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Attentive Traffic Flow Machines

This is a PyTorch implementation of Attentive Traffic Flow Machines (ATFM). ATFM is a a unified neural network which can effectively learn the spatial-temporal feature representations of crowd flow with an attention mechanism.

If you use this code for your research, please cite our papers (Conference Version and Journal Version):

@inproceedings{liu2018attentive,
  title={Attentive Crowd Flow Machines},
  author={Liu, Lingbo and Zhang, Ruimao and Peng, Jiefeng and Li, Guanbin and Du, Bowen and Lin, Liang},
  booktitle={2018 ACM Multimedia Conference on Multimedia Conference},
  pages={1553--1561},
  year={2018},
  organization={ACM}
}
@article{liu20120dynamic,
  title={Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction},
  author={Liu, Lingbo and Zhen, Jiajie and Li, Guanbin and Zhan, Geng and He, Zhaocheng and Du, Bowen and Lin, Liang},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  year={2020}
}

Requirements

  • torch==0.4.1

Preprocessing

For Crowd Flow Prediction: download TaxiBJ / BikeNYC and put them into folder data/TaxiBJ and data/BikeNYC.

For Citywide Passenger Demand Prediction (CPDP): the dataset of CPDP has been in folder data/TaxiNYC.

Model Training

# TaxiBJ
python run_taxibj.py

# BikeNYC
python run_bikenyc.py

# TaxiNYC
python run_taxinyc.py

Testing

# TaxiBJ
python test_taxibj.py

# BikeNYC
python test_bikenyc.py

# TaxiNYC
python test_taxinyc.py

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