Bilinear Spatiotemporal Fusion Network: An efficient approach for traffic flow prediction

Abstract

Accurate traffic flow forecasting is critical for intelligent transportation systems, yet increasing model complexity in spatiotemporal graph neural networks does not always yield proportional gains. In this paper, we present a Bilinear Spatiotemporal Fusion Network (BLSTF) tailored for stable, periodic traffic scenarios. First, a temporal enhancement module is introduced to mitigate multi-step error accumulation. Second, predefined graph priors with linear feedback leverage known road topologies for straightforward yet effective spatial modeling. Finally, a bilinear fusion mechanism seamlessly integrates refined temporal and spatial features with minimal computational overhead. Extensive experiments on four real-world datasets show that BLSTF outperforms state-of-the-art methods, achieving MAE and MAPE of 14.05 and 13.90% on PEMS03, 17.93 and 12.12% on PEMS04, 18.87 and 7.86% on PEMS07, and 13.49 and 8.71% on PEMS08, demonstrating BLSTF’s potential to deliver accurate, efficient, and interpretable traffic flow forecasts.