The invention relates to the technical field of traffic engineering. The invention discloses a road traffic state prediction method and device based on a graph attention network, which can improve the accuracy of traffic flow prediction under the condition of special events. The method comprises the following steps: acquiring historical traffic sequence information; performing event coding processing on the historical traffic sequence information to obtain space-time embedding representation; performing feature extraction processing on the spatio-temporal embedding representation to obtain historical spatio-temporal features; performing fine-grained hierarchical modeling processing on the historical spatiotemporal features to obtain a two-step spatiotemporal diagram and a three-step spatiotemporal diagram; performing conversion processing on the historical spatial-temporal characteristics, the two-step spatial-temporal diagram and the three-step spatial-temporal diagram to obtain a first time scale spatial-temporal diagram, a second time scale spatial-temporal diagram and a third time scale spatial-temporal diagram; and decoding the first time scale space-time diagram, the second time scale space-time diagram and the third time scale space-time diagram to obtain predicted traffic flow information.
本发明涉及交通工程技术领域。本发明公开了一种基于图注意网络的道路交通状态预测方法及装置,其能够在特殊事件的情况下提高交通流预测的准确性。所述方法包括获取历史交通序列信息;对所述历史交通序列信息进行事件编码处理,获得时空嵌入表示;对所述时空嵌入表示进行特征提取处理,获得历史时空特征;对所述历史时空特征进行细粒度的分层建模处理,得到双步时空图和三步时空图;将所述历史时空特征、所述双步时空图和所述三步时空图进行转换处理,得到第一时间尺度时空图、第二时间尺度时空图和第三时间尺度时空图;将所述第一时间尺度时空图、所述第二时间尺度时空图和所述第三时间尺度时空图进行解码处理,获得预测交通流信息。
Road traffic state prediction method and device based on graph attention network
基于图注意网络的道路交通状态预测方法及装置
2025-03-14
Patent
Electronic Resource
Chinese
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