The invention discloses a segmented iteration long-term traffic flow prediction method based on a space-time diagram convolutional network. Firstly, entities of sensors involved in an application scene are mapped into node weight information of a graph theory to achieve problem modeling, and a graph model is obtained; dividing the traffic characteristic matrix to serve as the input of the space-time diagram convolutional network; and finally, predicting the prediction result of each segment in parallel by using a parallel multi-step PMF prediction method, and embedding the predicted segments in combination with positions to obtain the traffic information of the next moments. According to the space-time diagram convolutional network, segmentation iteration is adopted to replace point-by-point iteration, and parallel multi-step prediction PMF is adopted to replace recursive multi-step prediction RMF during prediction. According to the method, the recursive iteration times required for extracting the time features are remarkably reduced through a segmented iteration strategy, so that the challenge of effectively training the GRU on a long sequence is solved. In addition, due to the adoption of the PMF, the inherent error accumulation problem in the traditional RMF method is further relieved.

    本发明公开了基于时空图卷积网络的分段迭代长期交通流量预测方法。首先将应用场景中涉及的传感器的实体映射成图论的节点权重信息来实现问题模型化,获取图模型;然后通过将交通特征矩阵划分,作为时空图卷积网络的输入。最后利用并行多步预测PMF的方法并行预测各个分段的预测结果,将预测的各个分段结合位置嵌入获得接下来各时刻的交通信息。本发明时空图卷积网络采用分段迭代代替逐点迭代,预测时用并行多步预测PMF代替递归多步预测RMF。本发明通过分段迭代策略显著减少了提取时间特征所需的递归迭代次数,从而解决了在长序列上有效训练GRU的挑战。此外,PMF的采用进一步缓解了传统RMF方法中固有的误差累积问题。


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    Title :

    Segmented iteration long-term traffic flow prediction method based on space-time diagram convolutional network


    Additional title:

    基于时空图卷积网络的分段迭代长期交通流量预测方法


    Contributors:
    LIU PENG (author) / ZU LIHAO (author)

    Publication date :

    2025-01-07


    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 / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06Q Datenverarbeitungssysteme oder -verfahren, besonders angepasst an verwaltungstechnische, geschäftliche, finanzielle oder betriebswirtschaftliche Zwecke, sowie an geschäftsbezogene Überwachungs- oder Voraussagezwecke , DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES



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