The invention discloses an urban traffic flow prediction method and system fusing spatio-temporal characteristics and a large language model, and the system comprises a spatio-temporal compiler module and a spatio-temporal adapter module. The space-time compiler module extracts traffic flow features by using time step embedding, time embedding day and space embedding and performs feature fusion to obtain a token in which a space-time mode is packaged about traffic flow data; and the space-time adapter module allows the model to keep the language processing strength while adapting to the dynamic state of the traffic flow by freezing a pre-trained Bert model Transform block, and fuses the obtained token with the pre-trained BERT model Transform block to obtain the final prediction of the traffic flow. According to the method, spatial and temporal features are combined with a large language model, and spatial and temporal modes in traffic data are modeled by using the advanced learning ability of the LLM, so that highly accurate prediction is realized.
本发明公开了一种融合时空特征与大语言模型的城市交通流预测方法及系统,包括时空编译器模块和时空适配器模块,所述时空编译器模块通过使用时间步嵌入、时间嵌入天和空间嵌入来提取交通流量特征并进行特征融合得到关于交通流数据封装了时空模式的令牌;所述时空适配器模块通过冻结预训练Bert模型Transformer块,允许模型在适应交通流动态的同时保留其语言处理强度,将得到的令牌与预训练的BERT模型Transformer块融合得到交通流量最终预测。本发明将时空特征与大语言模型相结合,利用LLM的高级学习能力对交通数据中的空间和时间模式进行建模,从而实现高度准确的预测。
Urban traffic flow prediction method and system fusing spatial-temporal characteristics and large language model
融合时空特征与大语言模型的城市交通流预测方法及系统
18.03.2025
Patent
Elektronische Ressource
Chinesisch
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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