The invention provides a high-speed traffic flow prediction method based on long and short period Transform fusion trend and seasonal characteristics, and belongs to the field of data unsupervised prediction. The invention provides a high-speed traffic flow prediction method based on long and short period Transform fusion trend and seasonal characteristics, aiming at the problems that time-space relevance exists among variables in traffic flow data and short-term and long-term information cannot be fully combined when a traditional method is used for processing the data, and the invention provides the high-speed traffic flow prediction method based on the long and short period Transform fusion trend and seasonal characteristics. According to the method, a Transform attention mechanism is innovatively combined with diffusion convolution, the spatial-temporal correlation of short-period data is deeply mined, meanwhile, the trend of Transform is fused with seasonal characteristics, and the long-term dependency relationship in different periods is effectively captured. And finally, fusing the prediction results of different period data by using three weight moments, and fully considering the characteristics of short-term data and long-term data. When the network model is applied to high-speed traffic flow prediction, the prediction accuracy can be improved, and the effectiveness of the method is verified by experimental results.
本发明提出一种基于长短周期Transformer融合趋势与季节特征的高速交通流预测方法,属于数据无监督预测领域。针对交通流数据中变量间存在的时空关联性,以及传统方法在处理数据时未能充分结合近期与远期信息的问题,提出一种基于长短周期Transformer融合趋势与季节特征的高速交通流预测方法。本发明创新性地将Transformer注意力机制与扩散卷积相结合,深入挖掘短周期数据的时空相关性,同时将Transformer的趋势与季节特征相融合,有效捕获不同周期内的长期依赖关系。最后,通过利用3个权重矩对不同周期数据的预测结果进行融合,该方法充分考虑了短期数据与长期数据的特点。将所提的网络模型应用于高速交通流预测,能够提高预测准确度,实验结果验证了该方法的有效性。
High-speed traffic flow prediction method based on long-short period Transform fusion trend and seasonal characteristics
基于长短周期Transformer融合趋势与季节特征的高速交通流预测方法
28.02.2025
Patent
Elektronische Ressource
Chinesisch
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