The invention relates to a traffic flow prediction method based on a long and short term space-time diagram neural network, and belongs to the technical field of computers. The method comprises the following steps: inputting traffic flow data of a long time sequence, dividing the long time sequence into non-overlapping subsequences with equal length, and performing mask reconstruction on the subsequences in order to improve the operation efficiency of a model; the processed subsequences are respectively sent to a long-term feature extractor, a short-term feature extractor and a periodic feature extractor, the extracted features are spliced, and finally the spliced features are further processed through a multi-layer perceptron to obtain a final prediction result. According to the method, a subsequence becomes a basic unit of feature extraction, and a mask mechanism is introduced, so that the model can learn compressed subsequence representation with rich context information from a long-time sequence. Feature extraction is carried out through stacking multilayer expansion causal convolution, trend features in a long-time sequence are captured, and the problem of gradient disappearance in a traditional prediction algorithm is avoided.
本发明涉及一种基于长短期时空图神经网络的交通流预测方法,属于计算机技术领域。该方法为:输入长时间序列的交通流数据,将长时间序列划分成等长的非重叠的子序列,为了提高模型的运行效率,对子序列进行掩码重构;接着将处理后的子序列分别送入长期特征提取器、短期特征提取器与周期特征提取器,再将提取到的特征进行拼接,最终通过多层感知机进一步处理这些拼接的特征,得到最后的预测结果。该方法让子序列成为特征提取的基本单位,并引入掩码机制,使得模型使模型可以从长时间序列中学习压缩的、上下文信息丰富的子序列表示。通过堆叠多层扩张因果卷积进行特征提取,捕捉长时间序列中的趋势特征,避免传统预测算法中面临的梯度消失问题。
Traffic flow prediction method based on long and short term space-time diagram neural network
一种基于长短期时空图神经网络的交通流预测方法
2025-01-24
Patent
Electronic Resource
Chinese
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