关键词: traffic flow forecasting, graph neural networks, spatio-temporal modeling, attention mechanisms, transformer

Abstract: Accurate traffic flow forecasting hinges on modeling coupled spatio-temporal dependencies rather than treating space and time in isolation. Many prior methods process spatial and temporal features separately — either in series or in parallel — and then fuse them with simple operators, which weakens their ability to capture intrinsic space-time interactions. We propose multi-graph transformer for traffic flow forecasting (MGTTP), a framework with an innovatively designed bidirectional spatio-temporal interaction mechanism: temporal signals guide multi-graph spatial fusion, while spatial context guides attention-based temporal aggregation. It addresses the limitations of static spatial fusion in existing multi-graph models and the serial spatio-temporal modeling paradigm in vanilla transformer baselines, achieving deep coupled modeling of spatio-temporal features. First, MGTTP builds three complementary graphs — adjacency, reachability, and similarity — and applies temporal feature-guided attention to dynamically fuse their multi-dimensional spatial representations. Subsequently, a transformer encoder captures long-term temporal dependencies, with spatial feature-guided attention to aggregate the time series. Finally, a gated fusion module realizes the ultimate fusion of spatio-temporal features for prediction. Extensive experiments on four public real-world traffic datasets demonstrate that MGTTP outperforms all compared mainstream baseline models across all evaluation metrics, with statistically significant performance gaps, validating the effectiveness of the proposed bidirectional spatio-temporal interaction mechanism.

Key words: traffic flow forecasting, graph neural networks, spatio-temporal modeling, attention mechanisms, transformer