The invention discloses a federated learning-based vehicle travel time estimation method, which comprises the following steps of: processing motor vehicle data to obtain a user driving characteristic data set; according to the data input requirement of the model, inputting the user driving feature data set into a local model for single-round federated learning training to generate a local model weight, and calculating the Pearson correlation coefficient of a local model neural network layer and a global model neural network layer; uploading the local model neural network layer with the large Pearson correlation coefficient to a federated learning cloud server; weighting the local model weight and the global model weight in proportion to obtain a new round of global model weight; the global model weight is sent to a local model corresponding to the user participating in the federation learning of the round, the local model weight is updated, and a final local model is obtained; and inputting the to-be-evaluated data into the local model to carry out vehicle travel time estimation to obtain an evaluation result. The method is safe and efficient, and can be widely applied to the technical field of intelligent traffic systems.

    本发明公开了一种基于联邦学习的车辆行程时间估计方法,方法包括:对机动车数据进行处理,获取用户行驶特征数据集;根据模型的数据输入要求,将用户行驶特征数据集输入本地模型进行单轮联邦学习训练生成本地模型权重,并计算本地模型神经网络层和全局模型神经网络层的皮尔森相关系数,将皮尔森相关系数大的本地模型神经网络层上传至联邦学习云端服务器;将本地模型权重和全局模型权重按比例加权得到新一轮的全局模型权重;将全局模型权重发送给参与本轮联邦学习的用户对应的本地模型更新本地模型权重,得到最终的本地模型;将待评估数据输入本地模型进行车辆行程时间估计,得到评估结果。本发明安全且高效,可广泛应用于智能交通系统技术领域。


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    Titel :

    Federal learning-based vehicle travel time estimation method


    Weitere Titelangaben:

    一种基于联邦学习的车辆行程时间估计方法


    Beteiligte:
    HUANG ZETENG (Autor:in) / ZHOU MENG (Autor:in) / YOU LINLIN (Autor:in) / LIU SHENG (Autor:in)

    Erscheinungsdatum :

    09.06.2023


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



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