《Table 2 Fluctuation forecasting results of other passenger flow models》

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《基于模糊信息粒化和CPSO-LS-SVM的城市轨道交通客流量组合预测（英文）》

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In order to further verify the validity of the model proposed in this paper，several commonly used prediction models are selected for comparison and analysis，which are BP neural network and standard LS-SVM.The forecasting results of the prediction models are shown in Table 2.Based on the characteristics of each component，the relative error，the root mean square error（RMSE）and the mean absolute percentage error（MAPE）are used as the evaluation indexes of the three prediction models.The relative error of three models are shown in Table3，the RMSE and MAPE of the predicted results are shown in Table 4.