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过站航班地面保障过程动态预测

王立文 李彪 邢志伟 刘洪恩 罗谦

王立文, 李彪, 邢志伟, 等 . 过站航班地面保障过程动态预测[J]. 北京航空航天大学学报, 2021, 47(6): 1095-1104. doi: 10.13700/j.bh.1001-5965.2020.0165
引用本文: 王立文, 李彪, 邢志伟, 等 . 过站航班地面保障过程动态预测[J]. 北京航空航天大学学报, 2021, 47(6): 1095-1104. doi: 10.13700/j.bh.1001-5965.2020.0165
WANG Liwen, LI Biao, XING Zhiwei, et al. Dynamic prediction of ground support process for transit flight[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(6): 1095-1104. doi: 10.13700/j.bh.1001-5965.2020.0165(in Chinese)
Citation: WANG Liwen, LI Biao, XING Zhiwei, et al. Dynamic prediction of ground support process for transit flight[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(6): 1095-1104. doi: 10.13700/j.bh.1001-5965.2020.0165(in Chinese)

过站航班地面保障过程动态预测

doi: 10.13700/j.bh.1001-5965.2020.0165
基金项目: 

国家重点研发计划 2018YFB1601200

国家自然科学基金委员会-中国民用航空局联合研究基金 U1533203

中央高校基本科研业务费-中国民航大学专项 3122019094

详细信息
    通讯作者:

    邢志伟. E-mail: cauc_xzw@163.com

  • 中图分类号: V351;TP181

Dynamic prediction of ground support process for transit flight

Funds: 

National Key R & D Program of China 2018YFB1601200

National Natural Science Foundation of China-China Civil Aviation Administration Joint Research Fund U1533203

the Fundamental Research Funds for the Central Universities (Special for Civil Aviation University of China) 3122019094

More Information
  • 摘要:

    过站航班地面保障过程预测是机场协同决策系统的重要功能。针对目前无法实现过程精细化动态预测且精度较低的问题,提出了一种基于贝叶斯网络的过站航班地面保障过程动态预测方法。建立了地面保障过程贝叶斯网络模型,设计了基于航班属性的初始样本空间生成算法,结合高斯核概率密度估计构建了地面保障过程动态预测方法。某枢纽机场实际数据的仿真结果表明:所提方法在充分考虑航班运行属性的基础上实现了各保障节点的动态预测,其平均绝对误差仅为2.224 1 min,均方根误差相比其他方法低近2 min,能够为机场运行短时战术组织提供客观的决策依据。

     

  • 图 1  过站航班地面保障过程

    Figure 1.  Ground support process for transit flight

    图 2  地面保障过程网络拓扑

    Figure 2.  Network topological graph of ground support process

    图 3  初始样本空间生成结果

    Figure 3.  Generation results of initial sample space

    图 4  关键保障节点先验概率模型

    Figure 4.  Prior kernel probability model of key support nodes

    图 5  航班到达后的保障过程初始预测结果

    Figure 5.  Initial prediction results of support process after flight arrival

    图 6  保障节点条件概率模型样例

    Figure 6.  Sample of conditional probability model for support nodes

    图 7  多航班关键保障节点预测结果

    Figure 7.  Key support node prediction results for multi-flight

    图 8  多航班预测误差分析结果

    Figure 8.  Error analysis results of multi-flight prediction

    图 9  单航班内部过程预测结果对比

    Figure 9.  Comparison of prediction results for single flight internal process

    图 10  多航班撤轮挡时间预测对比

    Figure 10.  Comparison of prediction results for multi-flight off block time

    表  1  过站航班运行过程属性

    Table  1.   Operation process attributes for transit flights

    字段 属性
    航司代码 CA
    执飞日期 2019-05-10
    机位 240
    预计到达 14:13
    前序架次 9
    航班号 1796
    机型 B738
    航线性质 国内远程
    实际到达 14:25
    后序架次 12
    下载: 导出CSV

    表  2  过站航班运行过程共享时刻数据

    Table  2.   Shared time data of operation process for transit flights

    保障节点 过程时刻
    上轮挡 14:30
    下客结束 14:38
    机上清洁结束 14:47
    垃圾完成 14:49
    配餐完成 14:45
    添加航油完成 14:51
    货舱配载上传 14:55
    允许上客发布 15:57
    机务巡检确认 14:25
    上客结束 15:16
    撤轮挡 15:19
    实际起飞 15:31
    下载: 导出CSV

    表  3  单航班动态预测结果

    Table  3.   Dynamic prediction results for single flight

    保障节点 初始预测时间/min 最终预测时间/min 实际时间/min
    x1 5.2 5.2 5
    x2 14.5 14.1 13
    x3 25.4 24.1 22
    x4 24.1 28.6 24
    x5 17.7 19.2 20
    x6 23.3 25.8 26
    x7 32.5 32.7 30
    x8 22.8 22.7 23
    x9 29.1 35.9 32
    x10 45.4 43.8 41
    x11 51.0 54.5 54
    下载: 导出CSV

    表  4  单航班预测误差分析结果

    Table  4.   Error analysis results of single flight prediction

    保障节点 MAE/min MRE
    x1 0.247 5 0.049 5
    x2 1.285 1 0.098 9
    x3 4.053 5 0.184 2
    x4 2.495 3 0.104 0
    x5 1.892 3 0.094 6
    x6 0.952 5 0.036 6
    x7 3.816 2 0.127 2
    x8 0.894 7 0.038 9
    x9 2.561 9 0.080 1
    x10 4.441 9 0.108 3
    x11 1.824 4 0.033 8
    下载: 导出CSV

    表  5  预测精度参数对比

    Table  5.   Comparison of prediction accuracy parameters

    方法 RMSE/min TIC
    贝叶斯网络演化 0.998 1 0.009 6
    深度神经网络 2.921 7 0.029 7
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-04-28
  • 录用日期:  2020-06-12
  • 刊出日期:  2021-06-20

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