Missing Traffic Data Imputation based on Tensor Completion and Graph Network Fusion

Abstract

During traffic data acquisition, missing data often arise owing to equipment failures and network disruptions. Despite extensive research on traffic data imputation, two primary limitations persist: First, existing methods struggle to fully integrate the spatiotemporal correlations and low-rank structures inherent in traffic data. Second, current research has mostly focused on missing completely at random (MCAR), with limited attention on other missing data patterns. We propose an innovative method, tensor completion and graph network fusion (TCGNF), to address these challenges for missing traffic data imputation. This method initially utilizes tensor completion for the preliminary imputation of missing data. Subsequently, it constructs the road network by leveraging the Pearson correlation coefficient from historical road data and the physical distances between detectors. The method then uses graph sampling and aggregation (GraphSAGE) to extract spatiotemporal data features from the two road networks and fuse them. Finally, these features are trained in generative adversarial networks (GANs) for accurate data imputation. Extensive experiments were conducted on two publicly accessible traffic datasets to validate the efficacy of the TCGNF model. The outcomes of these experiments indicate that the TCGNF model demonstrates superior generalization capabilities, significantly outperforming other state-of-the-art data imputation models concerning overall performance.