Leveraging pretrained local augmentation modules for enhanced traffic flow prediction in STGNN

Traffic forecasting is challenging due to the complexity and variability of traffic systems. Multigraph spatiotemporal graph neural networks improve accuracy by capturing spatiotemporal correlations but struggle with nodes having few neighbors. We propose a pretrainable local augmentation module (LAM) to address this, capturing long-term historical data and using adjacency relationships to enrich node features. We also introduce a clustered feature correlation graph to uncover hidden correlations. Experiments on the METR-LA dataset show significant error reductions.