Gated Recurrent Unit Embedded with Dual Spatial Convolution for Long-Term Traffic Flow Prediction

被引:3
作者
Zhang, Qingyong [1 ]
Zhou, Lingfeng [1 ]
Su, Yixin [1 ]
Xia, Huiwen [1 ]
Xu, Bingrong [1 ]
机构
[1] Wuhan Univ Technol, Sch Automat, 122 Luoshi Rd, Wuhan 430070, Peoples R China
基金
中国国家自然科学基金;
关键词
traffic flow prediction; graph convolution network; gated mechanism; recurrent neural network; spatiotemporal dependence; NEURAL-NETWORKS; MODEL;
D O I
10.3390/ijgi12090366
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Considering the spatial and temporal correlation of traffic flow data is essential to improve the accuracy of traffic flow prediction. This paper proposes a traffic flow prediction model named Dual Spatial Convolution Gated Recurrent Unit (DSC-GRU). In particular, the GRU is embedded with the DSC unit to enable the model to synchronously capture the spatiotemporal dependence. When considering spatial correlation, current prediction models consider only nearest-neighbor spatial features and ignore or simply overlay global spatial features. The DSC unit models the adjacent spatial dependence by the traditional static graph and the global spatial dependence through a novel dependency graph, which is generated by calculating the correlation between nodes based on the correlation coefficient. More than that, the DSC unit quantifies the different contributions of the adjacent and global spatial correlation with a modified gated mechanism. Experimental results based on two real-world datasets show that the DSC-GRU model can effectively capture the spatiotemporal dependence of traffic data. The prediction precision is better than the baseline and state-of-the-art models.
引用
收藏
页数:22
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