A Novel Spatiotemporal Data Low-Rank Imputation Approach for Traffic Sensor Network

Abstract

The Internet of Things (IoT) has enormous potential to transform the transport industry by improving passenger experiences, safety, and efficiency. However, the collected spatiotemporal data by traffic sensor network often suffer from missing values (MVs), which affect the overall performance of the system. As a result, accurate recovery of MVs is essential for the successful application of IoT in transportation. In this article, we propose a novel MVs imputation model by integrating low-rank tensor completion (LRTC) and sparse self-representation into a unified framework. In doing so, the global multidimensional correlation, as well as sample self-similarity, can be well leveraged for imputation. In order to solve the proposed model, an elaborate solution algorithm is developed, following the principle of alternating direction method of multipliers (ADMMs). Importantly, each step in ADMM can be implemented efficiently by analyzing the problem structure. Moreover, in order to select proper parameters for the model, an improved harmony search heuristic algorithm based on dual harmonies generation strategy is developed, thus sufficiently considering the information contained in current harmony memory. The experiments on two real-world traffic data are carried out to evaluate the proposed approach. The results verify that in comparison with the classic matrix/tensor completion and other competing algorithms, our method significantly improves the imputation performance.