The invention provides a traffic flow prediction method based on a dual dynamic space-time automatic encoder network, and relates to the technical field of traffic flow prediction. Comprising the following steps: designing an automatic encoder for learning an intrinsic mode from traffic flow data, and representing a current traffic flow information code through low dimension; modeling is carried out on the dynamic characteristics of the traffic flow graph nodes, and a dual dynamic space-time coding network (DDSTAEN) framework is established; converting the traffic flow graph into a dual hypergraph, and capturing dynamic spatio-temporal characteristics of edges; projecting the current hidden state to a future hidden state to realize traffic flow prediction; a prediction loss function is obtained by training a dual dynamic space-time automatic encoder. The invention provides a dual dynamic space-time coding network (DDSTAEN) framework. According to the framework, a dual dynamic space-time convolutional network is adopted to learn and identify an internal mode and a hidden state in existing traffic flow data, and a predicted hidden state is utilized to reconstruct a future traffic flow dynamic state.
本发明提供一种基于对偶动态时空自动编码器网络的交通流预测方法,涉及交通流预测的技术领域。包括以下步骤:设计用于从交通流数据中学习内在模式的自动编码器,并将当前的交通流信息编码通过低维进行表示;对交通流图节点的动态特征进行建模,建立对偶动态时空编码网络DDSTAEN框架;将交通流图转换为对偶超图,捕获边的动态时空特征;将当前隐藏状态投影到未来隐藏状态,实现交通流的预测;通过对偶动态时空自动编码器来训练,获取预测损失函数。本发明提出了一种对偶动态时空编码网络(DDSTAEN)框架。该框架采用对偶动态时空卷积网络来学习和识别现有交通流数据中的内在模式和隐藏状态,并利用预测的隐藏状态来重构未来的交通流动态。
Traffic flow prediction method based on dual dynamic space-time automatic encoder network
一种基于对偶动态时空自动编码器网络的交通流预测方法
2024-12-06
Patent
Electronic Resource
Chinese
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