The invention discloses an urban road network dynamic OD estimation method based on a graph convolutional neural network. The method mainly comprises the steps of obtaining AVI detection data and mobile crowdsourcing trajectory data in a research road network, performing road network matching, counting the number of vehicles passing through each detection point in different time periods, and generating an AVI detection OD matrix and a mobile crowdsourcing OD matrix; modeling into graph structure data according to real dynamic OD characteristics of a road network, and constructing a daily dynamic OD relation graph based on a mobile crowdsourcing OD matrix; fusing the AVI detection OD matrix and the mobile crowdsourcing OD matrix by using a projection method, and generating a priori OD matrix as feature representation of nodes in a daily dynamic OD relation graph; the daily dynamic OD relation graph and the corresponding feature representation serve as input, a GCN model serves as a convolutional layer to construct an encoder, decoding is carried out through a full connection layer, and a dynamic OD estimation model based on a graph convolutional neural network is constructed; and calculating the total loss of the model and adjusting model parameters according to the total loss of the model to obtain a trained dynamic OD estimation model.
本发明公开了一种基于图卷积神经网络的城市路网动态OD估计方法。主要步骤包括:获取研究路网内AVI检测数据和移动众包轨迹数据,进行路网匹配,分时段统计各个检测点通过的车辆数,生成AVI检测OD矩阵和移动众包OD矩阵;根据路网真实动态OD特性将其建模为图结构数据,基于移动众包OD矩阵构建日常动态OD关系图;利用投影法融合AVI检测OD矩阵和移动众包OD矩阵,生成先验OD矩阵,作为日常动态OD关系图内节点的特征表示;将日常动态OD关系图及对应的特征表示作为输入,以GCN模型作为卷积层构建编码器,通过全连接层进行解码,搭建基于图卷积神经网络的动态OD估计模型;计算模型总损失并根据模型总损失调整模型参数,得到训练好的动态OD估计模型。
Urban road network dynamic OD estimation method based on graph convolutional neural network
一种基于图卷积神经网络的城市路网动态OD估计方法
2025-01-10
Patent
Electronic Resource
Chinese
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