MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for
  Traffic Forecast via Structural Entropy Optimization
Dongcheng Zou Senzhang Wang Xuefeng Li Hao Peng Yuandong Wang Chunyang Liu Kehua Sheng Bo Zhang
Traffic forecasting is a complex multivariate time-series regression task of
paramount importance for traffic management and planning. However, existing
approaches often struggle to model complex multi-range dependencies using local
spatiotemporal features and road network hierarchical knowledge. To address
this, we propose MultiSPANS. First, considering that an individual recording
point cannot reflect critical spatiotemporal local patterns, we design
multi-filter convolution modules for generating informative ST-token embeddings
to facilitate attention computation. Then, based on ST-token and
spatial-temporal position encoding, we employ the Transformers to capture
long-range temporal and spatial dependencies. Furthermore, we introduce
structural entropy theory to optimize the spatial attention mechanism.
Specifically, The structural entropy minimization algorithm is used to generate
optimal road network hierarchies, i.e., encoding trees. Based on this, we
propose a relative structural entropy-based position encoding and a multi-head
attention masking scheme based on multi-layer encoding trees. Extensive
experiments demonstrate the superiority of the presented framework over several
state-of-the-art methods in real-world traffic datasets, and the longer
historical windows are effectively utilized. The code is available at
https://github.com/SELGroup/MultiSPANS.