轻量级模型提升大规模交通流量预测精度与效率
Positional-aware Spatio-Temporal Network for Large-Scale Traffic Prediction
- 引入位置感知嵌入分离节点表征，增强空间区分度
- 设计时间注意力模块，有效捕捉长时序依赖关系
- 适合城市级及以上规模交通系统实时预测场景
交通流预测已成为日常生活不可或缺的任务，需在图结构下利用各位置间时空关系预测未来流量。然而，大范围地理区域和更长预测周期下的高出行需求，要求模型能清晰区分每个节点并具备全局历史视角，这在以往工作中未被充分关注。此外，数据规模增大也阻碍了多数模型在真实环境中的部署。为此，本文提出一种轻量级的位置感知时空网络（PASTN），以端到端方式有效捕捉时空复杂性。PASTN引入位置感知嵌入以分离各节点表示，并通过时间注意力模块提升模型对长程历史的感知能力。大量实验验证了PASTN在不同规模数据集（县、大都市、州）上的有效性与高效性。进一步分析表明，新引入模块均具显著作用。
原文摘要 · Abstract (English)
Traffic flow forecasting has emerged as an indispensable mission for daily life, which is required to utilize the spatiotemporal relationship between each location within a time period under a graph structure to predict future flow. However, the large travel demand for broader geographical areas and longer time spans requires models to distinguish each node clearly and possess a holistic view of the history, which has been paid less attention to in prior works. Furthermore, increasing sizes of data hinder the deployment of most models in real application environments. To this end, in this paper, we propose a lightweight Positional-aware Spatio-Temporal Network (PASTN) to effectively capture both temporal and spatial complexities in an end-to-end manner. PASTN introduces positional-aware embeddings to separate each node's representation, while also utilizing a temporal attention module to improve the long-range perception of current models. Extensive experiments verify the effectiveness and efficiency of PASTN across datasets of various scales (county, megalopolis and state). Further analysis demonstrates the efficacy of newly introduced modules either.
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