SATORIS-N: Spectral Analysis based Traffic Observation Recovery via Informed Subspaces and Nuclear-norm minimization

2026-02-03

AI Summary

This work addresses the challenge of accurately reconstructing traffic density matrices under high missing rates—caused by communication disruptions, sensor occlusions, or sparse connected vehicles—by proposing a subspace-aware nuclear norm minimization approach. Leveraging the low-rank structure and singular vector subspace stability observed across temporally adjacent traffic data, the method explicitly incorporates prior subspace information into a convex optimization framework. This yields a semidefinite programming (SDP) model with subspace alignment constraints that guarantees global optimality in matrix recovery. Evaluated on real-world datasets from Beijing and Shanghai, the proposed method significantly outperforms established baselines including SoftImpute, IterativeSVD, conventional statistical models, and deep learning approaches, thereby enabling robust support for V2X applications such as cooperative perception and trajectory prediction.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Non-convex Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Traffic-density matrices from different days exhibit both low rank and stable correlations in their singular-vector subspaces. Leveraging this, we introduce SATORIS-N, a framework for imputing partially observed traffic-density by informed subspace priors from neighboring days. Our contribution is a subspace-aware semidefinite programming (SDP)} formulation of nuclear norm that explicitly informs the reconstruction with prior singular-subspace information. This convex formulation jointly enforces low rank and subspace alignment, providing a single global optimum and substantially improving accuracy under medium and high occlusion. We also study a lightweight implicit subspace-alignment} strategy in which matrices from consecutive days are concatenated to encourage alignment of spatial or temporal singular directions. Although this heuristic offers modest gains when missing rates are low, the explicit SDP approach is markedly more robust when large fractions of entries are missing. Across two real-world datasets (Beijing and Shanghai), SATORIS-N consistently outperforms standard matrix-completion methods such as SoftImpute, IterativeSVD, statistical, and even deep learning baselines at high occlusion levels. The framework generalizes to other spatiotemporal settings in which singular subspaces evolve slowly over time. In the context of intelligent vehicles and vehicle-to-everything (V2X) systems, accurate traffic-density reconstruction enables critical applications including cooperative perception, predictive routing, and vehicle-to-infrastructure (V2I) communication optimization. When infrastructure sensors or vehicle-reported observations are incomplete - due to communication dropouts, sensor occlusions, or sparse connected vehicle penetration-reliable imputation becomes essential for safe and efficient autonomous navigation.

Problem

• traffic-density reconstruction
• missing data imputation
• spatiotemporal matrix completion
• sensor occlusion
• V2X systems

Innovation

• subspace-aware nuclear norm
• semidefinite programming
• traffic density imputation
• low-rank matrix completion
• spatiotemporal subspace alignment