The invention discloses a highway lane-level speed prediction method based on multi-source data fusion, and the method comprises the following steps: dividing lanes, and calculating the mutual influence degree between different lanes through GRG; using an Attention mechanism to calculate attention weights of the target lane at different times; establishing an improved model GRGA based on the attention weight and the mutual influence degree between different lanes; based on the improved model GRGA, establishing a GRULSTM fusion deep learning model; and according to the GRULSTM fusion deep learning model, predicting the average driving speed of any lane of the expressway in a specific time period in real time. The highway lane-level speed prediction method based on multi-source data fusion is fast in speed prediction and high in precision, and can provide a reference basis for traffic guidance of traffic control personnel.
本发明公开了一种基于多源数据融合的高速公路车道级速度预测方法,包括以下步骤:划分车道,利用GRG计算不同车道之间的相互影响度;使用Attention机制计算目标车道不同时间的注意力权重;基于注意力权重和不同车道之间的相互影响度,建立改进模型GRGA;基于改进模型GRGA,建立GRU_LSTM融合深度学习模型;根据GRU_LSTM融合深度学习模型,实时预测高速公路任一车道特定时间段内的平均行驶速度。本发明一种基于多源数据融合的高速公路车道级速度预测方法预测速度块、精准高,能够为交通管制人员进行交通诱导提供参考依据。
Expressway lane-level speed prediction method based on multi-source data fusion
一种基于多源数据融合的高速公路车道级速度预测方法
2023-11-07
Patent
Elektronische Ressource
Chinesisch
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