mirror of https://github.com/czzhangheng/STDEN.git
57 lines
1.7 KiB
Markdown
57 lines
1.7 KiB
Markdown
# Spatio-Temporal Differential Equation Network
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This is a Pytroch implementation of Spatio-temporal Differential Equation Network (STDEN) for physics-guided traffic flow prediction, as described in our paper:
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Jiahao Ji, Jingyuan Wang, Zhe Jiang, Jiawei Jiang, and Hu Zhang, **[STDEN: Towards Physics-guided Neural Networks for Traffic Flow Prediction](https://www.bigscity.com/publications/stden-towards-physics-guided-neural-networks-for-traffic-flow-prediction/)**, AAAI 2022.
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The training framework of this project comes from [chnsh](https://github.com/chnsh/DCRNN_PyTorch). Thanks a lot :)
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## Requirement
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* scipy>=1.5.2
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* numpy>=1.19.1
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* pandas>=1.1.5
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* pyyaml>=5.3.1
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* pytorch>=1.7.1
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* future>=0.18.2
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* torchdiffeq>=0.2.0
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Dependency can be installed using the following command:
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```
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pip install -r requirements.txt
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```
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## Model Traning and Evaluation
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You can run the code by
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```bash
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# traning for dataset GT-221
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python stden_train.py --config_filename=configs/stden_gt.yaml
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# testing for dataset GT-221
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python stden_eval.py --config_filename=configs/stden_gt.yaml
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```
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The configuration file of all datasets are as follows:
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|dataset|config file|
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|GT-221|stden_gt.yaml|
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|WRS-393|stden_wrs.yaml|
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|ZGC-564|stden_zgc.yaml|
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Note the data is not public and I am not allowed to distribute it.
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## Cite
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If you find the paper usefule, please cite as following:
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```tex
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@inproceedings{ji2022stden,
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title={STDEN: Towards Physics-guided Neural Networks for Traffic Flow Prediction},
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author={Ji, Jiahao and Wang, Jingyuan and Jiang, Zhe and Jiang, Jiawei and Zhang, Hu},
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booktitle={2022 AAAI Conference on Artificial Intelligence (AAAI'22)},
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year={2022}
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}
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``` |