A Scalable Context-Aware STGCN Framework for Real-Time Traffic Forecasting with Residual Correction

Accurate short-term traffic prediction is a key requirement for modern traffic management systems, yet many existing approaches remain focused on offline evaluation and do not address the challenges of continuous real-time deployment. In this work, we present a context-aware spatiotemporal graph convolutional network (STGCN) framework designed for low-latency, scalable traffic forecasting under operational conditions. The proposed approach integrates structural information from the road network, temporal regularities derived from historical data, and a residual correction mechanism trained on systematic prediction errors observed during real-time operation. The framework is designed to remain lightweight, enabling continuous minute-level inference without computational overhead that would hinder long-term deployment. The methodology is evaluated in two real-world case studies of different scale and complexity. In Thessaloniki, Greece, multiple forecasting models are evaluated across different temporal resolutions using one-minute speed data, with the proposed STGCN selected for real-time deployment. A residual correction module trained on historical prediction errors further improves real-time forecasting accuracy compared to the baseline STGCN deployment. Scalability is further demonstrated in the South Holland region of the Netherlands, where the same architecture is applied to a larger network and extended to multi-horizon forecasting. Results show that the proposed framework achieves competitive predictive performance while maintaining low computational cost, and that incorporating residual error learning provides a robust and practical solution for improving forecasting accuracy in real-world deployments. These findings highlight the importance of combining domain-specific modeling with operational considerations in traffic prediction systems.