traffic-forecasting

PatchSTG introduces a patch-based spatiotemporal graph Transformer for traffic forecasting on irregular sensor networks, achieving near-linear complexity while maintaining competitive performance.

Proposes GC-MoE, a graph-conditioned mixture of experts framework for traffic forecasting that assigns each node a personalized combination of frozen pretrained spatio-temporal GNN experts based on graph topology and recent input, training only a lightweight routing module (∼17K parameters) and achieving competitive performance on four benchmarks.

Proposes a Global-Local Graph Attention Network (GLGAT) with pairwise encoding and event-based adjacency matrix for traffic forecasting, effectively capturing spatio-temporal correlations and achieving competitive performance on real-world datasets.