龚循强, 邱万锦, 吕开云, 张通, 张瑞, 罗升. 变分模态分解与自适应图卷积门控循环网络的交通流量组合预测模型[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230249
引用本文: 龚循强, 邱万锦, 吕开云, 张通, 张瑞, 罗升. 变分模态分解与自适应图卷积门控循环网络的交通流量组合预测模型[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230249
GONG Xunqiang, QIU Wanjin, LÜ Kaiyun, ZHANG Tong, ZHANG Rui, LUO Sheng. A Combined Traffic Flow Prediction Model Based on Variational Mode Decomposition and Adaptive Graph Convolutional Gated Recurrent Network[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230249
Citation: GONG Xunqiang, QIU Wanjin, LÜ Kaiyun, ZHANG Tong, ZHANG Rui, LUO Sheng. A Combined Traffic Flow Prediction Model Based on Variational Mode Decomposition and Adaptive Graph Convolutional Gated Recurrent Network[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230249

变分模态分解与自适应图卷积门控循环网络的交通流量组合预测模型

A Combined Traffic Flow Prediction Model Based on Variational Mode Decomposition and Adaptive Graph Convolutional Gated Recurrent Network

  • 摘要: 针对交通流量预测中预定义静态图结构难以包含全局时空信息和非平稳性的问题,提出一种结合变分模态分解( VMD)和自适应图卷积门控循环网络(AGCGRN)的交通流量预测模型。该模型首先利用VMD算法将原始交通流量序列数据分解成一系列调幅调频的本征模态函数分量,降低序列的非平稳性。然后引入自适应邻接矩阵建立自适应图卷积网络,并将自适应图卷积嵌入门控循环单元中,设计一种AGCGRN,实现自适应学习每个节点交通流量的时空特性。最后利用AGCGRN模型对各分量进行预测,并将各分量的预测值重构得到最终的预测值。采用PeMS04数据集进行实验验证,并与7种模型进行对比。实验结果表明,所提模型相较其他模型在预测性能上均有不同程度的提升,相比次优的模型在平均绝对误差、均方根误差和平均绝对百分比误差上均降低33%以上。为了评估所提模型中各个组件的有效性,将所提模型与各个消融模型进行对比,结果表明所提模型相比各个消融模型在以上3个评价指标上均降低15%以上,说明该模型在交通流量预测中的可行性和优越性。

     

    Abstract: Objectives: Aiming at the problem that the pre-defined static graph structure cannot contain global spatiotemporal information and non-stationarity in traffic flow prediction, a traffic flow prediction model combining variational mode decomposition (VMD) and adaptive graph convolutional gated recurrent network (AGCGRN) is proposed. Methods: First, the model first uses the VMD algorithm to decompose the original traffic flow sequence data into a series of amplitude modulated and frequency modulated intrinsic mode function components, reducing the non-stationary nature of the sequence. Then, an adaptive adjacency matrix is introduced to establish an adaptive graph convolutional network, and the adaptive graph convolution is embedded in the entry-level control loop unit. An AGCGRN is designed to achieve adaptive learning of the spatiotemporal characteristics of traffic flow at each node. Finally, the AGCGRN model is used to predict each component, and the predicted values of each component are reconstructed to obtain the final predicted values. Results: Experimental validation was conducted using the PeMS04 dataset and compared with seven models: HA, SVR, LSTM, GRU, STGCN, ASTGCN, and STSGCN. (1) In the one-step prediction experiment, the proposed VMD-AGCGRN model had the highest prediction accuracy among all models, and compared to the suboptimal STSGCN model, it reduced the evaluation indexes of MAE, RMSE and MAPE by 33.266%, 36.166%, and 33.443%, respectively. (2) In multi-step prediction experiments, the proposed VMD-AGCGRN model consistently outperformed other comparative models in predicting results at different times, and its growth was relatively stable. Compared with the suboptimal model, the MAE, RMSE and MAPE of the predicted results at 15 minutes, 30 minutes, and 60 minutes decreased by 30.659%/34.106%/31.397%, 28.597%/32.809%/28.474%, and 24.846%/30.765%/20.589%, respectively. (3) In order to evaluate the effectiveness of various components in the proposed model, each ablation model was compared with the proposed model. Compared with the VMD-GCGRN model without adaptive graph structure, the proposed model reduced the MAE, RMSE, and MAPE evaluation indicators by 15.110%, 13.930%, and 32.358%, respectively. Compared with the AGCGRN model without VMD module, the proposed model reduced the MAE, RMSE, and MAPE evaluation indexes by 26.794%, 31.553%, and 32.802%, respectively. Overall, the proposed model has higher prediction accuracy compared to all ablation models, indicating that the introduction of VMD and adaptive graph structure has a positive contribution to predicting traffic flow. Conclusions: The VMD-AGCGRN model can effectively extract the spatiotemporal characteristics of traffic flow and reduce the impact of non-stationary factors, improve the accuracy of traffic flow prediction, and has certain feasibility and superiority. In traffic flow prediction, the graph convolutional network optimized by adaptive graph structure learning can better learn the spatial correlation characteristics of the traffic network, and the introduction of VMD can improve the prediction accuracy of traffic flow.

     

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