Papers › Spatial-Temporal Bipartite Graph Attention Network for Traffic Forecasting
Spatial-Temporal Bipartite Graph Attention Network for Traffic Forecasting
Dimuthu Lakmal, Kushani Perera, Renata Borovica-Gajic, Shanika Karunasekera
Accurate traffic forecasting is pivotal for an efficient data-driven transportation system. The intricate nature of spatial-temporal dependencies and non-linearity present in traffic data has posed a significant challenge to the modeling of accurate traffic forecasting systems. Lately, there has been a significant effort to develop complex Spatial-Temporal Graph Neural Networks (STGNN) that predominantly utilize various Graph Neural Networks (GNN) and attention-based encoder-decoder architectures due to their ability to capture non-linear dependencies in spatial and temporal domains effectively. However, conventional GNNs limit explicit propagation of past information among nodes, while attention-based models such as transformers do not support finer-grained attention score distribution. In this study, we address the aforementioned issues and introduce a novel STGNN namely, Spatio-Temporal Bipartite Graph Attention Network (STBGAT) that allows explicit modeling of past information propagation among nodes. Further, we present a heterogeneous cross-attention mechanism in a transformer to compute finer-grained feature-wise attention distribution enabling the model to capture richer and more expressive temporal dependencies. Our experiments reveal that the proposed architecture outperforms the state-of-the-art approaches proposed in recent literature.
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.
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