An Integrated Intra-View and Inter-View Framework for Multiple Traffic Variable Data Simultaneous Recovery

Rapid advancements in traffic monitoring and sensing technologies have permitted the multiplex and democratized gathering of numerous traffic data (e.g. speed, volume), depicting identical traffic dynamics from various but complementary views. Incomplete values are ubiquitous in these data, which undermines their utility in subsequent applications. In order to manage and enhance traffic data quality, most existing methods recover single traffic variable data independently based on intra-view spatiotemporal correlations, while the inter-view complementarities are ignored. In this paper, we leverage both intra-view and inter-view correlations for multiple traffic variable data simultaneous recovery. To explore the inter-view relationships, a multi-view subspace consistency learning module is developed to bridge connections and activate complementarities among multi-view traffic data. Specifically, the latent subspace features of each data view are extracted and organized as a multi-view subspace tensor with low-rank regularization. The multi-view low-rank tensor captures the consistent subspace structure across multiple data views while reserving unique features within each data view. To characterize the intra-view dependencies, a tensor-based low-rank representation is presented to explore the distinct spatiotemporal patterns within single-view traffic data. For model validation, we additionally design a nonrandom missing pattern to simulate sensor permanent failure cases in practice. Extensive experiments implemented on three real-world multi-view traffic datasets demonstrate the effectiveness and robustness of the proposed model.