MHGNet通过多异构图建模，提升交通流量预测精度。
MHGNet: Multi-Heterogeneous Graph Neural Network for Traffic Prediction
- 将交通数据分解为多模式，用嵌入矩阵捕捉时间与节点特征。
- 基于欧氏距离聚类节点，实现O(N)复杂度的高效分组。
- 在四个基准数据集上表现优异，适合城市交通系统优化。
近年来，交通流量预测在智能交通系统管理中发挥关键作用。然而，传统方法常将非欧几里得低维交通数据建模为单类型节点与边的简单图，难以捕捉同类型节点间的相似趋势。为此，本文提出MHGNet，一种用于建模时空多异构图的新框架。该框架中，STD模块通过时间戳嵌入矩阵与节点嵌入矩阵的特征映射，将单模式交通数据解耦为多模式数据；随后，节点聚类器利用节点与不同类型聚点之间的欧氏距离，以O(N)时间复杂度完成聚类。每个簇内的节点在由DSTGG模块生成的时空融合子图上进行残差子图卷积，并在SIE模块中完成节点重定位与权重再分配。为验证MHGNet的有效性，本文在四个广泛使用的基准数据集上开展大量消融实验与定量评估，结果表明其性能显著优越。
原文摘要 · Abstract (English)
In recent years, traffic flow prediction has played a crucial role in the management of intelligent transportation systems. However, traditional forecasting methods often model non-Euclidean low-dimensional traffic data as a simple graph with single-type nodes and edges, failing to capture similar trends among nodes of the same type. To address this limitation, this paper proposes MHGNet, a novel framework for modeling spatiotemporal multi-heterogeneous graphs. Within this framework, the STD Module decouples single-pattern traffic data into multi-pattern traffic data through feature mappings of timestamp embedding matrices and node embedding matrices. Subsequently, the Node Clusterer leverages the Euclidean distance between nodes and different types of limit points to perform clustering with O(N) time complexity. The nodes within each cluster undergo residual subgraph convolution within the spatiotemporal fusion subgraphs generated by the DSTGG Module, followed by processing in the SIE Module for node repositioning and redistribution of weights. To validate the effectiveness of MHGNet, this paper conducts extensive ablation studies and quantitative evaluations on four widely used benchmarks, demonstrating its superior performance.
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