动态融合模块提升交通流量预测精度
Dynamic Trend Fusion Module for Traffic Flow Prediction
- 用动态趋势变换器联合建模时空特征
- 在四个真实数据集上达到领先性能
- 适合交通流预测与智能交通系统研究者
准确的交通流量预测对运输物流等应用至关重要，但因复杂的时空相关性和非线性交通模式而面临挑战。现有方法通常分开建模空间与时间依赖关系，难以有效融合。为此，本文提出动态时空趋势变换器 DST2former，通过自适应嵌入捕捉时空相关性，并融合动态与静态信息以学习交通网络的多视角动态特征。该方法采用动态趋势表示变换器（DTRformer）分别对时间和空间维度的编码器生成动态趋势，通过跨时空注意力机制进行融合。预定义图被压缩为表示图，以提取静态属性并减少冗余。在四个真实世界交通数据集上的实验表明，该框架实现了最先进的性能。
原文摘要 · Abstract (English)
Accurate traffic flow prediction is essential for applications like transport logistics but remains challenging due to complex spatio-temporal correlations and non-linear traffic patterns. Existing methods often model spatial and temporal dependencies separately, failing to effectively fuse them. To overcome this limitation, the Dynamic Spatial-Temporal Trend Transformer DST2former is proposed to capture spatio-temporal correlations through adaptive embedding and to fuse dynamic and static information for learning multi-view dynamic features of traffic networks. The approach employs the Dynamic Trend Representation Transformer (DTRformer) to generate dynamic trends using encoders for both temporal and spatial dimensions, fused via Cross Spatial-Temporal Attention. Predefined graphs are compressed into a representation graph to extract static attributes and reduce redundancy. Experiments on four real-world traffic datasets demonstrate that our framework achieves state-of-the-art performance.
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