arXiv:2511.01267v1 Announce Type: new 
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.

تم تقديم نهج جديد لملء بيانات المرور، مع التركيز على أهمية جودة البيانات في أنظمة النقل الذكية. تستخدم هذه الطريقة خوارزميات متقدمة لاستعادة التنسور منخفض الرتبة لاستعادة بيانات المرور المتدهورة بشكل فعال، مما يعزز اتخاذ القرار في إدارة المرور.

Se ha introducido un nuevo enfoque para la imputación de datos de tráfico, centrándose en la importancia de la calidad de los datos en los Sistemas de Transporte Inteligente. Este método aprovecha algoritmos avanzados de recuperación de tensores de bajo rango para restaurar eficazmente los datos de tráfico degradados, mejorando la toma de decisiones en la gestión del tráfico.

Une nouvelle approche pour l'imputation des données de circulation a été introduite, mettant l'accent sur l'importance de la qualité des données dans les systèmes de transport intelligents. Cette méthode utilise des algorithmes avancés de récupération de tenseurs à faible rang pour restaurer efficacement les données de circulation dégradées, améliorant ainsi la prise de décision dans la gestion du trafic.

A new approach to traffic data imputation has been introduced, focusing on the importance of data quality in Intelligent Transportation Systems. This method leverages advanced low-rank tensor recovery algorithms to effectively restore degraded traffic data, enhancing decision-making in traffic management.

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

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

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