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新型冠状病毒肺炎疫情影响下中国航空货运量分析与预测

陈亚东 丁松滨 刘计民 宋晓敏 隋东

陈亚东, 丁松滨, 刘计民, 宋晓敏, 隋东. 新型冠状病毒肺炎疫情影响下中国航空货运量分析与预测[J]. 交通信息与安全, 2022, 40(2): 155-162. doi: 10.3963/j.jssn.1674-4861.2022.02.018
引用本文: 陈亚东, 丁松滨, 刘计民, 宋晓敏, 隋东. 新型冠状病毒肺炎疫情影响下中国航空货运量分析与预测[J]. 交通信息与安全, 2022, 40(2): 155-162. doi: 10.3963/j.jssn.1674-4861.2022.02.018
CHEN Yadong, DING Songbin, LIU Jiming, SONG Xiaomin, SUI Dong. An Analysis and Forecasting of Air Cargo Volume in China Under the Impacts of COVID-19 Epidemic[J]. Journal of Transport Information and Safety, 2022, 40(2): 155-162. doi: 10.3963/j.jssn.1674-4861.2022.02.018
Citation: CHEN Yadong, DING Songbin, LIU Jiming, SONG Xiaomin, SUI Dong. An Analysis and Forecasting of Air Cargo Volume in China Under the Impacts of COVID-19 Epidemic[J]. Journal of Transport Information and Safety, 2022, 40(2): 155-162. doi: 10.3963/j.jssn.1674-4861.2022.02.018

新型冠状病毒肺炎疫情影响下中国航空货运量分析与预测

doi: 10.3963/j.jssn.1674-4861.2022.02.018
基金项目: 

中国民用航空局基金项目 TM2019-9-1/2

中国民用航空局华东空管局基金项目 1007KFA21061

详细信息
    作者简介:

    陈亚东(1997—),硕士研究生. 研究方向:航空运输. E-mail: chenyadong1997@163.com

    通讯作者:

    丁松滨(1964—),博士,教授. 研究方向:民航安全、飞行性能工程研究. E-mail : dingzhili@nuaa.edu.cn

  • 中图分类号: U8

An Analysis and Forecasting of Air Cargo Volume in China Under the Impacts of COVID-19 Epidemic

  • 摘要: 在新型冠状病毒肺炎疫情对航空货运的影响下,月度航空货运量出现异于历史趋势的极端数据,而传统航空货运量预测模型有在极端数据影响下误差较大的问题。因此,研究了适用于后疫情时代的中国航空货运量短期预测方法。对2009—2020年中国航空货运量月度数据进行分析,发现中国航空货运量呈稳定增长趋势。受疫情影响出现短期剧烈波动,在假设疫情对航空货运的影响逐渐减弱的前提下,选取Holt-Winters乘法模型与求和自回归移动平均ARIMA乘积季节模型分别提取航空货运量数据的长期趋势、周期特征和短期波动特征,并采用4种不同权重确定方法构建了多个航空货运量组合预测模型。运用Holt-Winter模型、ARIMA模型及其组合预测模型对2021—2022年中国航空月度货运量进行了预测,以2021年1月—5月的航空货运量数据作为验证数据集,对比分析了不同预测模型的预测误差。结果表明:Holt-Winters与ARIMA组合预测模型的平均绝对百分比误差与最大绝对百分比误差普遍小于自身单一模型的;基于最小二乘法赋权的组合模型预测效果最优,基于残差倒数法赋权的组合模型预测效果次优;最优组合模型的平均绝对百分比误差为1.93%,比次优组合模型降低了8.53%,较单一的Holt-Winters模型与ARIMA模型分别降低了71.70%与20.58%,验证了最优组合模型对后疫情时代中国航空货运量月度数据预测的有效性。

     

  • 图  1  2009—2020年中国航空货运量月度数据时序图

    Figure  1.  Time sequence diagram of monthly data of Chinese air cargo volume from 2009 to 2020

    图  2  Holt-Winters乘法模型预测结果

    Figure  2.  Prediction results of Holt-Winters multiplication model

    图  3  运输量一阶差分、一阶季节差分图

    Figure  3.  1st difference and 1st seasonal difference diagram of transportation volume

    图  4  运输量二阶差分、一阶季节差分图

    Figure  4.  2nd difference and 1st seasonal difference diagram of transportation volume

    图  5  差分处理后自相关图

    Figure  5.  Autocorrelation diagram after data processing

    图  6  差分处理后偏自相关图

    Figure  6.  Partial autocorrelation diagram after data processing

    图  7  ARIMA(1, 2, 2)(0, 1, 2)12 模型预测结果

    Figure  7.  Forecasting results of ARIMA(1, 2, 2)(0, 1, 2)12 model

    表  1  2009—2020年中国航空货运量月度数据

    Table  1.   Monthly data of Chinese air cargo volume from 2009 to 2020  单位: 10 000 t

    年份 月度货运量
    1 2 3 4 5 6 7 8 9 10 11 12
    2009 26.43 25.64 33.83 35.21 34.13 33.72 35.99 39.08 45.65 41.83 45.00 45.54
    2010 45.07 34.27 47.96 46.60 46.73 43.63 44.54 46.32 51.57 49.64 49.82 51.45
    2011 49.65 30.07 48.90 47.83 44.74 43.51 45.29 45.53 50.65 46.94 48.59 49.92
    2012 35.42 37.77 46.84 44.53 44.99 42.86 43.73 45.56 52.79 45.62 51.40 50.01
    2013 47.66 29.31 48.72 46.17 46.78 43.76 43.97 46.31 52.23 48.80 53.00 51.00
    2014 46.80 33.40 51.73 49.47 50.39 46.17 47.19 49.56 53.93 52.96 55.53 55.00
    2015 51.86 40.69 52.00 52.22 52.89 49.39 48.72 50.36 55.48 54.17 58.71 58.80
    2016 58.80 35.20 56.40 54.10 55.20 53.60 52.40 53.30 59.70 59.00 64.20 65.10
    2017 56.50 41.80 58.90 57.50 59.90 58.10 54.80 57.10 64.40 59.00 66.70 66.10
    2018 63.90 45.60 61.50 60.50 62.60 61.40 59.10 60.70 66.30 63.10 66.10 67.00
    2019 67.20 37.60 63.10 60.80 62.40 60.70 61.60 63.00 69.00 66.50 70.00 71.30
    2020 60.60 29.70 48.40 48.40 54.90 57.80 55.20 54.90 66.50 62.10 67.50 69.50
    下载: 导出CSV

    表  2  Holt-Winters乘法模型统计

    Table  2.   Statistical test of Holt-Winters multiplication model

    模型拟合度统计 杨-博克斯Q(18)检验
    平稳R2 R2 MAPE /% 统计 DF 显著性
    0.625 0.908 4.536 20.285 15 0.161
    下载: 导出CSV

    表  3  2021年和2022年航空货运量预测值(Holt-Winters乘法模型)

    Table  3.   Forecasting results of air cargo volume in 2021 and 2022 (with Holt-Winters multiplication model)

    年份 月份 预测值/x104t 年份 月份 预测值/x104t
    2021 1 61.92 2022 1 64.65
    2 39.75 2 41.50
    3 62.37 3 65.12
    4 61.79 4 64.51
    5 64.52 5 67.35
    6 62.33 6 65.06
    7 60.38 7 63.02
    8 61.78 8 64.47
    9 70.11 9 73.16
    10 66.34 10 69.22
    11 71.17 11 74.25
    12 71.54 12 74.64
    下载: 导出CSV

    表  4  备选模型的检验

    Table  4.   Verification of alternative models

    模型参数 R2 正态化BIC
    (1, 2, 2)(0, 1, 2)12 0.928 2.276
    (1, 2, 2)(1, 1, 2)12 0.928 2.317
    (1, 2, 1)(0, 1, 2)12 0.906 2.450
    (1, 2, 1)(1, 1, 2)12 0.919 2.440
    (2, 2, 1)(0,1,2)12 0.927 2.333
    (2, 2, 1)(1, 1, 2)12 0.922 2.401
    (2, 2, 2)(0, 1, 2)12 0.950 2.281
    (2, 2, 2(0, 1, 2)12 0.908 2.556
    下载: 导出CSV

    表  5  ARIMA(1, 2, 2)(0, 1, 2)12 模型统计

    Table  5.   Statistical test of ARIMA (1, 2, 2)(0, 1, 2)12 model

    模型拟合度统计 杨-博克斯Q(18)检验
    平稳R2 R2 MAPE /% 统计 DF 显著性
    0.921 0.928 3.248 10.215 13 0.676
    下载: 导出CSV

    表  6  2021年和2022年的航空货运量预测值(ARIMA(1, 2, 2)(0, 1, 2)12

    Table  6.   Forecasting results of air cargo volume in 2021 and 2022 (ARIMA (1, 2, 2)(0, 1, 2)12)

    年份 月份 预测值/xl04t 年份 月份 预测值/xl04t
    2021 1 65.64 2022 l 70.7
    2 49.37 2 55.23
    3 65.79 3 71.37
    4 64.24 4 69.6
    5 66.62 5 72.35
    6 65.59 6 71.78
    7 64.39 7 70.28
    8 65.71 8 71.28
    9 72.82 9 79.16
    10 69.73 10 75.94
    11 74.34 11 80.76
    12 75.38 12 82.14
    下载: 导出CSV

    表  7  组合模型权重分配

    Table  7.   Weight distribution of combined model

    模型 赋权方法 Holt-Winters乘法模型 arima乘积季节模型
    组合模型l 等权平均法 0.50 0.50
    组合模型2 方差倒数法 0.42 0.58
    组合模型3 残差倒数法 0.39 0.61
    组合模型4 最小二乘法 0.30 0.70
    下载: 导出CSV

    表  8  各模型预测结果对比

    Table  8.   Comparison of forecasting results of all models  单位: 10 000 t

    时间 实际值 arima乘积季节模型 Holt-Winters乘法模型 组合模型1 组合模型2 组合模型3 组合模型4
    1月 66.90 65.64 61.92 63.78 64.08 64.19 64.53
    2月 45.90 49.37 39.75 44.56 45.33 45.62 46.51
    3月 65.50 65.79 62.37 64.08 64.35 64.46 64.77
    4月 65.50 64.24 61.79 63.02 63.21 63.28 63.51
    5月 66.40 66.62 64.52 65.57 65.74 65.80 66.00
    下载: 导出CSV

    表  9  各模型误差比较

    Table  9.   Comparison of model errors

    模型名称 MAPE /% 最大绝对百分比误差/%
    ARIMA乘积季节模型 2.43 7.56
    Holt-Winters乘法模型 6.82 13.40
    组合模型1 2.96 4.66
    组合模型2 2.34 4.22
    组合模型3 2.11 4.05
    组合模型4 1.93 3.54
    下载: 导出CSV

    表  10  中国航空货运量预测结果(组合模型4)

    Table  10.   Forecasting results of air cargo volume in China (combined model 4)

    年份 月份 预测值/x104t 年份 月份 预测值/x104t
    2021 1 2022 1 68.89
    2 2 51.11
    3 3 69.50
    4 4 68.07
    5 5 70.85
    6 64.61 6 69.76
    7 63.19 7 68.10
    8 64.53 8 69.24
    9 72.01 9 77.36
    10 68.71 10 73.92
    11 73.39 11 78.81
    12 74.23 12 79.89
    下载: 导出CSV
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  • 收稿日期:  2021-08-21
  • 网络出版日期:  2022-05-18

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