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基于SARIMA模型的广珠城际铁路客流量预测

李洁 彭其渊 杨宇翔

李洁, 彭其渊, 杨宇翔. 基于SARIMA模型的广珠城际铁路客流量预测[J]. 西南交通大学学报, 2020, 55(1): 41-51. doi: 10.3969/j.issn.0258-2724.20180617
引用本文: 李洁, 彭其渊, 杨宇翔. 基于SARIMA模型的广珠城际铁路客流量预测[J]. 西南交通大学学报, 2020, 55(1): 41-51. doi: 10.3969/j.issn.0258-2724.20180617
LI Jie, PENG Qiyuan, YANG Yuxiang. Passenger Flow Prediction for Guangzhou-Zhuhai Intercity Railway Based on SARIMA Model[J]. Journal of Southwest Jiaotong University, 2020, 55(1): 41-51. doi: 10.3969/j.issn.0258-2724.20180617
Citation: LI Jie, PENG Qiyuan, YANG Yuxiang. Passenger Flow Prediction for Guangzhou-Zhuhai Intercity Railway Based on SARIMA Model[J]. Journal of Southwest Jiaotong University, 2020, 55(1): 41-51. doi: 10.3969/j.issn.0258-2724.20180617

基于SARIMA模型的广珠城际铁路客流量预测

doi: 10.3969/j.issn.0258-2724.20180617
基金项目: 国家自然科学基金(U1834209);国家重点研发计划(2017YFB1200701);国家自然科学基金(71871188)
详细信息
    作者简介:

    李洁(1991—),女,博士研究生,研究方向为铁路运输组织优化,E-mail:jieli20092746@126.com

    通讯作者:

    彭其渊(1962—),男,教授,研究方向为铁路运输组织优化,E-mail:qiyuan-peng@home.swjtu.edu.cn

  • 中图分类号: U293.4

Passenger Flow Prediction for Guangzhou-Zhuhai Intercity Railway Based on SARIMA Model

  • 摘要: 为实现铁路车站发送客流量的短期预测,研究预测步长对短期客流预测效果的影响,分析了广珠城际铁路车站发送客流的特征和变化规律,结合客流特征及季节性差分自回归滑动平均模型(seasonal autoregressive integrated moving average,SARIMA)的适用性,构建了SARIMA客流预测模型,利用Python软件中的Statsmodels模块完成了SARIMA客流模型的精细化调参,以广州南站、小榄站的发送客流量为例验证了模型的有效性. 结果表明,SARIMA预测模型可以较好地适用于不同数量等级的客流预测,其预测精度随预测步长的增加而降低. 预测步长为1时,广州南站、小榄站、珠海站客流预测平均绝对百分比误差(mean absolute percentage error,MAPE)值分别为3.97%,5.83%,5.43%;预测步长增加为2时,各车站客流预测误差显著增加,广州南站、小榄站、珠海站客流预测误差MAPE值分别为5.31%,6.79%,7.62%;预测步长大于2时,预测误差基本保持稳定. 将SARIMA模型预测效果与随机森林(random forest, RF)、支持向量机(support vector machine, SVM)、梯度提升算法(gradient boosting, GB)、K最近邻算法(K-nearest neighbor, KNN)模型或方法的预测效果进行对比,预测步长为1时,SARIMA模型预测效果略优于其余4种模型,5种预测模型预测精度差距较小;预测步长大于1时,RF、SVM、GB、KNN模型预测误差随预测步长显著增加,预测误差为SARIMA模型的数倍. SARIMA模型在客流时间序列的多步预测方面具有较大的优势.

     

  • 图 1  SARIMA时间序列预测模型流程

    Figure 1.  Flowchart of SARIMA time series prediction model

    图 2  各车站日均发送客流量

    Figure 2.  Distribution of average daily passengers departing from each station

    图 3  车站客流周期性分布特征

    Figure 3.  Periodic distribution of daily passengers departing from each station

    图 4  车站节假日客流分布特征

    Figure 4.  Distribution of passengers departing from each station during the vacations

    图 5  广州南站发送客流量时间序列分解

    Figure 5.  Decomposition of departing passengers time series at Guangzhou South station

    图 6  时间序列自相关与偏自相关

    Figure 6.  Autocorrelation diagram and partial autocorrelation diagram of passenger time series

    图 7  SARIMA(5,0,1)(2,1,2)7残差分布及检验情况

    Figure 7.  Distribution and test of residual of SARIMA(5,0,1)(2,1,2)7

    图 8  预测步长为1时广州南站客流预测值与RMSE分布情况

    Figure 8.  Prediction result and RMSE distribution of one-step forward forecast at Guangzhou South station

    图 9  广州南站不同预测步长MAPE分布情况

    Figure 9.  MAPE distribution with different forecast steps of Guangzhou South station

    图 10  预测步长为1时小榄站与珠海站客流预测值与RMSE分布情况

    Figure 10.  Prediction result and RMSE distribution of one-step forward forecast at Xiaolan and Zhuhai station

    图 11  不同模型预测效果对比

    Figure 11.  Comparison of prediction results of different models

    表  1  原始序列的ADF平稳性检验

    Table  1.   ADF test of the original time series

    统计量临界值显著性 P
    1%水平5%水平10%水平
    −3.509 8 −3.447 1 −2.868 9 −2.570 7 0.007 7
    下载: 导出CSV

    表  2  一阶季节性差分序列ADF检验结果

    Table  2.   ADF test of first-order seasonal difference sequence

    统计量临界值显著性 P
    1%水平5%水平10%水平
    −5.588 0 −3.444 2 −2.867 5 −2.570 0 0.000 001
    下载: 导出CSV

    表  3  Ljung-Box检验结果

    Table  3.   Results of Ljung-Box test

    滞后量相关系数统计量显著性 P
    1.0 −0.020 909 0.179 688 0.671 642
    2.0 −0.031 391 0.585 691 0.746 138
    3.0 −0.026 921 0.885 028 0.829 039
    4.0 0.007 576 0.908 794 0.923 296
    5.0 0.003 262 0.913 212 0.969 258
    6.0 0.026 582 1.207 246 0.976 525
    7.0 −0.005 190 1.218 483 0.990 495
    8.0 0.019 373 1.375 445 0.994 588
    9.0 0.012 077 1.436 592 0.997 590
    10.0 −0.052 022 2.574 035 0.989 752
    11.0 0.018 233 2.714 121 0.993 990
    12.0 0.066 690 4.592 856 0.970 216
    13.0 0.029 064 4.950 590 0.976 265
    14.0 −0.051 118 6.060 014 0.964 956
    15.0 −0.028 291 6.400 705 0.972 203
    16.0 −0.131 306 13.758 166 0.616 724
    17.0 −0.037 413 14.357 022 0.641 688
    18.0 −0.063 751 16.100 248 0.585 551
    19.0 0.038 014 16.721 674 0.608 717
    20.0 0.054 497 18.002 092 0.587 270
    下载: 导出CSV

    表  4  不同车站客流SARIMA预测模型参数

    Table  4.   SARIMA parameters of different stations

    车站模型参数AIC 值
    小榄 ${\rm{SARIMA}}\left( {3,1,1} \right){\left( {2,0,0} \right)_7}$ 6 017.504 36
    珠海 ${\rm{SARIMA}}\left( {3,1,1} \right){\left( {1,0,1} \right)_7}$ 6 348.004 67
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-08-10
  • 修回日期:  2018-11-10
  • 网络出版日期:  2019-11-27
  • 刊出日期:  2020-02-01

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