Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework

This folder concludes the further revised version of pytorch implementation of our CMuST model.

Requirements

• python 3.8
• see requirements.txt

Dataset Sources

NYC dataset

Taxi Trip Records https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page

Demographic Data: https://data.cityofnewyork.us/City-Government/Demograp-hic-Statistics-By-Zip-Code/

Road Network: https://data.cityofnewyork.us/City-Government/NYC-Street-Centerline-CSCL-/

SIP dataset

Due to the privacy protocols between our department and SIP traffic administration offices, the statistics of traffic flows and speed values cannot be open source.

Chicago dataset

Taxi Trip Records https://data.cityofchicago.org/Transportation/Taxi-Trips-2013-2023-/wrvz-psew/about_data

Traffic Crashes - People https://data.cityofchicago.org/Transportation/Traffic-Crashes-People/u6pd-qa9d/about_data

Traffic Crashes - Crashes https://data.cityofchicago.org/Transportation/Traffic-Crashes-Crashes/85ca-t3if/about_data

Traffic Crashes - Vehicles https://data.cityofchicago.org/Transportation/Traffic-Crashes-Vehicles/68nd-jvt3/about_data

Data Preparation

This can be found in ./data/README.md.

Training Example

python main.py --dataset NYC --num_nodes 206 --tod_size 48 --gpu 0

python main.py --dataset CHI --num_nodes 220 --tod_size 48 --gpu 1

Citation

If you find our work useful in your research, please cite:

@inproceedings{yi2024get,
  title={Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework},
  author={Yi, Zhongchao and Zhou, Zhengyang and Huang, Qihe and Chen, Yanjiang and Yu, Liheng and Wang, Xu and Wang, Yang},
  booktitle={Thirty-eighth Conference on Neural Information Processing Systems},
  year={2024}
}

Contact

If you have any questions or suggestions, please feel free to contact:

• Zhongchao Yi (zhongchaoyi@mail.ustc.edu.cn)
• Zhengyang Zhou (zzy0929@ustc.edu.cn)

More Related Works

• ComS2T: A complementary spatiotemporal learning system for data-adaptive model evolution. arXiv Preprint.

• Maintaining the status quo: Capturing invariant relations for OOD spatiotemporal learning. KDD'23. [Code]

• LeRet: Language-Empowered Retentive Network for Time Series Forecasting. IJCAI'24. [Code]

Acknowledgement

We sincerely thanks the following GitHub repositories for providing valuable codebases and datasets:

https://github.com/liuxu77/LargeST

https://github.com/ACAT-SCUT/CycleNet

https://github.com/nnzhan/Graph-WaveNet

https://github.com/Zhang-Zijian/PromptST

https://github.com/GestaltCogTeam/STID

https://github.com/XDZhelheim/STAEformer

https://github.com/Echohhhhhh/GSNet

https://nn.labml.ai/diffusion/stable_diffusion/model/autoencoder.html