The invention discloses an urban traffic situation identification method based on a directed graph convolutional neural network. The method comprises the steps: carrying out the traffic situation classification of historical traffic flow information, converting an urban road network into a directed graph according to a point-edge conversion rule, and extracting a corresponding sub-graph; then, calculating the weight of a directed edge and the weight between non-directly connected nodes, standardizing the number of nodes of the subgraph, and calculating a traffic information matrix and a feature matrix of the subgraph; finally, designing a traffic directed graph convolutional neural network model, performing training and testing, using the model for classifying real-time traffic flow information to identify the real-time traffic situation of all road sections. According to the method, the incidence relation between directed road sections of different levels and different grades under the hybrid road network is fully considered, a unified standardized model input and traffic situation recognition model is designed, and good universality is achieved; moreover, the method has the characteristics of simple process, easiness in calculation, easiness in programming realization and the like, and can be suitable for complex urban road networks.
基于有向图卷积神经网络的城市交通态势识别方法,首先,对历史交通流信息进行交通态势分类,根据“点边”转换规则,把城市路网转换成有向图,并提取相应的子图;然后,计算有向边的权重和非直接相连节点间的权重,标准化处理子图节点个数,计算子图的交通信息矩阵及其特征矩阵;最后,设计交通有向图卷积神经网络模型,并进行训练和测试,该模型可以分类实时交通流信息,从而识别出所有路段的实时交通态势。本发明方法充分考虑了混合路网下不同层级、不同等级的有向路段之间的关联关系,设计了统一的标准化模型输入和交通态势识别模型,具有很好的普适性;而且,方法具有流程简单、计算容易和易编程实现等特点,可以适用于复杂的城市路网。
Urban traffic situation identification method based on directed graph convolutional neural network
基于有向图卷积神经网络的城市交通态势识别方法
14.08.2020
Patent
Elektronische Ressource
Chinesisch
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