This is a PyTorch implementation of the paper: Towards Online Spatio-Temporal Data Prediction: A Knowledge Distillation Driven Continual Learning Approach (Storm)
The model is implemented using Python3 with dependencies specified in requirements.txt, and configuration.txt indicates the Conda version and Python version used.
pip install -r requirements.txt
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METR-LA ：The METR-LA dataset contains traffic data collected from 207 loop detectors on highways in Los Angeles County. The data spans from March 1, 2012, to June 30, 2012, with a sampling interval of 5 minutes.
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PEMS-BAY：The PEMS-BAY dataset consists of traffic data collected by 325 sensors in the California Bay Area. The data spans from January 1, 2017, to May 31, 2017, with a sampling interval of 5 minutes.
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PEMS04： This PEMS04 dataset includes traffic data from the PeMS system in California's District 4. It covers the period from January 1, 2018, to February 28, 2018, with a sampling interval of 5 minutes, collected from 307 sensors.
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PEMS08：The PEMS08 dataset comprises traffic data from the PeMS system in California's District 8. The data spans from July 1, 2016, to August 31, 2016, sampled every 5 minutes, and collected from 170 sensors.
[METR-LA & PEMS-BAY]：https://github.com/liyaguang/DCRNN
[PEMSD4 & PEMSD8]：https://github.com/Davidham3/ASTGCN
# Create data directories, For example
mkdir -p data/{PEMSD4}
Download PEMSD4 dataset then unzip in PEMSD4
- STGCN
cd STGCN
python STGCN_Main.py
- AGCRN
cd AGCRN
python AGCRN_Main.py
- MTGNN
cd MTGNN
python MTGNN_Main.py
- STAEformer
cd STAEformer
python STGCN_Main.py
cd model/
python train.py -d <dataset> -g <gpu_id>
Our research code refers to the following works:
[1] STG4Traffic：https://github.com/trainingl/STG4Traffic
[2] STAEformer https://github.com/XDZhelheim/STAEformer