Papers › Bilinear Spatiotemporal Fusion Network: An efficient approach for traffic flow prediction
Bilinear Spatiotemporal Fusion Network: An efficient approach for traffic flow prediction
Jing Chen, Shixiang Pan, Weimin Peng, Wenqiang Xu
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections