The invention discloses a federated learning traffic prediction method based on traffic mode classification, and the method comprises the steps: firstly carrying out the clustering of the traffic data of a to-be-predicted traffic data management center, constructing a label according to a clustering result, and constructing a traffic mode classification model based on a multi-layer perceptron; then model parameters of the traffic mode classification model are shared to other traffic data management centers, the other traffic data management centers classify local traffic data, and then each category of data set after classification is utilized to train a gating cycle unit GRU according to a federated learning framework to obtain model parameters of each category of GRU; and finally, classifying the future traffic flow of the to-be-predicted traffic data management center, and predicting the future traffic flow of the to-be-predicted traffic data management center by using the model parameters of the GRU of the corresponding category.
本发明公开一种基于交通模式分类的联邦学习交通预测方法,该方法首先对待预测的交通数据管理中心的交通数据进行聚类,根据聚类后的结果,构建标签,构建基于多层感知器的交通模式分类模型;然后将交通模式分类模型的模型参数共享给其他交通数据管理中心,由它们对本地交通数据进行分类,然后利用分类后的各个类别数据集按照联邦学习框架分别训练门控循环单元GRU,得到每个类别的GRU的模型参数;最后,对待预测的交通数据管理中心的未来交通流进行分类,并用对应类别的GRU的模型参数对待预测的交通数据管理中心的未来交通流进行预测。
Federal learning traffic prediction method based on traffic mode classification
一种基于交通模式分类的联邦学习交通预测方法
2025-01-14
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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