  Due to detector malfunctions and communication failures, missing data is
ubiquitous during the collection of traffic data. Therefore, it is of vital
importance to impute the missing values to facilitate data analysis and
decision-making for Intelligent Transportation System (ITS). However, existing
imputation methods generally perform zero pre-filling techniques to initialize
missing values, introducing inevitable noises. Moreover, we observe prevalent
over-smoothing interpolations, falling short in revealing the intrinsic
spatio-temporal correlations of incomplete traffic data. To this end, we
propose Mask-Aware Graph imputation Network: MagiNet. Our method designs an
adaptive mask spatio-temporal encoder to learn the latent representations of
incomplete data, eliminating the reliance on pre-filling missing values.
Furthermore, we devise a spatio-temporal decoder that stacks multiple blocks to
capture the inherent spatial and temporal dependencies within incomplete
traffic data, alleviating over-smoothing imputation. Extensive experiments
demonstrate that our method outperforms state-of-the-art imputation methods on
five real-world traffic datasets, yielding an average improvement of 4.31% in
RMSE and 3.72% in MAPE.
