Cross-Domain Transformer Spatial-Temporal Fusion Network for Traffic Flow Forecasting

Accurate traffic forecasting is challenging due to the intricate inter-dependencies of road networks and congestion caused by unexpected accidents. Recent work has focused on dynamically changing traffic characteristics but has paid less attention to the global cross-spatial-temporal domain of modeling, which may limit their performance. In this paper, we propose a novel plug-and-play fusion unit to accurately express the spatial-temporal dependencies by cross-domain complementary information integration, named the Cross-Domain Transformer Spatial-Temporal Fusion Network (CDTSTFN). By introducing two-stage fusion units, we compensate information loss and resolve the mismatch in fused information. This enables CDTSTFN to largely augment the base spatial-temporal predictors with learned both local-global spatial and short-long temporal dependencies on cross-domain spatial-temporal patterns. A comprehensive set of both quantitative and qualitative assessments is performed on six public traffic network datasets (PeMS03, PeMS04, PeMS07, PeMS08, METR-LA, and PeMS-BAY), demonstrating the superior performance of our model.