The invention relates to the field of traffic flow prediction, in particular to an airport traffic flow prediction method based on a graph neural network. The method comprises the following steps: firstly, acquiring airport traffic flow observation data, airport aviation meteorological data, air route network data, flight time data and flight flight duration data between airport pairs; a multi-relation airport network is constructed through airport route network data, flight time data and flight flight duration data between airport pairs, and an airport traffic flow data set is constructed according to airport traffic flow observation data and aviation meteorological data; fusing the multi-relation airport network into a directed single-relation airport network by applying an attention mechanism; extracting spatial structure characteristics of a plurality of airport traffic networks by using a bidirectional GCN model; and the model is embedded into a GRU model, spatial-temporal correlation of airport traffic flow data is extracted, and an airport traffic flow prediction model is constructed. According to the method, airport traffic flow prediction in a large-range and multi-airport scene is realized, and the airport traffic flow prediction precision is improved.
本发明涉及交通流预测领域,具体涉及一种基于图神经网络的机场交通流预测方法。首先,获取机场交通流观测数据、机场航空气象数据、航路网络数据、航班时刻数据以及机场对之间航班飞行时长数据;通过机场航路网络数据、航班时刻数据以及机场对之间航班飞行时长数据构建多关系机场网络,根据机场交通流观测数据和航空气象数据构建机场交通流数据集;应用注意力机制将多关系机场网络融合为有向的单关系机场网络;使用双向GCN模型提取多个机场交通网络的空间结构特征;将该模型嵌入到GRU模型,提取机场交通流数据的时空相关性,构建机场交通流预测模型。本发明实现大范围、多机场情景的机场交通流预测,提高机场交通流量预测精度。
Airport traffic flow prediction method based on graph neural network
一种基于图神经网络的机场交通流预测方法
2023-04-14
Patent
Electronic Resource
Chinese
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