The invention discloses a multi-level embedded traffic flow space-time prediction method based on a pre-training large language model. A traffic flow space-time prediction model comprises a space-time embedding layer, a multi-layer time coding network, a word segmentation alignment layer, the pre-training large language model and a traffic flow decoding layer. The method comprises the following steps: constructing and capturing a complex dynamic space-time relationship of traffic flow through adaptive traffic flow space-time embedding and a multi-layer time coding network; a traffic flow node token and a prompt text token which describe time semantic information through node dimension word segmentation are aligned and combined through a word segmentation alignment layer; and finally, inputting the multi-level embedding integrated with the historical features, the dynamic spatio-temporal features and the prompt text features into a pre-training large language model, and finishing a downstream prediction task by training the large language model. According to the method, the prediction precision can be improved, and trainable parameters can be reduced.
本发明公开一种多层次嵌入的基于预训练大语言模型的交通流时空预测方法,其中的交通流时空预测模型包括时空嵌入层、多层时间编码网络、分词对齐层、预训练大语言模型和交通流解码层;通过自适应的交通流时空嵌入和多层时间编码网络,构建并捕获交通流复杂的动态时空关系;通过分词对齐层将以节点维度分词描述时间语义信息的交通流节点tokens与提示文本tokens对齐结合;最后,将融合了历史特征、动态时空特征和提示文本特征的多层次嵌入输入预训练大语言模型,通过训练大语言模型完成下游预测任务。本发明的方法能够提高预测精度,并减少可训练参数。
Multi-level embedded traffic flow space-time prediction method based on pre-training large language model
一种多层次嵌入的基于预训练大语言模型的交通流时空预测方法
03.01.2025
Patent
Elektronische Ressource
Chinesisch
IPC: | G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung |
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