66 lines
2.1 KiB
Markdown
66 lines
2.1 KiB
Markdown
# Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
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[](https://beta.gemnasium.com/projects/github.com/liyaguang/DCRNN)
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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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- hyperopt>=0.1
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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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- python 2.7
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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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## Traffic Data
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The traffic data file for Los Angeles is available [here](https://drive.google.com/open?id=1tjf5aXCgUoimvADyxKqb-YUlxP8O46pb), and should be
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put into the `data/` folder.
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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 gen_adj_mx.py --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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## Train the Model
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```bash
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python dcrnn_seq2seq_train.py --config_filename=data/model/dcrnn_config.json
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```
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## Run the Pre-trained Model
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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 is in `data/results/dcrnn_predictions_[1-12].h5`.
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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{li2017dcrnn_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},
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year={2018}
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}
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```
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