轻量级模型融合图嵌入与旋转位置编码，提升交通预测精度
Lightweight Spatio-Temporal Attention Network with Graph Embedding and Rotational Position Encoding for Traffic Forecasting
- 结合时空注意力与图嵌入，捕捉长距离交通动态
- 通过网格搜索优化旋转位置编码频率，显著提升建模能力
- 无需复杂特征工程，在PeMS04/08数据集上表现领先
交通预测是智能交通系统中的关键任务。现有研究多将图神经网络（GNNs）与其他模型结合，但GNN仅考虑短程空间信息。本文提出轻量级时空注意力网络LSTAN-GERPE，融合时空注意力机制，有效捕捉长程交通动态。通过网格搜索确定时空注意力中旋转位置编码的最优频率，实现系统性优化，增强复杂交通模式的建模能力。同时，将地理坐标地图融入时空嵌入，提升特征表示。无需大量特征工程，该方法在真实交通数据集PeMS04和PeMS08上达到先进水平。
原文摘要 · Abstract (English)
Traffic forecasting is a key task in the field of Intelligent Transportation Systems. Recent research on traffic forecasting has mainly focused on combining graph neural networks (GNNs) with other models. However, GNNs only consider short-range spatial information. In this study, we present a novel model termed LSTAN-GERPE (Lightweight Spatio-Temporal Attention Network with Graph Embedding and Rotational Position Encoding). This model leverages both Temporal and Spatial Attention mechanisms to effectively capture long-range traffic dynamics. Additionally, the optimal frequency for rotational position encoding is determined through a grid search approach in both the spatial and temporal attention mechanisms. This systematic optimization enables the model to effectively capture complex traffic patterns. The model also enhances feature representation by incorporating geographical location maps into the spatio-temporal embeddings. Without extensive feature engineering, the proposed method in this paper achieves advanced accuracy on the real-world traffic forecasting datasets PeMS04 and PeMS08.
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