The invention relates to a traffic flow prediction method based on a multimode dynamic memory graph convolutional network, and belongs to the technical field of intelligent traffic. An existing graph convolutional neural network method is insufficient in space-time correlation characteristic extraction, insufficient in periodic characteristic consideration and inaccurate in dynamic evolution correlation capture. In order to solve the problem, firstly, a time sequence feature extraction module and a bidirectional memory loop network module are established, and captured time sequence features are fused to form a comprehensive feature vector; then, in a dynamic graph convolution module, through a fusion mode of a diffusion graph convolution neural network, an attention mechanism and a typical traffic mode, capturing spatial correlation between nodes, and generating a new dynamic adjacency matrix; the dynamic adjacency matrix can reflect the node connection relation changing along with time. According to the method, the adjacent matrix is dynamically updated through the multimode dynamic memory graph convolutional network, emergencies and abnormal conditions can be better dealt with, and the accuracy and robustness of traffic flow prediction are effectively improved.

    本发明涉及一种基于多模动态记忆性图卷积网络的交通流量预测方法,属于智能交通技术领域。目前图卷积神经网络的方法时空相关特性提取不足、周期性特征考虑不充分、动态演变相关性捕捉不准确。为了解决上述问题,本发明首先建立了时序特征提取模块和双向记忆循环网络模块,并将捕获的时序特征融合形成综合特征向量。然后,在动态图卷积模块中,通过扩散图卷积神经网络、注意力机制和典型交通模式的融合方式,捕捉节点间的空间相关性,并生成新的动态邻接矩阵。该动态邻接矩阵能够反映随时间变化的节点连接关系。本发明通过多模动态记忆性图卷积网络动态更新邻接矩阵,更好地应对突发事件和异常情况,有效提高了交通流量预测的准确性和鲁棒性。


    Access

    Download


    Export, share and cite



    Title :

    Traffic flow prediction method based on multimode dynamic memory graph convolutional network


    Additional title:

    一种基于多模动态记忆性图卷积网络的交通流量预测方法


    Contributors:
    HUANG XIAOGE (author) / YANG WENZHUO (author) / ZHOU ENZHOU (author) / CHEN QIANBIN (author)

    Publication date :

    2024-12-10


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



    Traffic flow prediction method based on graph convolutional network

    XU HUI / MENG FANYU / REN QIANQIAN et al. | European Patent Office | 2025

    Free access

    Traffic flow prediction method based on adaptive dynamic fusion graph convolutional network

    ZHANG SHUAI / YU WANGZHI / LEE HAE KWANG et al. | European Patent Office | 2024

    Free access

    Traffic flow prediction method based on multi-view dynamic graph convolutional network

    HUANG XIAOHUI / YE YUMING / LING JIAHAO et al. | European Patent Office | 2022

    Free access

    Space-time adaptive dynamic graph convolutional network traffic flow prediction method

    CUI WENTIAN / LOU JUNGANG / SHEN QING et al. | European Patent Office | 2024

    Free access

    Traffic flow prediction method based on dynamic adaptive adversarial graph convolutional neural network

    WANG HUI / WANG YU / DU KAI | European Patent Office | 2024

    Free access