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