运筹与模糊学

Vol.6 No.1 (February 2016)

基于时间序列分析的上海地铁16号线客流预测—以临港大道站为例
Prediction of Shanghai Metro Line 16 Passenger Flow Based on Time Series Analysis—with Lingang Avenue Station as a Study Case

 

作者:

陈彦莉 , 朱小林 , 张小红 :上海海事大学文理学院,上海

沙玉五 :上海磁浮交通发展有限公司客运服务部,上海

 

关键词:

城市轨交时间序列ARIMA模型Urban Rail Transit Time Series Analysis ARIMA Model

 

摘要:

随着不断发展城市轨道交通建设也不断涌现诸多问题,如何以科学手段来预测客流,从而提高轨道交通运营的效率引起广泛关注。时间序列分析是主流的预测方法,其中ARIMA模型适用于各类的序列,是迄今最通用的时间序列预测法。本文将以上海地铁16号线临港大道站为例,对其日客流通过时间序列分析方法,建立差分自回归移动平均模型(ARIMA模型),利用自相关函数和偏自相关函数来初步判断和识别ARIMA模型各个参数,并根据所建立的模型来预测16号线临港大道站后两周客流数据,以此为提高临港地区城市轨交运营效率,改善临港地区地铁与公交高效衔接建立基础。

Problems emerge along with the continuous development of urban rail transit, and how to predict the passenger flow to improve the efficiency of the rail transit operation by the scientific method has caused widely public concern. Time series analysis is the mainstream of forecasting method. And ARIMA model acts on all kinds of sequences, so it is the most common time series prediction method by far. This study proposes Autoregressive Integrated Moving Average Model (ARIMA model) to predict the passenger flow data of the line 16 Lingang Avenue Station based on the historical datum through time series analysis in order to improve the operational efficiency of the urban rail transit and effective cohesion with buses in Lingang area. We utilize the autocorrelation and partial autocorrelation function to preliminarily judge and identify the parameters of ARIMA model.

文章引用:

陈彦莉 , 沙玉五 , 朱小林 , 张小红 (2016) 基于时间序列分析的上海地铁16号线客流预测—以临港大道站为例。 运筹与模糊学, 6, 15-26. doi: 10.12677/ORF.2016.61003

 

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