A Spatio-Temporal Online Robust Tensor Recovery Approach for Streaming Traffic Data Imputation

TL;DR

This paper introduces a novel online tensor recovery method for streaming traffic data that leverages spatio-temporal correlations, significantly improving accuracy and efficiency over traditional batch methods in real-world scenarios.

Contribution

It presents a new online robust tensor recovery algorithm that exploits both global and local data structures for scalable traffic data imputation.

Findings

Achieves high recovery accuracy in real-world traffic datasets.

Improves computational efficiency by up to three orders of magnitude.

Effectively handles missing and anomalous traffic data.

Abstract

Data quality is critical to Intelligent Transportation Systems (ITS), as complete and accurate traffic data underpin reliable decision-making in traffic control and management. Recent advances in low-rank tensor recovery algorithms have shown strong potential in capturing the inherent structure of high-dimensional traffic data and restoring degraded observations. However, traditional batch-based methods demand substantial computational and storage resources, which limits their scalability in the face of continuously expanding traffic data volumes. Moreover, recent online tensor recovery methods often suffer from severe performance degradation in complex real-world scenarios due to their insufficient exploitation of the intrinsic structural properties of traffic data. To address these challenges, we reformulate the traffic data recovery problem within a streaming framework, and propose a novel online robust tensor recovery algorithm that simultaneously leverages both the global spatio-temporal correlations and local consistency of traffic data, achieving high recovery accuracy and significantly improved computational efficiency in large-scale scenarios. Our method is capable of simultaneously handling missing and anomalous values in traffic data, and demonstrates strong adaptability across diverse missing patterns. Experimental results on three real-world traffic datasets demonstrate that the proposed approach achieves high recovery accuracy while significantly improving computational efficiency by up to three orders of magnitude compared to state-of-the-art batch-based methods. These findings highlight the potential of the proposed approach as a scalable and effective solution for traffic data quality enhancement in ITS.