Affiliations: [a]
College of Information Engineering, China Jiliang University, Hangzhou, China
| [b]
Books and Information Center, Zhejiang Institute of Communications, Hangzhou, China
| [c]
Jiaxing Sudoku Bridge Technology Co., Ltd., Jiaxing, China
| [d]
China Academy of Financial Research, Zhejiang University of Finance and Economics, Hangzhou, China
| [e]
Library, Zhejiang University of Finance and Economics, Hangzhou, China
Correspondence:
[*]
Corresponding author. Xin Wu, Ph.D., Professor, China Academy of Financial Research, Zhejiang University of Finance and Economics, Hangzhou, China 310018. [email protected].
Abstract: The key to solving traffic congestion is the accurate traffic speed forecasting. However, this is difficult owing to the intricate spatial-temporal correlation of traffic networks. Most existing studies either ignore the correlations among distant sensors, or ignore the time-varying spatial features, resulting in the inability to extract accurate and reliable spatial-temporal features. To overcome these shortcomings, this study proposes a new deep learning framework named spatial-temporal gated graph convolutional network for long-term traffic speed forecasting. Firstly, a new spatial graph generation method is proposed, which uses the adjacency matrix to generate a global spatial graph with more comprehensive spatial features. Then, a new spatial-temporal gated recurrent unit is proposed to extract the comprehensive spatial-temporal features from traffic data by embedding a new graph convolution operation into gated recurrent unit. Finally, a new self-attention block is proposed to extract global features from the traffic data. The evaluation on two real-world traffic speed datasets demonstrates the proposed model can accurately forecast the long-term traffic speed, and outperforms the baseline models in most evaluation metrics.
