Spatiotemporal traffic data imputation via tensorial weighted Schatten-p norm minimization

• Main Authors: Hu, Y. (Author), Wang, S. (Author), Yin, B. (Author), Zhang, Y. (Author), Zhao, Y. (Author)
• Format: Article
• Language: English
• Published: John Wiley and Sons Inc 2022
• Subjects: Completion methods; Data imputation; Intelligent systems; Matrix forms; P-norm minimization; Property; Rank structure; Regularization terms; Selection scheme; Singular values; Tensors; Traffic data
• Online Access: View Fulltext in Publisher

 Spatiotemporal traffic data exhibit multi-granular low-rank structure due to their periodicity among different timelines. Traditional low rank data completion methods fail to characterize such properties and produce unsatisfactory results for data imputation. In this paper, a tensorial weighted Schatten-p norm minimization (TWSN) is proposed for spatiotemporal traffic data imputation. TWSN consists of an approximation term and a low-rank regularization term over the recovered tensor data, where the latter is a combination of the weighted Schatten-p norm in the matrix form of each mode of the tensor. For each mode, TWSN utilizes a selection scheme of the mode-wise weights to capture different properties of singular values of each mode of the tensor. Overall, TWSN not only plays a balancing role between the rank function and the nuclear norm, but also captures the anisotropic correlation of singular values of each mode of the tensor. TWSN is evaluated on four real-world datasets with different ping frequencies (2, 5, 10 min) and its performance is compared with several state-of-the-art methods. The experimental results show that TWSN outperforms other methods under various data missing scenarios. © 2022 The Authors. IET Intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.