The invention discloses a sudden congestion discrimination method and system based on multi-source traffic big data fusion. The method comprises the steps of: firstly carrying out vectorization on anobtained real-time road condition map, building a corresponding topological relation based on interrupted discrete points; fusing obtained multi-source traffic big data, and building a corresponding data set for storing the data; training a training data set by using a neural network model; performing convergence by using an error square and a loss function to obtain a corresponding discriminationmodel; performing zeroing on any plurality of multi-source traffic big data in the data set, and inputting the data into the verified discrimination model to train the model to obtain a congestion discrimination model; and finally, obtaining a plurality of sudden data based on the topological relation, inputting the sudden data into the congestion discrimination model, establishing a corresponding topological grade relation according to a prediction result, and carrying out road condition pigment assignment. Therefore, the processing effect of sudden congestion can be improved.
本发明公开了一种基于多源交通大数据融合的突发拥堵判别方法及系统,首先将获取的实时路况图进行矢量化,并基于打断的离散点,构建对应的拓扑关系,然后将获取的多源交通大数据进行融合,并构建对应的数据集进行保存,接着,利用利用神经网络模型对训练数据集进行训练,并利用误差平方和损失函数进行收敛,得到对应的判别模型,紧接着将所述数据集中的任意多个所述多源交通大数据进行归零后输入验证后的所述判别模型进行训练,得到拥堵判别模型;最后,基于所述拓扑关系获取多个突发数据输入所述拥堵判别模型,根据所述预测结果,建立对应的拓扑等级关系,并进行路况色素赋值,能够提高对突发性拥堵的处理效果。
Sudden congestion discrimination method and system based on multi-source traffic big data fusion
一种基于多源交通大数据融合的突发拥堵判别方法及系统
12.03.2021
Patent
Elektronische Ressource
Chinesisch
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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