Spatio-Temporal Traffic Data Tensor Restoration Method Based on Direction Weighting and P-Shrinkage Norm

Abstract

Due to the influence of data collection methods and external complex factors, missing traffic data is inevitable. However, complete traffic information is a necessary input for route planning and forecasting tasks. To reduce the impact of missing data problems, this paper uses the low-rank tensor completion framework based on T-SVD to complete the missing spatio-temporal traffic data, the aim is to recover a low-rank tensor from a tensor with partial observation terms, and the WLRTC-P model is proposed. We use the idea of direction weighting to solve the dependence of the original model on the data input direction, extract each direction correlation information of the tensor spatio-temporal traffic data, and use the p-shrinkage norm to replace the tensor average rank minimization problem, and the study shows that the p-shrinkage norm is tighter than the tensor nuclear norm and, finally, uses the alternating direction method of multipliers to solve this model. Experiments on two publicly available spatio-temporal traffic datasets verified the conjecture of data input direction’s influence on the completion accuracy, and compared with the existing classical model methods, WLRTC-P has high precision and generalization ability.