The invention relates to the field of intelligent traffic, in particular to a time-space diagram transfer learning-based traffic flow prediction method, which comprises the steps of constructing and training a TL-STGCN model, obtaining traffic flow data to be processed, inputting the traffic flow data to the trained TL-STGCN model, and outputting a prediction result. The TL-STGCN model comprises a space-time diagram convolutional network, a domain discriminator module and a prediction module; according to the method, the traffic flow characteristics of the source road network with sufficient data are combined to assist in predicting the future traffic flow of the target road network with scarce data, and a result shows that for a traffic road network prediction task with scarce data, the TL-STGCN has better prediction performance than an existing baseline model.
本发明涉及智能交通领域,具体涉及一种基于时空图迁移学习的交通流预测方法,包括构建并训练TL‑STGCN模型,获取待处理交通流数据输入训练好的TL‑STGCN模型,输出预测结果;所述TL‑STGCN模型包括时空图卷积网络、域判别器模块和预测模块;本发明结合数据充足的源路网的交通流特征,辅助预测数据稀缺的目标路网未来交通流,结果表明,对于数据稀缺的交通路网预测任务,TL‑STGCN比现有基线模型具有更好的预测性能。
Traffic flow prediction method based on space-time diagram transfer learning
一种基于时空图迁移学习的交通流预测方法
2023-10-27
Patent
Elektronische Ressource
Chinesisch
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06Q Datenverarbeitungssysteme oder -verfahren, besonders angepasst an verwaltungstechnische, geschäftliche, finanzielle oder betriebswirtschaftliche Zwecke, sowie an geschäftsbezogene Überwachungs- oder Voraussagezwecke , DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES |
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