| 殷礼胜,魏帅康,孙双晨,何怡刚.基于 FEEMD-SAPSO-BiLSTM 组合模型的
短时交通流预测[J].电子测量与仪器学报,2021,35(10):72-81 |
| 基于 FEEMD-SAPSO-BiLSTM 组合模型的
短时交通流预测 |
| Short-term traffic flow forecast based on FEEMD-SAPSO-BiLSTMcombined model |
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| DOI： |
| 中文关键词: 短时交通流 快速集合经验模态分解 自然选择自适应变异粒子群 双向长短时记忆网络 |
| 英文关键词:short-term traffic fast ensemble empirical model decomposition selection adaptive particle swarm optimization bidirection
long short-term memory |
| 基金项目:国家自然科学基金资助项目（62073114，51577046，61673153）、省基金项目（JZ2021AKZR0344）资助 |
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| 中文摘要: |
| 为了提高短时交通流的预测精度和预测速度,基于交通流量序列的不平稳性和随机性,提出了快速集合经验模态分解
(fast ensemble empirical mode decomposition, FEEMD) 和自然选择自适应变异粒子群算法 ( selection adaptive particle swarm
optimization,SAPSO)优化双向长短时记忆网络( bidirection long short-term memory,BiLSTM) 相结合的预测模型。 首先,利用
FEEMD 将原始不平稳的交通流量序列分解成多个较平稳的固有模态分量( intrinsic mode function,IMF)和残差分量( resdiue,
Res),并滤除掉噪声部分,提高建模精度;其次,引入复合多尺度排列熵( composite multiscale permutation entropy,CMPE)检测交
通流量子序列的随机性并根据随机性的相近程度对其进行聚类重组,简化模型的构建,提高预测精度;然后,对重组后的子序列
使用 BiLSTM 进行预测,并利用 SAPSO 优化 BiLSTM 的权值和阈值,进一步提高组合模型的预测精度和预测速度;最后,将各子
序列预测值叠加得到最终的预测值。 实验结果表明,FEEMD-SAPSO-BiLSTM 组合模型的均方根误差比 FEEMD-PSO-BiLSTM 和
SAPSO-BiLSTM 组合模型分别降低了 22. 9%和 54. 3%,收敛速度方面,FEEMD-SAPSO-BiLSTM 明显快于 FEEMD-PSO-BiLSTM 模
型。 因此在预测短时交通流上,提出的组合模型提高了预测精度和预测速度,达到了期望的预测效果。 |
| 英文摘要: |
| In order to improve the prediction accuracy and speed of short-term traffic flow, based on the instability and randomness of the
traffic flow sequence, fast ensemble empirical mode decomposition ( FEEMD) and natural selection adaptive mutation particle swarm
optimization algorithm (SAPSO) are proposed to optimize the two-way Predictive model combined with long and short-term memory
network (BiLSTM). Firstly, using FEEMD to decompose the original unsteady traffic flow sequence into multiple stable intrinsic modal
components (IMF) and residual components (Res), and filter out the noise part to improve modeling accuracy; secondly, introducing
composite Multi-scale permutation entropy (CMPE) to detect the randomness of traffic flow sub-sequences and regroups them to simplify
model construction and improve prediction accuracy; then, using BiLSTM to predict the reorganized subsequences, and use SAPSO to
optimize the weights and thresholds of BiLSTM to further improve the prediction accuracy and prediction speed of the combined model;
finally, the prediction values of each sub-sequence are superimposed to obtain the final prediction value. The experimental results show
that the root mean square error of the FEEMD-SAPSO-BiLSTM combined model is 22. 9% and 54. 3% lower than the FEEMD-PSOBiLSTM combined model and the SAPSO-BiLSTM combined model, respectively. In terms of convergence speed, the FEEMD-SAPSOBiLSTM model is obviously faster than FEEMD-PSO-BiLSTM model. Therefore, in predicting short-term traffic flow, the proposed
combined model improves the prediction accuracy and speed and achieves the desired prediction effect. |
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