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Traffic forecasting is a complex multivariate time-series regression task ofparamount importance for traffic management and planning. However, existingapproaches often struggle to model complex multi-range dependencies using localspatiotemporal features and road network hierarchical knowledge. To addressthis, we propose MultiSPANS. First, considering that an individual recordingpoint cannot reflect critical spatiotemporal local patterns, we designmulti-filter convolution modules for generating informative ST-token embeddingsto facilitate attention computation. Then, based on ST-token andspatial-temporal position encoding, we employ the Transformers to capturelong-range temporal and spatial dependencies. Furthermore, we introducestructural entropy theory to optimize the spatial attention mechanism.Specifically, The structural entropy minimization algorithm is used to generateoptimal road network hierarchies, i.e., encoding trees. Based on this, wepropose a relative structural entropy-based position encoding and a multi-headattention masking scheme based on multi-layer encoding trees. Extensiveexperiments demonstrate the superiority of the presented framework over severalstate-of-the-art methods in real-world traffic datasets, and the longerhistorical windows are effectively utilized. The code is available athttps://github.com/SELGroup/MultiSPANS.

MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimization