Spatiotemporal Adaptive Gated Graph Convolution Network for Urban Traffic Flow Forecasting [paper]
- pytorch >= 1.4.0
- numpy >= 1.18.1
- scikit-learn >= 0.21.0
- pytorch geometric >= 1.4.1
- pyaml
- scipy
- tqdm
The data in paper can be download here: GAIA Open Dataset
Run the following command to generate semantic neighbor adjacency matrix.
# Achieve DTW distance matrix
python tools/DTW_embedding.py
# Set threshold to generate semantic neighbor adjacency matrix
python tools/DTW_matrix_analysis.py# Training process
python train.py --config_filename='config.yaml'
# Testing process
python test.py --config_filename='config.yaml'If you find this repository, e.g., the paper, code and the datasets, useful in your research, please cite the following paper:
@inproceedings{DBLP:conf/cikm/LuGJFZ20,
author = {Bin Lu and
Xiaoying Gan and
Haiming Jin and
Luoyi Fu and
Haisong Zhang},
title = {Spatiotemporal Adaptive Gated Graph Convolution Network for Urban
Traffic Flow Forecasting},
booktitle = {{CIKM} '20: The 29th {ACM} International Conference on Information
and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020},
pages = {1025--1034},
publisher = {{ACM}},
year = {2020}
}