Graph Spectral Regularized Tensor Completion for Traffic Data Imputation

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.

Automated summary

This paper presents a graph spectral regularized tensor completion algorithm based on graph Fourier transform and graph-tensor singular value decompositions to address the issue of incomplete traffic data in ITS caused by sensor malfunctions and communication faults. By introducing temporal regularized constraints, the recovery accuracy is significantly improved. Experimental results demonstrate the superiority of the proposed algorithm under different missing patterns.