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

arXiv:2202.08982v3 Announce Type: replace
Abstract: 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. Such shortcomings can …

abstract arxiv correlations cs.lg data forecasting graph graph neural networks graphs networks neural networks research results spatial temporal traffic transportation type