中国铁道科学2013，Vol.34Issue(3)：122-127,6.DOI:10.3969/j.issn.1001-4632.2013.03.20
基于小波分析的最小二乘支持向量机轨道交通客流预测方法
A Wavelet Analysis Based LS-SVM Rail Transit Passenger Flow Prediction Method
摘要
Abstract
To deal with rail transit passenger flow forecasting problem,the discrete one-dimensional Daub4 wavelet analysis method was adopted to decompose the original time series data of passenger flow in a certain period into different low-frequency and high-frequency components,which were used as sample data to train least squares support vector machine (LS-SVM) to determine LS-SVM nuclear parameter σ,coefficients a and b.The trained LS-SVM was used to predict the low-frequency and high-frequency components of passenger flow time series data in a future period of time.Then Daub4 wavelet analysis method was again adopted to reconstruct the predicted low-frequency and high-frequency components to obtain the predicted time series data of passenger flow in a future period of time.Compared with historical average prediction method and gray prediction method,results show that wavelet analysis based SVM passenger flow prediction method has higher accuracy in short-term passenger flow prediction for rail transit.关键词
轨道交通/客流预测/短期预测/小波分析/支持向量机/数据处理Key words
Rail transit/ Passenger flow prediction/ Short-term prediction/ Wavelet analysis/ Support vector machine/ Data processing分类
交通工程引用本文复制引用
杨军,侯忠生..基于小波分析的最小二乘支持向量机轨道交通客流预测方法[J].中国铁道科学,2013,34(3):122-127,6.基金项目
国家自然科学基金资助项目(60834001) （60834001）
国家科技支撑计划项目(2011BAG01B02) （2011BAG01B02）
