The invention provides a traffic state prediction and traceability method, device and equipment based on multi-modal data, which can be applied to the field of space-time prediction and traffic planning and management. The method comprises the steps that a topological graph representing a road connection relation and multi-mode traffic sequence data of a target historical time period are acquired, the topological graph comprises road nodes and edge relations, and the road nodes represent roads; processing each traffic sequence data and the topological graph by using a graph neural network to obtain traffic spatial-temporal characteristics; the traffic spatial-temporal features corresponding to the traffic sequence data are processed based on a cross attention network, a congestion prediction result is obtained, and the congestion prediction result represents whether the road nodes are congested or not; and processing the congestion prediction result based on a Shapley value algorithm to obtain a traceability result, the Shapley value algorithm being used for calculating a contribution value of each traffic sequence data to the congestion prediction result.
本发明提供了一种基于多模态数据的交通状态预测及溯源方法、装置和设备,可以应用于时空预测及交通规划与管理领域。该方法包括:获取表征道路连接关系的拓扑图以及目标历史时段的多模态的交通序列数据,其中,拓扑图包括道路节点和边关系,道路节点表征道路;利用图神经网络处理每个交通序列数据和拓扑图,得到交通时空特征;基于交叉注意力网络处理多个交通序列数据各自对应的交通时空特征,得到拥堵预测结果,拥堵预测结果表征道路节点是否发生拥堵;基于沙普利值算法处理拥堵预测结果,得到溯源结果,沙普利值算法用于计算每个交通序列数据对拥堵预测结果的贡献值。
Traffic state prediction and tracing method, device and equipment based on multi-modal data
基于多模态数据的交通状态预测及溯源方法、装置和设备
2025-03-28
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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