Integrated platform for urban intelligence tasks including traffic and demand prediction.
We report MAE / RMSE in pems-bay dataset (12 steps / 1 hour).
| Model | MAE | RMSE |
|---|---|---|
| DCRNN | 0.92 | 1.58 |
| GMAN | 1.99 | 3.87 |
| WaveNet | 4.70 | 7.53 |
We report MAE / RMSE in PeMS dataset (9 steps).
| Model | MAE | RMSE |
|---|---|---|
| STGCN | 18.30 | 18.92 |
| ASTGCN | 2.94 | 5.50 |
| MSTGCN | 2.94 | 5.52 |
- pems-bay, metr-la: Download h5 files from Google Drive and place in datasets directory.
- PeMSD7: Download files from STGCN Github and place in datasets directory.
- PEMS: Download files from ASTGNN Github and place in datasets directory.
conda create -n $ENV_NAME$ python=3.7
conda activate $ENV_NAME$
# CUDA 11.3
pip install torch==1.11.0+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
# Or, CUDA 10.2
pip install torch==1.11.0+cu102 --extra-index-url https://download.pytorch.org/whl/cu102
pip install -r requirements.txt
# DCRNN
python train.py --model DCRNN --ddir ../datasets/ --dname pems-bay --device $DEVICE$ --num_pred 12
# GMAN
python train.py --model GMAN --ddir ../datasets/ --dname pems-bay --device $DEVICE$ --num_pred 12
# WaveNet
python train.py --model WaveNet --ddir ../datasets/ --dname pems-bay --device $DEVICE$ --num_pred 12
# STGCN
python train.py --model STGCN --ddir ../datasets/ --dname PEMSD --device $DEVICE$ --num_pred 9
# ASTGCN
python train.py --model ASTGCN --ddir ../datasets/ --dname PEMSD --device $DEVICE$ --num_pred 9
# MSTGCN
python train.py --model MSTGCN --ddir ../datasets/ --dname PEMSD --device $DEVICE$ --num_pred 9