traffic_prediction.bib

@article{yu20193d,
  title={3d graph convolutional networks with temporal graphs: A spatial information free framework for traffic forecasting},
  author={Yu, Bing and Li, Mengzhang and Zhang, Jiyong and Zhu, Zhanxing},
  journal={arXiv preprint arXiv:1903.00919},
  year={2019}
}
@article{bai2020adaptive,
  title={Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting},
  author={Bai, Lei and Yao, Lina and Li, Can and Wang, Xianzhi and Wang, Can},
  journal={arXiv preprint arXiv:2007.02842},
  year={2020}
}
@inproceedings{guo2019attention,
  title={Attention based spatial-temporal graph convolutional networks for traffic flow forecasting},
  author={Guo, Shengnan and Lin, Youfang and Feng, Ning and Song, Chao and Wan, Huaiyu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={33},
  pages={922--929},
  year={2019}
}
@article{li2017diffusion,
  title={Diffusion convolutional recurrent neural network: Data-driven traffic forecasting},
  author={Li, Yaguang and Yu, Rose and Shahabi, Cyrus and Liu, Yan},
  journal={arXiv preprint arXiv:1707.01926},
  year={2017}
}
@inproceedings{sen2019think,
  title={Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting},
  author={Sen, Rajat and Yu, Hsiang-Fu and Dhillon, Inderjit S},
  booktitle={Advances in Neural Information Processing Systems},
  pages={4837--4846},
  year={2019}
}
@inproceedings{zheng2020gman,
  title={Gman: A graph multi-attention network for traffic prediction},
  author={Zheng, Chuanpan and Fan, Xiaoliang and Wang, Cheng and Qi, Jianzhong},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={01},
  pages={1234--1241},
  year={2020}
}