基于图卷积网络的短时交通速度预测

交通预测是构建智能交通系统的重要技术，实时准确的交通预测有利于规划路线，提高出行效率。为提高交通速度预测精度，提出一种基于图卷积网络的短时交通速度预测模型。首先对交通速度数据进行时空特征分析，然后结合数据空间特性构造可学习的邻接矩阵来建立图卷积网络，同时考虑到交通数据的时间特性，因此在图卷积的基础上又添加了长短期记忆网络和注意力机制来共同构建预测模型。实验结果表明由于同时考虑了交通速度数据的时空特性，本文模型均方根误差、平均绝对误差和平均绝对百分比误差均小于传统模型和单个模型，验证了提出的模型预测精确度更高。

Short-term Traffic Speed Prediction Based on Graph Convolutional Network

Traffic forecasting is an important technology for building intelligent transportation systems. Real-time and accurate traffic forecasting is beneficial to route planning and improve travel efficiency. In order to improve the accuracy of traffic speed prediction, the article proposes a short-term traffic speed prediction model based on graph convolutional network. Firstly, the spatial and temporal characteristics of the traffic speed data are analyzed, and then the learnable adjacency matrix is constructed in combination with the data space characteristics to establish the graph convolution network. At the same time, considering the time characteristics of the traffic data, the long-term and short-term memory network and attention mechanism are added on the basis of graph convolution to jointly construct the prediction model. The experimental results show that due to the consideration of the temporal and spatial characteristics of traffic speed data, the root mean square error, average absolute error and average absolute percentage error of this model are all smaller than the traditional model and the single model, which verifies that the proposed model has higher prediction accuracy.