The invention discloses a graph neural network traffic flow prediction method and system based on an attention mechanism. The method comprises the steps of obtaining to-be-predicted urban traffic flowdata; constructing a road network map according to the road connection relationship; preprocessing the urban traffic flow data to be predicted; and inputting the road network map and the preprocessedresult into a pre-trained neural network based on an attention mechanism, and finally outputting a prediction result of the urban traffic flow. Roads and checkpoints are encoded according to the roadnetwork information, a road network graph structure is established according to the upstream and downstream relationship of the roads, vehicle passing data of the checkpoints is counted under different time dimensions nd summarized to form a road network traffic flow data table; a graph neural network formed by stacking multiple layers of attention modules is constructed, a time sequence attention mechanism and the graph attention network are used for modeling the traffic flow in the whole road network, and the future traffic flow condition of a specified checkpoint is predicted.
本发明公开了基于注意力机制的图神经网络交通流量预测方法及系统,获取待预测的城市交通流量数据;根据道路连接关系构建路网图;对待预测的城市交通流量数据进行预处理;将路网图和预处理后的结果,输入到预训练的基于注意力机制的神经网络中,最后输出城市交通流量的预测结果。根据路网信息对道路和卡口进行编码,并根据道路上下游关系建立路网图结构,统计卡口不同时间维度下的过车数据,汇总形成路网车流量数据表;构建有多层注意力模块堆叠组成的图神经网络,使用时序注意力机制和图注意力网络对整个路网中车流量进行建模,预测指定卡口未来的车流量情况。
Graph neural network traffic flow prediction method and system based on attention mechanism
基于注意力机制的图神经网络交通流量预测方法及系统
2020-05-15
Patent
Elektronische Ressource
Chinesisch
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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