Edge-Aware Dual-Path Spatial-Temporal Graph Attention Networks for Traffic Flow Forecasting
Year: 2026
Type: conference-paper
Abstract: Accurate traffic flow forecasting is essential for intelligent transportation systems, yet real-world road networks exhibit both explicit connectivity constraints and latent correlations induced by shared temporal patterns and dynamic road conditions. We present an edge-aware dualpath spatial-temporal graph attention framework for multistep traffic forecasting. The model employs two parallel spatial paths, combining a road-topology graph with an adaptive graph to capture hidden dependencies beyond direct connectivity. Unlike node-only attention, our spatial module injects edge attributes-static road length and time-varying road speed-directly into attention scoring and message aggregation, enabling condition-aware propagation under dynamic link states. Temporal dependencies are modeled by a dilated Temporal Convolutional Network (TCN) with residual connections to capture both short-term bursts and longer-range trends. A gated fusion module adaptively integrates the two paths. The proposed design retains the inductive advantages of attention-based graph learning while improving expressiveness through edge-aware interactions, making it suitable for realistic traffic systems where link conditions strongly modulate regional dynamics.
Institution: Tamkang University
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7204104166