ST-GTrans: Spatio-temporal graph transformer with road network semantic awareness for traffic flow prediction

Accurate traffic prediction has significant implications for traffic optimization and management. However, few studies have thoroughly considered the implicit spatial semantic information and intricate temporal patterns. To address these challenges, we propose a spatio-temporal graph transformer with road network semantic awareness (ST-GTrans) for traffic flow prediction, an architecture that extends the transformer to effectively model spatio-temporal dependencies in traffic data. This model incorporates a multiscale temporal transformer designed to capture historical traffic patterns across multiple time scales, enabling the identification of short- and long-term temporal dependencies. Additionally, ST-GTrans addresses spatial dependencies by separately modeling the dynamic and static traffic components. Dynamic components employ a graph transformer with an edge that captures the semantic interactions between nodes through a multi-head attention mechanism. This mechanism integrates edge features from a semantic matrix constructed using a dynamic time-warping method based on time-series traffic data. For the static components, a multi-hop graph convolutional network was used to model the spatial dependencies rooted in the road network. Finally, a generative decoder was incorporated to mitigate error accumulation in long-term predictions. Extensive experiments on diverse datasets, including the PeMS03 traffic dataset (California freeway traffic data), the Shanghai metro flow dataset, and the Hong Kong traffic dataset, validated the effectiveness of ST-GTrans in capturing complex spatio-temporal patterns and demonstrated significant improvements over state-of-the-art baseline methods across multiple metrics.