Fourth-Order Dimension Preserved Tensor Completion With Temporal Constraint for Missing Traffic Data Imputation

In the intelligent transportation system, the collected traffic data are usually incomplete. The low-rank completion models are effective in processing missing traffic data (MTD) imputation. However, the existing low-rank completion models encounter the following challenges: 1) Incorporating spatio-temporal information of traffic data often leads to significant time overhead; 2) To reduce the time overhead, some models based on the low-rank completion only embed the temporal information and discard the spatial information, thereby resulting in low imputation accuracy; 3) Missing traffic data carries complex spatio-temporal information, requiring multi-faceted analysis to extract valuable insights. To address these issues, we propose an efficient fourth-order dimension preserved tensor completion (FDPTC) with temporal constraint model. It works based on our proposed fourth-order dimension preserved (FDP) tensor decomposition model to capture the spatio-temporal information of traffic data from a high-dimensional perspective by extending dimensions. Additionally, we embed a temporal constraint into FDP tensor decomposition model to ensure consistency of MTD and introduce a non-negative constraint to accelerate convergence speed. By constructing this model, we successfully avoid direct operations on the extra information matrix/tensor, thereby optimizing efficiency. Experimental results on four real traffic datasets demonstrate that our proposed model achieves significantly higher imputation accuracy at an affordable computational burden compared with state-of-the-art models.