A Higher-Order Motif-Based Spatiotemporal Graph Imputation Approach for Transportation Networks

Table 1

The limitations of these existing methods.

Category	Limitation
Predictive methods	These methods fail to consider the case of continuous data missing.
Interpolation methods	They focus on the average calculation without considering data global attributes.
Statistical methods	The accuracy of these methods degrades owing to the unknown values of data distribution.
Adversarial learning	Most methods ignore global spatial dependencies in traffic network.
Graph-based methods	They only capture global spatial information from neighbouring data.