traffic speed data; time sequence data

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. Considering the complex distribution of missing data, it is essential to incorporate the missing features to extract dynamic spatial-temporal correlations in traffic processes. Motivated by these concerns, a novel Dynamic Spatial-Temporal Imputation Network with Missing Features (DSTMIN) is proposed to accurately impute traffic data.