Implementing the A3T-GCN architecture
In this section, we will train an Attention Temporal Graph Convolutional Network (A3T-GCN), designed for traffic forecasting. This architecture allows us to consider complex spatial and temporal dependencies:
- Spatial dependencies refer to the fact that the traffic condition of a location can be influenced by the traffic condition of nearby locations. For example, traffic jams often spread to neighboring roads.
- Temporal dependencies refer to the fact that the traffic condition of a location at a time can be influenced by the traffic condition of the same location at previous times. For example, if a road is congested during the morning peak, it is likely to remain congested until the evening peak.
A3T-GCN is an improvement over the temporal GCN (TGCN) architecture. The TGCN is a combination of a GCN and GRU that produces hidden vectors from each input time series. The combination of these two layers captures spatial and temporal...
