A Flexible Approach Based on Hybrid Global Low-Rankness and Smoothness Regularization With Nonlocal Structure for Traffic Data Imputation

Spatialtemporal traffic data are collected by a wide array of data collection devices deployed in intelligent transportation systems, which play crucial roles in data-driven intelligent transportation systems. However, hardware malfunctions or software system errors can lead to the inability to collect accurate data. Incomplete data in intelligent transportation systems can cause difficulties for subsequent applications, such as traffic flow anomaly detection and forecasting. Recently, low-rank tensor-based methods that characterize the global correlation of high-dimensional data have achieved superior performance. However, the majority of tensor-based approaches primarily account for the global correlation within the target traffic data, which is not sufficient to recover some challenging missing scenarios, such as fiber missing and slice missing that often occur in real situations. In this manuscript, we suggest a flexible method to explore the global correlation, local smoothness, and nonlocal self-similar redundancy in traffic data to improve recovery accuracy. Specifically, we utilize the multidimensional tensor nuclear norm to characterize the correlation structure, the multidimensional total variation to maintain smoothness detail, and the plug-and-play term to promote nonlocal self-similarity. The primary merits of this model lie in the fact that these priors characterize data features from different perspectives and complement each other. We develop an alternating direction method of multipliers to achieve efficient optimization for each variable. The proposed method is comprehensively evaluated through experiments across seven missing scenarios. Extensive experiments demonstrate that the suggested method with the aid of different priors outperforms many state-of-the-art approaches, especially structured missing scenarios.