MVSTA: Multi-View Spatio-Temporal Correlation Awareness Network for Traffic Data Imputation

Road traffic data imputation is an essential component of Intelligent Transportation Systems (ITS). However, the spatio-temporal characteristics in traffic data are complex and diverse, and existing methods are unable to comprehensively extract them. This article, through in-depth observations of traffic system dynamics, innovatively taxonomizes the complex spatio-temporal dependencies into four key dimensions: Geographical Spatial Correlations (GSC), Latent Spatial Correlations (LSC), Intra-sensor Temporal Correlations (ITC), and Cross-sensor Temporal Correlations (CTC). Motivated by this taxonomy, we aim to construct a novel framework that holistically utilizes these spatio-temporal correlations to improve imputation accuracy. Subsequently, we propose a Multi-View Spatio-Temporal Correlation Awareness Network (MVSTA) for traffic data imputation, which incorporates two specifically designed modules: a Unified Spatial Correlation Awareness module (USCA) and a Collaborative Temporal Correlation Awareness module (CTCA). The USCA integrates GSC and LSC into a unified representation by jointly modeling physical proximity and data-driven dependencies. The CTCA collaboratively extracts ITC and CTC by capturing both local temporal patterns in individual sensors and interactive dynamics across different sensors. Extensive experiments on three real-world traffic datasets demonstrate that MVSTA significantly outperforms all baselines, validating the effectiveness of our proposed taxonomy and framework.