SFMGTL

This is the repository for the paper named ''Semantic-Fused Multi-Granularity Cross-City Traffic Prediction'' published in Transportation Research Part C. In this paper, we propose a Semantic-Fused Multi-Granularity Transfer Learning (SFMGTL) model to achieve knowledge transfer across cities with fused semantics at different granularities. In detail, we design a semantic fusion module to fuse various semantics while conserving static spatial dependencies via reconstruction losses. Then, a fused graph is constructed based on node features through graph structure learning. Afterwards, we implement hierarchical node clustering to generate graphs with different granularity. To extract feasible meta-knowledge, we further introduce common and private memories and obtain domain-invariant features via adversarial training. It is worth noting that our work jointly addresses semantic fusion and multi-granularity issues in transfer learning. We conduct extensive experiments on six real-world datasets to verify the effectiveness of our SFMGTL model by comparing it with other state-of-the-art baselines.

Model Training and Testing

Before training the model, you need to install the dependencies:

pip install -r requirements.txt

The dataset can be acquired in the data/dataset.zip, just upzip it for using. Both training and testing functions are in main.py file, and you can directly run it by:

python main.py --scity 'NY' --tcity 'DC' --datatype 'pickup' --batch_size 64 --seed 12 --train_days 7 --hidden_dim 32

Cite the paper as follows:

Elsevier Version Arxiv Version

@article{chen2024semantic,
  title={Semantic-fused multi-granularity cross-city traffic prediction},
  author={Chen, Kehua and Liang, Yuxuan and Han, Jindong and Feng, Siyuan and Zhu, Meixin and Yang, Hai},
  journal={Transportation Research Part C: Emerging Technologies},
  volume={162},
  pages={104604},
  year={2024},
  publisher={Elsevier}
}