TL-DCRN, a new transfer learning approach for DCRNN, where a single model trained on a highway network can be used to forecast traffic on unseen highway networks. Given a traffic network with a large amount of traffic data, our approach consists of partitioning the traffic network into a number of subgraphs and using a new training scheme that utilizes subgraphs to marginalize the location-specific information, thus learning the traffic as a function of network connectivity and temporal patterns alone. The resulting trained model can be used to forecast traffic on unseen networks. We demonstrate that TL-DCRN can learn from San Francisco regional traffic data and can forecast traffic on the Los Angeles region and vice versa.

• scipy>=0.19.0
• numpy>=1.12.1
• pandas>=0.19.2
• tensorflow>=1.13.1
• pyaml

LA and SFO dataset is 'here'

Download the traffic data files for entire California 'speed.h5', adjacency matrix 'adj_mat.pkl' and distance between sensors 'distances.csv', and keep in the data/input_files/ folder.

# Generate TFrecord dataset for 64 graph partitions

python hdf_to_tfrecord.py --config_filename=input_files/tf_record_config.yaml

The script will generate a data/TFrecords/ folder with the train, test, and validation dataset for 64 partitions

# Run the TL-DCRNN model

python dcrnn_train.py --config_filename=data/dcrnn_config_32transfer.yaml

The generated prediction of TL-DCRNN will be in data/results/

If you find this repository, e.g., the code and the datasets, useful in your research, please cite the following paper:

@inproceedings{mallick2021transfer,
  title={Transfer learning with graph neural networks for short-term highway traffic forecasting},
  author={Mallick, Tanwi and Balaprakash, Prasanna and Rask, Eric and Macfarlane, Jane},
  booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},
  pages={10367--10374},
  year={2021},
  organization={IEEE}
}