Graph Spectral Regularized Tensor Completion for Traffic Data Imputation

Lei Deng

doi:10.1109/TITS.2021.3098637

Graph Spectral Regularized Tensor Completion for Traffic Data Imputation

2022-08-01

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DOI：10.1109/TITS.2021.3098637

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In intelligent transportation systems (ITS), incomplete traffic data due to sensor malfunctions and communication faults, seriously restricts the related applications of ITS. Recovering missing data from incomplete traffic data becomes an important issue for ITS. Existing works on traffic data imputation cannot achieve satisfactory accuracy due to inefficiently exploiting the underlying topological structure of the traffic data. In this paper, we model the topology of the road network as a graph and introduce graph Fourier transform (GFT) to process the traffic data. Then we utilize an algebraic framework termed as graph-tensor singular value decompositions (GT-SVD) to extract the hidden spatial information of traffic data. Furthermore, we propose a novel graph spectral regularized tensor completion algorithm based on GT-SVD and construct temporal regularized constraints to improve the recovery accuracy. The extensive experimental results on real traffic datasets demonstrate that the proposed algorithm outperforms the state-of-the-art methods under different missing patterns.

Keywords:

Tensors

Data models

Transportation

Data mining

Feature extraction

Matrix decomposition

Correlation

ITS

tensor completion

graph Fourier transform

traffic data imputation

Graph Spectral Regularized Tensor Completion for Traffic Data Imputation

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