Spatial-temporal gated graph convolutional network: a new deep learning framework for long-term traffic speed forecasting

被引:3
|
作者
Zhang, Dongping [1 ]
Lan, Hao [1 ]
Ma, Zhennan [2 ]
Yang, Zhixiong [3 ]
Wu, Xin [4 ]
Huang, Xiaoling [5 ]
机构
[1] China Jiliang Univ, Coll Informat Engn, Hangzhou, Peoples R China
[2] Zhejiang Inst Commun, Books & Informat Ctr, Hangzhou, Peoples R China
[3] Jiaxing Sudoku Bridge Technol Co Ltd, Jiaxing, Peoples R China
[4] Zhejiang Univ Finance & Econ, China Acad Financial Res, Hangzhou, Peoples R China
[5] Zhejiang Univ Finance & Econ, Lib, Hangzhou, Peoples R China
关键词
Traffic speed forecasting; graph convolution operation; gated recurrent unit; self-attention block; MODEL; FLOW;
D O I
10.3233/JIFS-224285
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:10437 / 10450
页数:14
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