Topology-Induced Low-Rank Tensor Representation for Spatio-Temporal Traffic Data Imputation

Spatio-temporal traffic data imputation is a fundamental component in intelligent transportation systems, which can significantly improve data quality and enhance the accuracy of downstream data mining tasks. Recently, low-rank tensor representation has shown great potential for spatio-temporal traffic data imputation. However, the low-rank assumption focuses on the global structure, neglecting the critical spatial topology and local temporal dependencies inherent in spatio-temporal data. To address these issues, we propose a topology-induced low-rank tensor representation (TILR), which can accurately capture the underlying low-rankness of the spatial multi-scale features induced by topology knowledge. Moreover, to exploit local temporal dependencies, we suggest a learnable convolutional regularization framework, which not only includes some classical convolution-based regularizers but also leads to the discovery of new convolutional regularizers. Equipped with the suggested TILR and convolutional regularizer, we build a unified low-rank tensor model harmonizing spatial topology and temporal dependencies for traffic data imputation, which is expected to deliver promising performance even under extreme and complex missing scenarios. To solve the proposed nonconvex model, we develop an efficient alternating direction method of multipliers (ADMM)-based algorithm and analyze its computational complexity. Extensive experiments demonstrate that the proposed model outperforms state-of-the-art baselines for various missing scenarios. These results reveal the critical synergy between topology-aware low-rank constraint and temporal dynamic modeling for spatio-temporal data imputation.