PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting

Understand

The complex spatial-temporal correlations in transportation networks make the traffic forecasting problem challenging.

• Since transportation system inherently possesses graph structures, many research efforts have been put with graph neural networks.
• Recently, constructing adaptive graphs to the data has shown promising results over the models relying on a single static graph structure.
• However, the graph adaptations are applied during the training phases and do not reflect the data used during the testing phases.