The invention provides a traffic prediction method based on distributed machine learning for predicting road traffic. In this case, the traffic prediction method based on distributed machine learning comprises the following steps: a learning server distributes a global multi-task traffic model to a learning agent to locally train the traffic model; the learning agent uploads a locally trained traffic model to the learning server; the learning server updates the global multi-task traffic model by using locally trained traffic model parameters acquired from the learning agent; the learning server generates a time-dependent global traffic map by using the well trained global multi-task traffic model; distributing the time-dependent global traffic map to vehicles running on a road; and calculating, by the vehicle, an optimal travel route with minimum travel time based on the driving plan using the time-related global traffic map.

    提供一种用于预测道路交通的基于分布式机器学习的交通预测方法。在这种情况下,所述基于分布式机器学习的交通预测方法包括:学习服务器将全局多任务交通模型分发给学习代理,以本地训练交通模型;学习代理将经本地训练的交通模型上传至所述学习服务器;学习服务器使用从学习代理获取的经本地训练的交通模型参数来更新全局多任务交通模型;学习服务器使用经良好训练的全局多任务交通模型生成时间相关全局交通地图;将所述时间相关全局交通地图分发给在道路上行驶的车辆;以及由车辆基于驾驶规划使用时间相关全局交通地图计算行驶时间最少的最优行驶路线。


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    Title :

    Distributed multi-task machine learning for traffic prediction


    Additional title:

    用于交通预测的分布式多任务机器学习


    Contributors:

    Publication date :

    2024-02-09


    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



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