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基于小波优化GRU-ARMA模型的空中交通流量短时预测方法

闫少华 谢晓璇 张兆宁

闫少华, 谢晓璇, 张兆宁. 基于小波优化GRU-ARMA模型的空中交通流量短时预测方法[J]. 交通信息与安全, 2022, 40(4): 177-184. doi: 10.3963/j.jssn.1674-4861.2022.04.019
引用本文: 闫少华, 谢晓璇, 张兆宁. 基于小波优化GRU-ARMA模型的空中交通流量短时预测方法[J]. 交通信息与安全, 2022, 40(4): 177-184. doi: 10.3963/j.jssn.1674-4861.2022.04.019
YAN Shaohua, XIE Xiaoxuan, ZHANG Zhaoning. A Short-term Prediction of Air Traffic Flow Based on a Wavelet-optimized GRU-ARMA Model[J]. Journal of Transport Information and Safety, 2022, 40(4): 177-184. doi: 10.3963/j.jssn.1674-4861.2022.04.019
Citation: YAN Shaohua, XIE Xiaoxuan, ZHANG Zhaoning. A Short-term Prediction of Air Traffic Flow Based on a Wavelet-optimized GRU-ARMA Model[J]. Journal of Transport Information and Safety, 2022, 40(4): 177-184. doi: 10.3963/j.jssn.1674-4861.2022.04.019

基于小波优化GRU-ARMA模型的空中交通流量短时预测方法

doi: 10.3963/j.jssn.1674-4861.2022.04.019
基金项目: 

国家重点研发计划项目 2020YFB1600103

详细信息
    作者简介:

    闫少华(1964—),硕士,副教授. 研究方向:空中交通管理、航空安全管理. E-mail: shyan@cauc.edu.cn

    通讯作者:

    张兆宁(1964—),博士,教授. 研究方向:交通运输规划与管理. E-mail: zzhaoning@263.net

  • 中图分类号: V355.1

A Short-term Prediction of Air Traffic Flow Based on a Wavelet-optimized GRU-ARMA Model

  • 摘要: 空中交通流量短时预测是空中交通管理的基础,是有效缓解交通拥堵问题的前提。为提高空中交通流量短时预测的精度,减小空中交通管制员的工作压力,提出了基于小波优化GRU-ARMA的空中交通流量短时预测方法。在传统预测方法的基础上,通过小波变换对原始流量数据进行多尺度分解,提取不同频率交通流量的细节特征,对原始流量数据进行预处理。同时,根据小波变换,在低频处将频率细分作为趋势项,高频处将时间细分作为噪声项。其中,趋势项反映了空中交通流量随时间演化的整体趋势性,噪声项反映了随机因素对空中交通流量的综合影响。使用门控循环单元(GRU)神经网络模型预测趋势项,自回归滑动平均模型(ARMA)模型预测噪声项;将趋势项和噪声项的预测值叠加,得到最终的短时流量预测值。误差分析表明,该方法在每个预测点上的误差保持在2%左右,预测效果稳定;而直接采用原始流量数据进行预测的GRU、BiLSTM、CNN-LSTM神经网络模型及单一的ARMA模型,每个点的预测误差在5%~37.14%之间。与GRU、BiLSTM、CNN-LSTM神经网络模型相比,该模型的预测精度分别提高了3.02%,5.39%,5.05%。

     

  • 图  1  GRU算法原理图

    Figure  1.  The structure of Gated Recurrent Unit (GRU)

    图  2  基于小波优化GRU-ARMA模型的空中交通流量短时预测流程

    Figure  2.  Short term air traffic flow frediction process based on Wavelet-Optimized GRU-ARMA model

    图  3  小波基函数信噪比

    Figure  3.  Signal noise ratio of wavelet basis function

    图  4  原始流量时间序列小波分解图

    Figure  4.  Wavelet decomposition of original traffic time series

    图  5  GRU神经网络loss曲线图

    Figure  5.  Loss graph of GRU

    图  6  趋势项预测结果

    Figure  6.  Prediction results of trend items

    图  7  噪声项ARMA定阶热力图

    Figure  7.  ARMA thermal diagram of noise term

    图  8  小波优化GRU-ARMA预测结果

    Figure  8.  Prediction results of the wavelet-optimized GRU-ARMA model

    图  9  不同模型预测结果

    Figure  9.  Prediction results of different models

    图  10  5种模型误差对比

    Figure  10.  Error comparison of five models

    表  1  不同分解层数信噪比对比

    Table  1.   Comparison of different decomposition layers of signal-to-noise ratio

    小波基函数 分解层数 信噪比
    bior2.2 3 24.195 0
    4 24.175 6
    5 24.174 6
    db3 3 21.701 3
    4 21.663 6
    5 21.639 2
    sym4 3 22.383 5
    4 22.336 5
    5 22.332 5
    下载: 导出CSV

    表  2  不同置信区间对应的临界ADF

    Table  2.   Corresponding critical ADF value under different confidence intervals

    置信区间 临界ADF
    99% -3.430 9
    95% -2.861 8
    90% -2.566 9
    下载: 导出CSV

    表  3  5种模型的评价指标

    Table  3.   Evaluation indexes of four models

    预测模型 评价指标
    RMSE MAE MAPE/%
    小波优化GRU-ARMA 1.338 0.958 1.74
    GRU 3.234 2.542 4.76
    BiLSTM 4.996 3.958 7.13
    CNN-LSTM 4.601 3.667 6.79
    ARMA 5.208 3.33 6.04
    下载: 导出CSV
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  • 收稿日期:  2022-04-11
  • 网络出版日期:  2022-09-17

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