DMSTG: Dynamic Multiview Spatio-Temporal Networks for Traffic Forecasting

doi:10.1109/TMC.2023.3328038

	DMSTG: Dynamic Multiview Spatio-Temporal Networks for Traffic Forecasting
	Diao, Zulong 1,2; Wang, Xin 3; Zhang, Dafang 4; Xie, Gaogang 5; Chen, Jianguo 6; Pei, Changhua 5; Meng, Xuying 1,2; Xie, Kun 4; Zhang, Guangxing 1
	2024-06-01
发表期刊	IEEE TRANSACTIONS ON MOBILE COMPUTING
ISSN	1536-1233
卷号	23 期号:6 页码:6865-6880
摘要	Traffic sensor networks are widely applied in smart cities to monitor traffic in real-time. Exploiting such data to forecast future traffic conditions has the potential to enhance the decision-making capabilities of intelligent transportation systems, which attracts widespread attention from both industries and academia. Among them, network-wide prediction based on graph convolutional neural networks(GCN) has become mainstream. It models the spatial dependencies of sensors in a graph with a pre-defined Laplacian matrix. However, understanding spatio-temporal traffic patterns is quite challenging as there is a huge difference in terms of traffic patterns during different periods or in different regions. In addition, the actual data collected can be polluted due to unavoidable data loss from severe communication conditions or sensor failures. Considering these issues, we propose a novel dynamic multiview spatial-temporal prediction framework which takes into consideration various factors, including local/global, short/long term spatio-temporal dependencies and their dynamic changes. We creatively design two different modules to comprehensively perceive the changes in traffic patterns. We first propose a dynamic learning module based on our theoretical derivation to estimate the Laplacian matrix of the graph for GCN timely. We also design a self-attention based module to dynamically assign a weight to each part in traffic data. The spatio-temporal features from multiple views are deeply fused by a feature fusion module. The forecasting performance is evaluated with 5 real-time traffic datasets. Experiment results demonstrate that our framework can consistently outperform the state-of-the-art baselines and be more robust under noisy environments.
关键词	Forecasting Convolutional neural networks Predictive models Roads Real-time systems Feature extraction Tensors Traffic forecasting dynamic spatial-temporal graph networks traffic sensor networks smart city services
DOI	10.1109/TMC.2023.3328038
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Telecommunications
WOS记录号	WOS:001216462000009
出版者	IEEE COMPUTER SOC
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38959
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Xie, Gaogang
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Dept Network Technol, Res Ctr, Beijing 100864, Peoples R China 2.Purple Mt Lab, Nanjing 210000, Peoples R China 3.SUNY Stony Brook, Dept Elect & Comp Engn, Stony Brook, NY 11794 USA 4.Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410006, Peoples R China 5.Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100864, Peoples R China 6.Sun Yat Sen Univ, Sch Software Engn, Zhuhai 519082, Peoples R China
推荐引用方式 GB/T 7714	Diao, Zulong,Wang, Xin,Zhang, Dafang,et al. DMSTG: Dynamic Multiview Spatio-Temporal Networks for Traffic Forecasting[J]. IEEE TRANSACTIONS ON MOBILE COMPUTING,2024,23(6):6865-6880.
APA	Diao, Zulong.,Wang, Xin.,Zhang, Dafang.,Xie, Gaogang.,Chen, Jianguo.,...&Zhang, Guangxing.(2024).DMSTG: Dynamic Multiview Spatio-Temporal Networks for Traffic Forecasting.IEEE TRANSACTIONS ON MOBILE COMPUTING,23(6),6865-6880.
MLA	Diao, Zulong,et al."DMSTG: Dynamic Multiview Spatio-Temporal Networks for Traffic Forecasting".IEEE TRANSACTIONS ON MOBILE COMPUTING 23.6(2024):6865-6880.