The invention provides a method for traffic flow prediction through a course learning guided space-time network, and the method comprises the steps: employing a space-time network model, introducing a course learning module to evaluate the prediction difficulty of nodes in a traffic network, employing a training scheduler to gradually introduce training nodes from easy to difficult for the space-time network model, so as to improve the prediction capability; and then, a global space-time encoder with multiple space-time attention mechanisms is adopted, and the problem that original semantic information is diluted in a layer-by-layer transmission process is relieved through an interlayer residual scaling technology, so that global space-time correlation of nodes is captured, and traffic flow prediction is realized.
本发明提供一种课程学习引导的时空网络用于交通流预测的方法,采用的时空网络模型通过引入课程学习模块,以评估交通网络中节点的预测难度,并采用训练调度器逐步为所述时空网络模型引入由易到难的训练节点,以提高预测能力;然后采用具有多头时空注意力机制的全局时空编码器,通过层间残差缩放技术缓解原始语义信息在逐层传递过程中被稀释的问题,以捕捉节点的全局时空相关性,从而实现交通流预测。
Method for traffic flow prediction by using space-time network guided by curriculum learning
一种课程学习引导的时空网络用于交通流预测的方法
2025-02-25
Patent
Electronic Resource
Chinese
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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