ST-LRTC: A Spatio-Temporal analysis empowered Low-Rank Tensor Completion method for missing traffic data imputation
Date:
In this study, we propose a novel Spatio-Temporal constrained Low-Rank Tensor Completion (ST-LRTC) method. The method utilizes a low-rank tensor to extract global features of traffic data, and a manifold embedding approach to depict the local geometric structure of spatiotemporal domains. Specifically, under the low-rank assumption, the method introduces temporal constraints based on the continuity and periodicity of traffic flow and a spatial constraint matrix reflecting the traffic flow transmission mechanism. We embed low-dimensional spatiotemporal constraint matrices into the low-rank tensor completion solving process to fully utilize the global features and local spatiotemporal characteristics of the traffic tensor.