Traffic Data Recovery From Corrupted and Incomplete Observations via Spatial-Temporal TRPCA

Abstract

Traffic information can be used for real-time traffic management and long-term transportation planning to increase traffic efficiency and safety. However, data containing both missing and deviating values, can seriously affect the accuracy of traffic information, even leading to incorrect results in traffic data analysis. In this paper, we propose a novel tensor-based data recovery method named spatial-temporal tensor robust principal component analysis (ST-TRPCA) to recover traffic data from corrupted and incomplete observations. Specifically, we not only fully account for the spatial-temporal properties of traffic data to increase the data recovery accuracy, but also utilize tensor factorization and its low-dimensional representation to improve computational efficiency. The extensive experimental results performed on real-world traffic dataset under various scenarios show that ST-TRPCA outperforms other state-of-the-art methods in both missing data recovery and anomaly detection, especially when the traffic data are severely corrupted.