Bi-STAT+: An Enhanced Bidirectional Spatio-Temporal Adaptive Transformer for Urban Traffic Flow Forecasting

Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems (ITS), playing a pivotal role in mitigating congestion, enhancing route optimization, and improving the utilization efficiency of roadway infrastructure. However, existing methods struggle in complex traffic scenarios due to static spatio-temporal embedding, restricted multi-scale temporal modeling, and weak representation of local spatial interactions. This study proposes Bi-STAT+, an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions: (1) an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations; (2) frequency-domain analysis in the temporal dimension for simultaneous high-frequency details and low-frequency trend extraction; and (3) an agent attention mechanism in the spatial dimension that enhances local feature extraction through dynamic weight allocation. Extensive experiments were performed on four distinct datasets, including two publicly benchmark datasets (PEMS04 and PEMS08) and two private datasets collected from Baotou and Chengdu, China. The results demonstrate that Bi-STAT+ consistently outperforms existing methods in terms of MAE, RMSE, and MAPE, while maintaining strong robustness against missing data and noise. Furthermore, the results highlight that prediction accuracy improves significantly with higher sampling rates, providing crucial insights for optimizing real-world deployment scenarios.