  Traffic forecasting has attracted widespread attention recently. In reality,
traffic data usually contains missing values due to sensor or communication
errors. The Spatio-temporal feature in traffic data brings more challenges for
processing such missing values, for which the classic techniques (e.g., data
imputations) are limited: 1) in temporal axis, the values can be randomly or
consecutively missing; 2) in spatial axis, the missing values can happen on one
single sensor or on multiple sensors simultaneously. Recent models powered by
Graph Neural Networks achieved satisfying performance on traffic forecasting
tasks. However, few of them are applicable to such a complex missing-value
context. To this end, we propose GCN-M, a Graph Convolutional Network model
with the ability to handle the complex missing values in the Spatio-temporal
context. Particularly, we jointly model the missing value processing and
traffic forecasting tasks, considering both local Spatio-temporal features and
global historical patterns in an attention-based memory network. We propose as
well a dynamic graph learning module based on the learned local-global
features. The experimental results on real-life datasets show the reliability
of our proposed method.
