00 | 60.0 | 65.0 | 70.0 | ... |
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| 2018/01/01 00:05:00 | 61.0 | 64.0 | 65.0 | ... |
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| 2018/01/01 00:10:00 | 63.0 | 65.0 | 60.0 | ... |
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| ... | ... | ... | ... | ... |
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Here is an article about [Using HDF5 with Python](https://medium.com/@jerilkuriakose/using-hdf5-with-python-6c5242d08773).
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Run the following commands to generate train/test/val dataset at `data/{METR-LA,PEMS-BAY}/{train,val,test}.npz`.
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```bash
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# Create data directories
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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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## 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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Besides, the locations of sensors in Los Angeles, i.e., METR-LA, 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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## Run the Pre-trained Model on METR-LA
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```bash
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# METR-LA
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python run_demo.py --config_filename=data/model/pretrained/METR-LA/config.yaml
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# PEMS-BAY
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python run_demo.py --config_filename=data/model/pretrained/PEMS-BAY/config.yaml
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```
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The generated prediction of DCRNN is in `data/results/dcrnn_predictions`.
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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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There is a chance that the training loss will explode, the temporary workaround is to restart from the last saved model before the explosion, or to decrease the learning rate earlier in the learning rate schedule.
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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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