wangqiang-codes / TGAE

TGAE: Temporal Graph Autoencoder for Travel Forecasting

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.

Requirements

• 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

Data

The pre-processed data is under the folder data.

Run

1. Specify the arguments in the train.py.
2. Run the code by python train.py.

Citation

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}}