Time varying Granger causality guided delay aligned propagation and spatiotemporal adaptive embedding for traffic speed prediction

Traffic speed prediction is vital for intelligent transportation systems. To achieve accurate forecasting, spatio-temporal graph neural networks have been widely adopted; however, despite recent advances, many existing methods still aggregate spatial features synchronously and rely on static or weakly adaptive dependency structures. This mismatch limits long-horizon accuracy because spatial influence in traffic networks is inherently directional, delay-sensitive, and time-varying. To address these limitations, we propose a unified forecasting framework: the Time-Varying Granger Causality-Enhanced Spatio-Temporal Adaptive Embedding Transformer (TVGC-STAEformer). Our framework utilizes a strictly causal Recursive Least Squares (RLS) procedure to estimate dynamic predictive influence and optimal propagation lags. These estimates guide a Delay-Aligned Dynamic Causal Message Passing (DADCMP) module to aggregate historical features at physically appropriate temporal offsets. Additionally, a Spatio-Temporal Adaptive Embedding (STAE) module explicitly encodes node-specific and time-varying heterogeneity. By precomputing these causal tensors, the model efficiently captures realistic directional dependencies directly from evolving traffic sequences without requiring strong interventional causation. Experiments on Daegu and METR-LA, supported by five-seed and congestion-transition analyses on Daegu, demonstrate the effectiveness and stability of TVGC-STAEformer, while a supplementary PeMSD7-M evaluation probes whether the observed long-horizon tendency is retained on a larger detector network. Specifically, compared to the strongest baseline (DCRNN) at the 60-min prediction horizon, our model reduces the MAE by 1.93% and 1.56% on the Daegu and METR-LA datasets, respectively. These results suggest that explicitly combining delay alignment, time-varying predictive influence, and node-time heterogeneity is a useful direction for traffic speed forecasting. These findings can support traffic agencies in selecting forecasting tools for proactive signal control, route guidance, and congestion management, particularly when reliable long-horizon speed estimates are required.