基于CEEMDAN-BLSTM模型的城轨交通短时客流预测

针对工作日和休息日客流变化的不同特征,分别构建工作日和休息日客流数据的预测模型,采用Pearson相关系数分析历史客流数据与预测值的相关程度.采用完全噪声辅助聚合经验模态分解(CEEMDAN)算法对客流数据进行分解,减少数据波动所带来的误差.根据客流的时间变化,采用改进的双向长短期记忆网络(BLSTM)对分解后的客流数据进行预测.将LSTM、BLSTM、EMD-BLSTM等模型与文中所提CEEMDAN-BLSTM模型进行对比,验证了文中提所提模型的有效性和准确性.

Combination forecast for urban rail transit passenger flow based on fuzzy information granulation and CPSO-LS-SVM

In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fuzzy information granulation and least squares support vector machine(LS-SVM)optimized by chaos particle swarm optimization(CPSO).Due to the nonlinearity and fluctuation of the passenger flow,firstly,fuzzy information granulation is used to extract the valid data from the window according to the requirement.Secondly,CPSO that has strong global search ability is applied to optimize the parameters of the LS-SVM forecasting model.Finally,the combined model is used to forecast the fluctuation range of early peak passenger flow at Tiyu Xilu Station of Guangzhou Metro Line 3 in 2014,and the results are compared and analyzed with other models.Simulation results demonstrate that the combined forecasting model can effectively track the fluctuation of passenger flow,which provides an effective method for predicting the fluctuation range of short-term passenger flow in the future.

基于二次分解和MVO-TCN的城轨站点客流预测研究

为提升城轨站点客流预测精度,提出一种基于变分模态分解(VMD)、经验小波变换(EWT)和多元宇宙优化算法(MVO)优化的时间卷积神经网络(TCN)的复合预测模型。使用VMD对城轨站点客流数据进行初步分解,生成若干本征模态函数(IMF)。随后,计算每个IMF的样本熵,并利用K-means算法根据样本熵值将IMFs聚类为复合分量,对高频复合分量进一步应用EWT进行细化分解,以减少波动性强的城轨站点客流数据的分解残留。通过MVO算法对TCN模型的参数进行优化,以强化模型在各个分量上的预测能力。最后,将各分量的预测结果进行重构,得到最终的客流预测值。实验结果表明,文中所提出的VMD-EWT-MVO-TCN模型通过二次分解改善了分解残留问题,结合对TCN参数的寻优提升了城轨站点客流预测精度,RMSE和MAE值分别为14.936 5和5.789 3,相较TCN模型提升了45.46%和50.28%。该模型能够为城轨站点客流管理提供新的技术支持和决策参考。