The invention provides a traffic prediction method (MSTMA) based on a multi-scale spatio-temporal attention mechanism model, modeling spatio-temporal dependence is crucial to improvement of model prediction traffic state performance, most of existing methods only perform modeling on time dependence, the spatial feature expression ability of traffic data is insufficient, and the model prediction performance is poor. The invention aims to improve the spatial perception capability of a model on traffic data and the capture capability on time dependence. According to the method, adjacent matrixes of different scales are constructed and fused into a dynamic adjacent matrix through an attention mechanism, and dynamic spatial dependency is captured through a graph convolutional neural network. And meanwhile, the day time and week time characteristics of the original data are multiplied by the dynamic parameter matrix, so that the dynamic time dependence can be better captured, and the original data is fused with the time characteristics and the space characteristics by utilizing a multi-head attention mechanism, so that the method has the capability of better representing the real data space-time level.
本发明提供了一种基于多尺度时空注意力机制模型的交通预测方法(MSTMA),建模时空依赖性对模型预测交通状态性能的提升至关重要,现有的方法多数只对时间依赖性进行建模,对交通数据的空间特征表达能力不足,为了提高模型对交通数据的空间感知能力以及对时间依赖性的捕获能力。本发明通过构建不同尺度的邻接矩阵并通过注意力机制融合为动态邻接矩阵,并通过图卷积神经网络捕获动态空间依赖性。同时将原始数据的天时间以及周时间特征与动态参数矩阵相乘使得能够更好的捕获动态时间依赖性,并利用多头注意力机制分别将原始数据与时间特征和空间特征融合,使其具有更好表示真实数据时空层面的能力。
Traffic prediction method based on multi-scale space-time attention mechanism
基于多尺度时空注意力机制的交通预测方法
2025-02-25
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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