ST-Hybrid: Dynamic Graph Learning with Multi-Scale Spatio-Temporal Attention for Traffic Forecasting

Accurate traffic forecasting is fundamental for intelligent transportation systems, directly influencing congestion management, safety, and emissions. A central challenge is the non-stationary nature of traffic flow, where relationships between sensors shift dynamically due to incidents, demand changes, and propagation waves. Many existing graph-based models rely on static graphs or computationally heavy dynamic mechanisms, limiting their suitability for real-time deployment. We propose ST-Hybrid, a spatio-temporal model that incorporates a lightweight state-conditioned dynamic graph learner. The module updates connectivity in real time using adaptive node embeddings and sparse top- k neighborhood selection, offering a practical balance between flexibility and efficiency. On PeMSD8, it obtains a mean absolute error (MAE) of 15.19 and root mean square error (RMSE) of 24.18, ranking second among recent dynamic-graph approaches. It maintains competitive accuracy on the larger PeMSD4 network (MAE 20.16, RMSE 32.17) and demonstrates robust performance on PeMSD3 (MAE 15.58, RMSE 25.93) and PeMSD7 (MAE 21.58, RMSE 34.36). Sensor-level analyses reveal that most residual error is concentrated in a small subset of volatile sensors, suggesting that targeted refinement may yield greater improvements than further global architectural complexity. Importantly, ST-Hybrid maintains low inference latency (approximately 15 ms), demonstrating its suitability for large-scale, real-time traffic forecasting applications.