Dynamic Spatial-Temporal Imputation Network With Missing Features for Traffic Data Imputation

Missing traffic data caused by sensor failures or communication errors significantly hinders the efficiency of downstream tasks in Intelligent Transportation Systems (ITS), such as the critical functions of traffic monitoring and decision-making. Since missing data contains important information, it is essential to extract dynamic spatial-temporal correlations in traffic processes by incorporating these missing features. Motivated by these concerns, a novel Dynamic Spatial-Temporal Imputation Network with Missing Features (DSTMIN) is proposed to accurately impute traffic data. DSTMIN comprises an embedding layer, a Mask Attention module (MA), and a Fusion Graph Convolution module (FGC). Specifically, an embedding layer is designed to accurately represent the distribution of missing data, thereby capturing both temporal features and missing features. Furthermore, in order to effectively capture the temporal correlations, MA integrates the missing features to emphasize the significance of observed data and reduce the adverse effects caused using incomplete data. To capture spatial correlations, FGC constructs the spatial graphs and model dynamic spatial correlations from traffic subsequences and the missing-data graph in the presence of missing features. The proposed DSTMIN is adequately evaluated to demonstrate its superior performance on two datasets, which achieves a remarkable 20% reduction in imputation error compared to state-of-the-art methods.