The invention relates to the field of traffic flow prediction, in particular to a heterogeneous graph collaborative learning traffic flow prediction method. The problems that according to an existing method, dynamic space-time dependence modeling is insufficient, heterogeneous relation utilization is missing, long-term and short-term feature extraction is difficult, and calculation efficiency is bottleneck are solved. The main scheme includes the steps that space-time embedding representation is generated, topological structure information and time features of nodes are fused, and space-time embedding representation STE is obtained; the method comprises the following steps: splicing input time sequence data with STE, filtering out high-frequency noise and enhancing long and short term dependency features through an adaptive spectrum block, and extracting multi-scale homogeneous time features through an interactive convolution block; in combination with adaptive memory attention and progressive graph convolution, spatial dependency is extracted from input data of homogeneous branches, and homogeneous spatial-temporal features are obtained; constructing a heterogeneous adjacency matrix, extracting negative correlation spatiotemporal features through heterogeneous graph spatiotemporal convolution, and performing cooperative interaction with homogeneous spatiotemporal features; and stacking multiple layers of space-time blocks, aggregating the output of each layer, and generating a traffic flow prediction result through an output layer.

    本发明涉及交通流预测领域,特别是涉及一种异质图协同学习交通流预测方法。解决现有方法在动态时空依赖建模不足、异质关系利用缺失、长短期特征提取困难及计算效率瓶颈等问题,主要方案包括生成时空嵌入表示,融合节点的拓扑结构信息与时间特征,得到时空嵌入表示STE;将输入时间序列数据与STE拼接,通过自适应频谱块滤除高频噪声并增强长短期依赖特征,再经交互卷积块提取多尺度同质时间特征;结合自适应记忆注意力和渐进图卷积,从同质分支的输入数据中提取空间依赖性,得到同质时空特征;构建异质邻接矩阵,通过异质图时空卷积提取负相关时空特征,并与同质时空特征进行协同交互;堆叠多层时空块,聚合各层输出并通过输出层生成交通流预测结果。


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    Title :

    Heterogeneous graph collaborative learning traffic flow prediction method


    Additional title:

    一种异质图协同学习交通流预测方法


    Contributors:
    NIU XINZHENG (author) / LUO JIAQING (author) / ZHOU YIKAI (author) / YAN CHAO (author) / WEN SHUAI (author) / LI YUE (author) / HUANG SHAN (author)

    Publication date :

    2025-06-27


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



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