This repository contains the source code of the following research paper:

• C.Y. Tian, W.K.V. Chan, Spatial-Temporal Attention Wavenet: a deep learning framework for traffic prediction considering spatial-temporal dependencies. IET Intell Transp Syst.2021;1–13. https://doi.org/10.1049/itr2.12044

Please cite

@article{https://doi.org/10.1049/itr2.12044,
author = {Tian, Chenyu and Chan, Wai Kin (Victor)},
title = {Spatial-temporal attention wavenet: A deep learning framework for traffic prediction considering spatial-temporal dependencies},
journal = {IET Intelligent Transport Systems},
volume = {15},
number = {4},
pages = {549-561},
doi = {https://doi.org/10.1049/itr2.12044},
url = {https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/itr2.12044},
year = {2021}
}

Traffic prediction on road networks is highly challenging due to the complexity of traffic systems and is a crucial task in successful intelligent traffic system applications. Existing approaches mostly capture the static spatial dependency relying on the prior knowledge of the graph structure. However, the spatial dependency can be dynamic, and sometimes the physical structure may not reflect the genuine relationship between roads. To better capture the complex spatial-temporal dependencies and forecast traffic conditions on road networks, we propose a multi-step prediction model named Spatial-Temporal Attention Wavenet (STAWnet). Temporal convolution is applied to handle long time sequences, and the dynamic spatial dependencies between different nodes can be captured using the self-attention network. Different from existing models, STAWnet does not need prior knowledge of the graph by developing a self-learned node embedding. These components are integrated into an end-to-end framework. The experimental results on three public traffic prediction datasets (METR-LA, PEMS-BAY, and PEMS07) demonstrate effectiveness. In particular, in the 1 hour ahead prediction, STAWnet outperforms state-of-the-art methods with no prior knowledge of the network.

Download METR-LA and PEMS-BAY data from Google Drive or Baidu Yun links provided by DCRNN. Process raw data

# Create data directories
mkdir -p data/{METR-LA,PEMS-BAY}

# METR-LA
python generate_training_data.py --output_dir=data/METR-LA --traffic_df_filename=data/metr-la.h5

# PEMS-BAY
python generate_training_data.py --output_dir=data/PEMS-BAY --traffic_df_filename=data/pems-bay.h5

python train.py

This research is funded by the Shenzhen Science and Technology Innovation Commission (Grant No. JCYJ20210324135011030, WDZC20200818121348001 and KCXFZ202002011010487), the National Natural Science Foundation of China (Grant No. 71971127), Guangdong Pearl River Plan (2019QN01X890), and the Hylink Digital Solutions Co., Ltd. (120500002).