The invention discloses an urban short-term traffic flow prediction method, system and device, and relates to the field of intelligent traffic, and the method comprises the steps: building a space-time model based on a deep meta-learning method according to historical traffic flow road environment data; the spatial-temporal model is a meta-learning graph fused convolutional loop network; the meta-learning graph fusion convolution loop network comprises a meta-learning graph fusion convolution module and a meta-learning recurrent neural module which are respectively used for capturing spatial features of the road network and time features of the road network in a dynamic fluctuation time sequence; utilizing a meta-learning node library to optimize an adjacent matrix generated by self-learning node features and traffic contexts in graph structure data based on a meta-learning unit and capturing spatial information of a global road network through a fusion gate mechanism in the spatial-temporal model; and predicting the traffic speed information of the prediction area by using the optimized space-time model. According to the invention, the accuracy and efficiency of urban traffic flow prediction in a large-scale network connection mixed environment can be improved.
本发明公开一种城市短期交通流预测方法、系统及设备,涉及智能交通领域,该方法包括根据历史的交通流道路环境数据,基于深度元学习方法,构建时空模型;时空模型为元学习图融合卷积循环网络;元学习图融合卷积循环网络包括:元学习图融合卷积模块和元学习循环神经模块,分别用于捕捉道路网络的空间特征和针对道路网路在动态波动时序下的时间特征;利用元学习节点库优化时空模型中基于元学习单元对图结构数据中的节点特征和交通上下文进行自学习及通过融合门机制捕捉全局路网的空间信息生成的邻接矩阵;利用优化后的时空模型对预测区域的交通速度信息进行预测。本发明能够提高在大规模网联混行环境下对城市交通流预测的准确性和效率。
Urban short-term traffic flow prediction method, system and equipment
一种城市短期交通流预测方法、系统及设备
2024-05-28
Patent
Electronic Resource
Chinese
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