Composite Nonconvex Low-Rank Tensor Completion With Joint Structural Regression for Traffic Sensor Networks Data Recovery

Traffic sensor networks allow convenient collection of travel data that are of great significance for intelligent transportation systems (ITSs). However, the universality of missing data impedes the application of ITS and thus accurate missing data recovery is indispensable in practice. Typically, t...

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Veröffentlicht in:	IEEE transactions on computational social systems 2024-10, Vol.11 (5), p.6882-6896
Hauptverfasser:	Chen, Xiaobo, Wang, Kaiyuan, Zhao, Feng, Deng, Fuwen, Ye, Qiaolin
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Computational modeling Correlation Data models Data recovery Intelligent transportation systems Iterative algorithms Missing data Nonconvex surrogate Probabilistic logic Regression models Smoothness spatial–temporal correlation Spatiotemporal data Spatiotemporal phenomena Structural analysis tensor completion Tensors traffic sensor networks Transportation networks
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