Traffic Flow Combined Prediction Model Based on Complementary Ensemble Empirical Mode Decomposition and Bidirectional Gated Recurrent Unit Optimized by Improved Sparrow Search Algorithm
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摘要: 该文针对短时交通流预测过程呈现的非线性、非平稳性及时序相关性特征，为提升预测的精度及收敛速度，提出一种基于互补集合经验模态分解(CEEMD)和改进麻雀搜索算法(ISSA)优化双向门控循环单元(BiGRU)的组合预测模型。首先，考虑到端点飞翼问题，通过改进CEEMD算法将交通流量序列分解为体现路网交通趋势性、周期性及随机性的本征模态函数(IMF)分量，有效提取了其中的先验特征；随后，利用BiGRU网络挖掘交通流量序列中的时序相关性特征，为避免局部最优，并提高麻雀搜索算法(SSA)全局搜索及局部开发能力，采用ISSA对BiGRU网络权值参数迭代择优。实验结果表明，该组合预测模型中各组件对提高预测精度均起到正向作用，同时在不同交通流量数据集下的预测性能较对比算法均更优，展现了精准、快速的预测表现以及良好的泛化能力。
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关键词:
- 短时交通流预测 /
- 互补集合经验模态分解 /
- 麻雀搜索算法 /
- 双向门控循环单元 /
- 边界局部特征延拓
Abstract: In order to improve the accuracy and convergence speed of prediction, a combined prediction model, based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Bidirectional Gated Recurrent Unit (BiGRU) optimized by Improved Sparrow Search Algorithm (ISSA), is proposed to deal with the nonlinear, non-stationary and temporal correlation of short-term traffic flow prediction. Firstly, considering the end-point flying wing problem, the traffic flow sequence is decomposed into Intrinsic Mode Function (IMF) components that reflect the trend, periodicity and randomness of road network traffic by improved CEEMD algorithm, which extracts effectively the prior features; Then, the BiGRU network is used to mine the temporal correlation in traffic flow sequence. To fear the local optimum, and improve the global search and local mining ability of Sparrow Search Algorithm (SSA), ISSA is used instead of gradient descent method to iterate the BiGRU network weights. The ablation experiment results show that each component in the combined prediction model plays a positive role in improving the prediction accuracy. The prediction performance under different traffic flow sets is better than the compared algorithm, showing accurate and fast prediction performance with good generalization ability. -
表 1 仿真参数设置
仿真参数 参数值 ISSA仿真参数 参数值 信噪比$ {\text{Nstd}} $ 0.2 dB 初始权重${w_{\rm{b}}}$ 0.9 噪声添加次数$ {\text{NE}} $ 500 次 终止权重${w_{\rm{e}}}$ 0.4 最大迭代次数$ {\text{iter}} $ 2 000 次 麻雀种族数量$ {\text{Np}} $ 50 个 模态分量总数$ m $ 9 个 最大迭代次数$ {\text{ite}}{{\text{r}}_M} $ 100 次 学习率$ {\text{lr}} $ 0.001 预警值$ {R_2} $ 0.5 隐含层神经元个数${N_{{\text{ly}}}}$ 64 个 安全值$ {\text{ST}} $ 0.8 
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表 2 各预测模型的误差对比
基准预测模型 MAE MAPE(%) RMSE 该组合模型与基准模型相比各指标变化 MAE MAPE(%) RMSE GRU 11.44 11.68 95.25 –7.86 –8.40 –61.31 BiGRU 7.38 8.16 61.51 –3.80 –4.88 –27.57 SSA-BiGRU 6.11 6.01 50.44 –2.53 –2.73 –16.50 CEEMD-ISSA-BiGRU 4.04 3.84 37.43 –0.46 –0.56 –3.49 文献[4] 5.45 5.74 47.98 –1.87 –2.46 –14.04 文献[1] 5.02 4.58 40.74 –1.44 –1.30 –6.80 改进CEEMD-ISSA-BiGRU(本文) 3.58 3.28 33.94 – – –
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