Composite Nonconvex Low-Rank Tensor Completion With Joint Structural Regression for Traffic Sensor Networks Data Recovery
Xiaobo Chen, Kaiyuan Wang, Feng Zhao, Fuwen Deng, Qiaolin Ye
Abstract
Traffic sensor networks allow convenient collection of travel data that are of great significance for intelligent transportation systems (ITSs). However, the universality of missing data impedes the application of ITS and thus accurate missing data recovery is indispensable in practice. Typically, the global low-rankness and local spatiotemporal smoothness exist in underlying traffic tensor data. In light of this, this article proposes an improved low-rank tensor completion (LRTC) model by exploiting abundant structural information from incomplete tensors. Specifically, a logarithm power composite (LPC)-norm is first proposed as a nonconvex substitute of the rank function, leading to a flexible characterization of tensor multidimensional correlation. Then, a joint structural regression (JSR) model is presented to simultaneously leverage the intrinsic temporal continuity and profile similarity of traffic data. By doing so, we construct a novel nonconvex LRTC model by integrating the global low-rankness and fine-grained spatiotemporal structure that are complementary to each other. To solve the proposed model, following the optimization framework of the alternating direction method of multipliers (ADMMs), we develop an efficient iterative algorithm where each step can be solved in a closed form. Extensive experiments on four real-world traffic data are conducted to evaluate the effectiveness of the proposed approach. The results demonstrate that compared with other tensor completion methods, our model significantly improves the recovery performance.