The invention provides a traffic flow prediction and prediction model training method, and relates to the technical field of artificial intelligence such as automatic driving, big data, cloud service and intelligent traffic. The traffic flow prediction method comprises the following steps: acquiring a historical traffic flow set corresponding to a target lane; obtaining a target feature data set corresponding to the target lane according to the at least one historical moment; obtaining a target flow trend set and a target flow residual set corresponding to the target lane according to the historical traffic flow set; obtaining a target fusion data set corresponding to the target lane according to the target feature set, the target traffic trend set and the target traffic residual set; and obtaining a target traffic flow prediction result corresponding to the target lane according to the target fusion data set. The prediction model training method comprises the steps of obtaining a training data set; constructing a neural network model comprising a time sequence decomposition module, a fusion module and a multi-feature cooperation module; and training the neural network model by using the training data set to obtain a prediction model.
本公开提供一种交通流量的预测与预测模型的训练方法,涉及自动驾驶、大数据、云服务、智慧交通等人工智能技术领域。交通流量的预测方法包括:获取与目标车道对应的历史交通流量集;根据至少一个历史时刻,得到与目标车道对应的目标特征数据集;根据历史交通流量集,得到与目标车道对应的目标流量趋势集与目标流量残差集;根据目标特征集、目标流量趋势集与目标流量残差集得到与目标车道对应的目标融合数据集;根据目标融合数据集得到与目标车道对应的目标交通流量预测结果。预测模型的训练方法包括:获取训练数据集;构建包括时间序列分解模块、融合模块与多特征协同模块的神经网络模型;使用训练数据集对神经网络模型进行训练,得到预测模型。
Traffic flow prediction and prediction model training method and device
交通流量的预测与预测模型的训练方法、装置
18.04.2025
Patent
Elektronische Ressource
Chinesisch
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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