| 殷礼胜,吴洋洋.基于改进 VMD-GAT-GRU 的交通流量组合预测模型[J].电子测量与仪器学报,2022,36(7):62-72 |
| 基于改进 VMD-GAT-GRU 的交通流量组合预测模型 |
| Traffic flow combination prediction model based on improved VMD-GAT-GRU |
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| DOI： |
| 中文关键词: 交通流量预测 变分模态分解 互信息熵 图注意力网络 门控循环单元网络 |
| 英文关键词:traffic flow prediction variational mode decomposition mutual information entropy graph attention network gated recurrent
unit |
| 基金项目:国家自然科学基金（62073114,6207022417）、安徽省自然科学基金（JZ2021AKZR0344）项目资助 |
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| 中文摘要: |
| 针对短时交通流时间序列非平稳性、空间相关性和时间依赖性的特点,为提高短时交通流预测模型的预测精度和收敛
速度,该文提出了一种基于改进的变分模态分解(VMD)、图注意力(GAT)网络和门控循环单元(GRU)网络的交通流量组合预
测模型。 首先,利用互信息熵(MI)改进的变分模态分解算法,将交通流量时间序列分解成一系列调幅调频信号子序列,降低了
时序信号的非平稳性,提高后续预测模型的预测精度;然后,将其输入图注意力网络,捕捉路网邻近节点的交通流量对中心预测
节点交通流量不同程度的影响,从而实现交通流量序列的空间相关性建模,进一步提高模型预测精度;接着,将交通流量分量子
序列分别送入门控循环单元网络,捕捉其时间依赖性,并使用改进的 RMSPRop 优化算法迭代寻优,在提升优化算法收敛速度的
同时提高了模型的预测精度;最后,结合各分量子序列的预测值,作为预测模型的最终输出。 实验采用 RTMC 系统交通数据,结
果表明,该文提出的改进 VMD-GAT-GRU 时空融合组合预测模型相较于 LSTM、GCN 和 GAT 基准模型,平均绝对误差(MAE)分
别降低 9. 35、4. 12、4. 09,平均绝对百分比误差(MAPE)分别降低 16. 42%、7. 32%、8. 1%,优化算法的收敛速度和组合模型的预
测精度均得到有效提升。 |
| 英文摘要: |
| For the characteristics of non-stationarity, spatial correlation and temporal dependence of short-term traffic flow time series,
this paper proposes a combined prediction model of traffic flow based on improved variational mode decomposition ( VMD), graph
attention (GAT) network and gated recurrent unit (GRU) network to improve its prediction accuracy and convergence speed. First, the
variable mode decomposition algorithm improved by mutual information entropy (MI) is used to decompose the traffic flow time series
into a series of amplitude modulation and frequency modulation signal sub-sequences, which reduces the non-stationarity of the time
series signal and improves the prediction accuracy of the model. Then, they are sent to the graph attention network to capture the traffic
flow of adjacent nodes of the road network to different degrees on the traffic flow of the central prediction node, so as to realize the spatial
correlation modeling and further improve the prediction accuracy of the combined model. Next, the traffic flow component sub-sequences
are sent to the gated recurrent unit network separately to capture the temporal dependence of the traffic flow sequence, and use the
improved RMSPRop optimization algorithm to iteratively search for optimization, which not only improves the convergence speed of the
optimization algorithm, but also improves the prediction accuracy of the model. Finally, the prediction values of each component subsequences are combined as the final output of the prediction model. The experiment used traffic data from the RTMC system, the results
show that compared with LSTM, GCN and GAT baseline models, the mean absolute error (MAE) is reduced by 9. 35, 4. 12 and 4. 09,
respectively, and the mean absolute percentage error ( MAPE) is reduced by 16. 42%, 7. 32%, and 8. 1%, respectively. The
convergence speed of the optimization algorithm and the prediction accuracy of the combined model are effectively improved. |
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