The invention discloses a traffic jam prediction method based on a tense graph convolutional neural network. The method comprises the steps of obtaining a data set image and performing preprocessing; a T-GCN-based prediction model is constructed and trained, the GCN maps the spatial features and relationships of the traffic flow between the observation stations into a graph, and the output of the GCN is input into a GRU model; the weight of each hidden state is calculated by using multilayer perception through a Softmax function, and congestion prediction is performed on a data set by using a trained T-GCN-based prediction module; and outputting a prediction result by using a full connection layer, and comparing the congestion prediction result. According to the invention, images of the road condition of the day are shot, the congestion condition of the current road is directly analyzed and the congestion condition of the subsequent road section is predicted in a graph convolution mode, and real-time traffic congestion prediction is carried out; through a mode of performing prediction through accumulation of congestion results, the effects of rapid response and accurate prediction can be realized, and the travel efficiency of the driver is satisfied.
本发明公开了一种基于时态图卷积神经网络的交通堵塞预测方法,获得数据集图像并进行预处理;构建基于T‑GCN的预测模型并进行训练,GCN将观测站之间交通流的空间特征和关系映射成一个图,将GCN的输出输入GRU模型;通过Softmax函数使用多层感知来计算每个隐藏状态的权重,采用训练后的基于T‑GCN的预测模对数据集进行拥堵预测;使用全连接层输出预测结果,对拥堵预测结果进行比对。本发明通过将当天的路况进行拍摄图像,并且通过图卷积的方式直接对当前道路的拥堵情况进行分析和预测后序路段的拥堵情况,进行实时的交通拥堵预测;通过拥堵结果的累积进行预测的方式,可以实现快速反应和精准预测的效果,满足了驾驶员的出行效率。
Traffic jam prediction method based on tense graph convolutional neural network
一种基于时态图卷积神经网络的交通堵塞预测方法
2023-08-22
Patent
Electronic Resource
Chinese
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