| 殷礼胜,孙双晨,魏帅康,田帅帅,何怡刚.基于自适应 VMD-Attention-BiLSTM 的
交通流组合预测模型[J].电子测量与仪器学报,2021,35(7):130-139 |
| 基于自适应 VMD-Attention-BiLSTM 的
交通流组合预测模型 |
| Traffic flow combination prediction model based on adaptiveVMD-attention-BiLSTM |
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| DOI： |
| 中文关键词: 短时交通流预测 自适应变分模态分解 双向长短时记忆网络 注意力机制 |
| 英文关键词:short-term traffic flow prediction adaptive variational modal decomposition bi-directional long-term memory network attention mechanism |
| 基金项目:国家自然科学基金（62073114，61673153，51637004）项目资助 |
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| 中文摘要: |
| 针对短时交通流量序列的非平稳性和随机性的特征,为提高短时交通流预测精度和收敛速度,提出一种基于自适应变
分模态分解(VMD)和结合注意力机制层的双向长短时记忆网络(BiLSTM)的组合预测模型。 首先,使用自适应变分模态分解
将时空交通流量序列分解为一系列有限带宽模态分量,细化了交通流信息,降低了非平稳性,提升了建模的精确度;其次,利用
结合注意力机制的双向长短时记忆网络挖掘分解后交通流量序列中的时空相关性,从而揭示其时空变化规律,从而进一步提升
了建模精确度,并且利用改进 Adam 算法进行网络权值优化,以加速了预测网络的训练收敛速度;最后,将各模态分量预测值叠
加求和作为最终交通流预测值。 实验结果表明,使用模态分解的预测模型预测性能明显优于未使用模态分解的预测模型,同时
自适应 VMD-Attention-BiLSTM 预测模型相较于 EEMD-Attention-BiLSTM 预测模型,均方根误差降低了 47. 1%,该组合预测模型
提升了预测精度,并且能够快速预测交通流量时间序列。 |
| 英文摘要: |
| In view of the non-stationary and random characteristics of the short-term traffic flow sequence, in order to improve the shortterm traffic flow prediction accuracy and model training speed, this paper proposes a combined prediction model based on adaptive
variational modal decomposition (VMD) and bi-directional long-term memory network (BiLSTM) combined with attention mechanism.
Firstly, the spatial and temporal traffic flow sequence is decomposed by the adaptive VMD method to a series of modal components with
limited bandwidth, which can refine the traffic flow information, reduce non-stationarity, and improve the accuracy of modeling.
Secondly, the spatio-temporal correlation in the short-time traffic flow sequence after decomposition is mined by BiLSTM combined with
attention mechanism to reveal its spatio-temporal variation rules, which further improves the modeling accuracy. In addition, in order to
accelerate the training convergence speed of the prediction network, the network weight optimization is carried out by the improved Adam
algorithm. Finally, the predicted value of each modal component is superimposed as the predicted value of the final traffic flow prediction
value. The experimental results show that the prediction performance of the model using modal decomposition is obviously better than that
of the model without modal decomposition, and the RMSE of the self-adaptive VMD-Attention-BiLSTM prediction model is reduced by
47. 1% compared with that of the EEMD-attention-BiLSTM prediction model. The combined prediction model improves the prediction
accuracy and can quickly predict the traffic flow time series. |
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