PFNet: Large-scale Traffic Forecasting with Progressive Spatio-Temporal Fusion

This is a TensorFlow implementation of PFNet.

Important Notice: Email Update to chenwang99@buaa.edu.cn

Requirements

• TensorFlow-gpu==2.5.0
• numpy==1.19.5
• networkx==2.6.3
• einops==0.3.2

Data

The original data is under the folder original_data/, and the pre-processed dataset is under the folder input/.

Run

Train Details

---

Before training this model, make sure the three following settings are modified in run.py:

MODE = 'train'              # train or test
DATASET = 'LondonHW'        # LondonHW or ManchesterHW
DURATION = 60

where DURATION is the constant of the forecasting horizon, such as 15, 30, and 60. After that, you can run python run.py to start training PFNet. The result will be generated in the experiments folder, including the tensorboard-logs folder, best model parameters, the result of prediction, ground truth, and the running log file.

Test Details

---

Before testing the PFNet, you should modify the MODE variable as follows:

MODE = 'test'              # train or test

Besides, go to the bottom of the run.py file, and comment out the following code:

history = trainer.fit()

After uncommenting the test code trainer.evaluate(is_pretrained=True, model_path='./experiments/LondonHW_15/2022-05-12-22-33-53/best_model'), and changing the path LondonHW_15/2022-05-12-22-33-53 of the test model, you can run python run.py to perform the test operation.

Citation

Please cite the following paper, if you find the repository or the paper useful.

@article{wang2023pfnet,
    title={PFNet: Large-Scale Traffic Forecasting With Progressive Spatio-Temporal Fusion},
    author={Wang, Chen and Zuo, Kaizhong and Zhang, Shaokun and Lei, Hanwen and Hu, Peng and Shen, Zhangyi and Wang, Rui and Zhao, Peize},
    journal={IEEE Transactions on Intelligent Transportation Systems},
    year={2023},
    publisher={IEEE}
}