基于经验模态分解与K近邻的轨道交通站点客流预测方法

Passenger Flow Prediction Method for Rail Transit Stations Based on Empirical Mode Decomposition and K-nearest Neighbors

文中提出一种经验模态分解(EMD)与K近邻非参数回归(KNN)组合的客流时间序列预测方法.基于EMD和KNN算法原理,在对KNN预测方法进行改进的基础上,构建了EMD-KNN组合算法流程.针对实例站点受新冠肺炎疫情影响,客流时间序列趋势发生明显变化的特征,利用BP结构断点检测法识别出三个结构性断点,选取客流变化趋势与预测日最为接近的时间序列段进行经验模态分解,将分解后的序列重组为高频、低频和趋势序列,分别运用考虑权重的K近邻算法进行预测,叠加得到最终预测结果,并与单一KNN算法和ARIMA模型预测结果比较.结果表明:EMD-KNN组合算法预测精度高于单一KNN算法和ARIMA模型,且能有效捕捉客流变化趋势.

A passenger flow time series forecasting method based on empirical mode decomposition(EMD)and K-nearest neighbor nonparametric regression(KNN)was proposed.Based on the principle of EMD and KNN algorithm,the EMD-KNN combined algorithm flow was constructed on the basis of improving KNN prediction method.According to the characteristics that the time series trend of passenger flow has changed obviously due to the influence of COVID-19 epidemic situation in the example stations.BP structural breakpoint detection method was used to identify three structural breakpoints,and the time series segment with the closest passenger flow change trend to the forecast day was selected for empirical mode decomposition.The decomposed sequences were reorganized into high-frequency,low-frequency and trend sequences,and then the K-nearest neighbor algorithm considering weight was used to predict,and the final prediction results were obtained by superposition,and compared with the prediction results of single KNN algorithm and ARIMA model.The results show that the prediction accuracy of EMD-KNN combination algorithm is higher than that of single KNN algorithm and ARIMA model,and it can effectively capture the changing trend of passenger flow.