A Multi-source Based Coupled Tensors Completion Algorithm for Incomplete Traffic Data Imputation

Missing data is an inevitable and ubiquitous problem in the data-driven Intelligent Transportation System (ITS), which seriously affects the accuracy of urban traffic planning and management. Most existing traffic data processing methods often only exploit the characteristics of single source data. In this paper, we present a novel coupled tensors model by using multisource traffic data for missing data imputation, and propose a tensor completion algorithm based on a modified CMTF-WOPT(Coupled Matrix and Tensor Factorization-Weighted OP-Timization) algorithm to recover the missing traffic data. We also present extensive simulation results by using real world traffic datasets to evaluate the performance of the proposed algorithm. The simulation results show that the proposed coupled tensor completion algorithm makes a significant improvement on the recovery accuracy compared with existing tensor completion algorithms, especially under high missing rates.