This is the PyTorch implementation of the paper:
Q. Wang, H. Jiang, M. Qiu, Y. Liu and D. Ye, "TGAE: Temporal Graph Autoencoder for Travel Forecasting," in IEEE Transactions on Intelligent Transportation Systems, 2023, doi: 10.1109/TITS.2022.3202089.
- python==3.8.8
- networkx
- numpy
- pandas
- sklearn
- torch==1.9.0
- torch-cluster==1.5.9
- torch-scatter==2.0.9
- torch-sparse==0.6.12
- torch-spline-conv==1.2.1
- torchvision==0.10.0
- torch-geometric==2.0.4
The pre-processed data is under the folder data.
- Specify the arguments in the
train.py. - Run the code by
python train.py.
Please cite the following paper, if you find the repository or the paper useful.
@ARTICLE{9889163,
author={Wang, Qiang and Jiang, Hao and Qiu, Meikang and Liu, Yifeng and Ye, Dongsheng},
journal={IEEE Transactions on Intelligent Transportation Systems},
title={TGAE: Temporal Graph Autoencoder for Travel Forecasting},
year={2023},
volume={24},
number={8},
pages={8529-8541},
doi={10.1109/TITS.2022.3202089}}