The invention discloses a neural tensor ring fusion method for traffic flow data completion, which belongs to the field of traffic data completion, and comprises the following steps: step 1, acquiring traffic flow data in a road network system, and modeling the traffic flow data into a third-order tensor; 2, constructing an objective function of a neural tensor ring fusion model by referring to a tensor ring decomposition thought; and step 3, traffic flow data completion is carried out based on the neural tensor ring fusion model. According to the method, a neural tensor ring fusion model is constructed by adopting a tensor ring decomposition thought, potential features of data are learned by adopting a three-order factor tensor form, and the structure and information of original data can be better stored while high-order data tensor is expressed and processed; meanwhile, multi-dimensional interaction of traffic data in time is captured by adopting a convolutional long-short-term memory network, and missing data are complemented together by using a plurality of matrixes containing historical time characteristics.
本发明公开了一种用于交通流量数据补全的神经张量环融合方法,属于交通数据补全领域,包括如下步骤:步骤1、获取路网系统中的交通流量数据,将交通流量数据建模为三阶张量;步骤2、参考张量环分解思想构建神经张量环融合模型的目标函数;步骤3、基于神经张量环融合模型进行交通流量数据补全。本发明采用张量环分解思想构建了神经张量环融合模型,采用三阶因子张量的形式学习数据的潜在特征,在表达与处理高阶数据张量的同时可以更好的保存原始数据的结构与信息;同时,采用卷积长短期记忆网络捕获交通数据在时间上的多维交互,使用包含历史时间特征的多个矩阵共同补全缺失数据。
Neural tensor ring fusion method for traffic flow data completion
一种用于交通流量数据补全的神经张量环融合方法
2025-01-03
Patent
Electronic Resource
Chinese
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