Attention-based spatial–temporal adaptive dual-graph convolutional network for traffic flow forecasting

Accurate traffic flow forecasting (TFF) is a prerequisite for urban traffic control and guidance, which has become the key to avoiding traffic congestion and improving traffic management in intelligent transportation systems. To precisely characterize the spatial structure of road networks and disco...

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Veröffentlicht in:	Neural computing & applications Jg. 35; H. 23; S. 17217 - 17231
Hauptverfasser:	Xia, Dawen, Shen, Bingqi, Geng, Jian, Hu, Yang, Li, Yantao, Li, Huaqing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.08.2023 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Artificial neural networks Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Convolution Data Mining and Knowledge Discovery Forecasting Graphs Image Processing and Computer Vision Intelligent transportation systems Mathematical models Modules Nodes Original Article Probability and Statistics in Computer Science Roads & highways Similarity Traffic congestion Traffic control Traffic flow Traffic information Traffic management Transportation networks Big data analytics Traffic flow forecasting Graph convolutional network Adaptive graph modeling FastDTW
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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