Adaptive spatial–temporal graph attention network for real-time traffic forecasting

Abstract

Accurate and efficient Multivariate Time Series Forecasting (MTSF) plays a critical role in intelligent transportation systems by supporting real-time traffic management. However, achieving reliable forecasting remains challenging due to complex and dynamically evolving spatial–temporal patterns. Existing forecasting methods often fail to adapt effectively to these dynamic traffic conditions and typically incur high computational costs, significantly limiting their deployment in real-time traffic management scenarios. To address these engineering challenges, this study proposes a novel Attention-based Spatial-Temporal Network (ASTNet), explicitly designed for adaptive and efficient real-time traffic forecasting. ASTNet introduces two innovative Artificial Intelligence (AI)-driven modules: an Adaptive Spatial Graph Encoder (ASGE), which dynamically models evolving spatial dependencies from real-time traffic data, thus overcoming the limitations of static graph structures; and a Temporal Attention-Gated Unit (TAGU), which efficiently captures critical temporal dependencies through the integration of recurrent gating mechanisms and self-attention techniques. Extensive evaluations conducted on widely-used traffic benchmark datasets (PEMS04, METR-LA, etc.) confirm that ASTNet achieves superior predictive accuracy and robustness compared to state-of-the-art methods, while significantly reducing inference latency. Ablation studies further validate that the combined innovations of ASGE and TAGU are crucial for ASTNet’s outstanding performance, highlighting its practical suitability and strong potential for deployment in real-time intelligent transportation applications.