基于滑动平均法的轨道交通短时客流实时预测

doi:10.13229/j.cnki.jdxbgxb20161256

Abstract

Abstract: This paper presents an algorithm for short-time passenger flow prediction in urban rail transit based on Moving Average (MA) method. First, the moving parameters of MA are determined, then, the direct forecasting passenger flow time is obtained by averaging the historical data. Second, the predicted time series of passenger flow are obtained by modifying the direct forecasting results with the actual-time passenger flow data. Experimental results suggest that the MA method is superior to Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), Wavelet Neural Network (WNN) and the combination of Wavelet and SVM (WS) both in prediction accuracy and computing speed. Therefore, the proposed algorithm can be applied to short-time prediction of complex passenger flow time series.

Key words: engineering of communications and transpotation system, moving-average, moving parameter, short-time passenger flow prediction, real-time forecasting

CLC Number:

U293.1

MENG Pin-chao, LI Xue-yuan, JIA Hong-fei, LI Yan-zhong. Short-time rail transit passenger flow real-time prediction based on moving average[J].吉林大学学报(工学版), 2018, 48(2): 448-453.

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Short-time rail transit passenger flow real-time prediction based on moving average

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