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基于度量学习的机场交通态势弱监督评估

杜婧涵 胡明华 张魏宁 尹嘉男

杜婧涵,胡明华,张魏宁,等. 基于度量学习的机场交通态势弱监督评估[J]. 北京航空航天大学学报,2023,49(7):1772-1778 doi: 10.13700/j.bh.1001-5965.2021.0568
引用本文: 杜婧涵,胡明华,张魏宁,等. 基于度量学习的机场交通态势弱监督评估[J]. 北京航空航天大学学报,2023,49(7):1772-1778 doi: 10.13700/j.bh.1001-5965.2021.0568
DU J H,HU M H,ZHANG W N,et al. Weakly supervised evaluation of airport traffic situation based on metric learning[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(7):1772-1778 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0568
Citation: DU J H,HU M H,ZHANG W N,et al. Weakly supervised evaluation of airport traffic situation based on metric learning[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(7):1772-1778 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0568

基于度量学习的机场交通态势弱监督评估

doi: 10.13700/j.bh.1001-5965.2021.0568
基金项目: 国家自然科学基金(52002178, 71731001);国家留学基金(202106830100, 202106830077);江苏省自然科学基金(BK20190416)
详细信息
    通讯作者:

    E-mail:j.yin@nuaa.edu.cn

  • 中图分类号: V351

Weakly supervised evaluation of airport traffic situation based on metric learning

Funds: National Natural Science Foundation of China (52002178, 71731001); China Scholarship Council (202106830100, 202106830077); Natural Science Foundation of Jiangsu Province (BK20190416)
More Information
  • 摘要:

    为准确感知机场场面运行环境,提出基于度量学习的交通态势弱监督评估方法。根据机场场面航空器的时空分布类型,从交通流量、起降队列、资源需求等视角构建交通态势指标体系;借鉴度量学习范式,利用预先定义的相似集和不相似集自动学习态势样本之间的距离度量;在此基础上,采用K均值算法实现弱监督条件下交通态势的等级划分。以上海浦东国际机场实际运行数据为例,分析并验证所提方法的有效性。实验结果表明:起始时刻离场瞬时流量、离场累计流量、离场跑道队列长度及进场累计流量的距离系数大于0.5,对场面态势影响较大;与基于欧式距离的K均值算法相比,度量学习将最优轮廓系数提升了33.3%,得到符合预期语义的聚类结果;此外,机场的平均滑行时间越长、跑道配置越复杂,其场面交通态势等级越高。

     

  • 图 1  航空器滑行时空图

    Figure 1.  Time-space diagram of aircraft taxiing

    图 2  机场交通态势评估流程

    Figure 2.  Flow chart of airport traffic situation assessment

    图 3  上海浦东国际机场场面布局

    Figure 3.  Scene layout of Shanghai Pudong International Airport

    图 4  进离场航班不同跑道配置下平均滑行时间分布

    Figure 4.  Distribution of average taxi time under different runway configurations of departure and arrival flights

    图 5  度量学习前后数据分布

    Figure 5.  Data distribution before and after metric learning

    图 6  不同聚类数下的轮廓系数

    Figure 6.  Contour coefficients under different cluster number

    图 7  不同态势等级下平均滑行时间的概率密度分布

    Figure 7.  Probability density distribution of average taxi time under different situation levels

    图 8  不同经验参数下最优轮廓系数

    Figure 8.  Optimal contour coefficient under different empirical parameters

    表  1  交通态势指标体系

    Table  1.   Traffic situation index system

    指标类型指标名称解释说明
    离场类指标起始时刻离场瞬时流量d1参考时间片起始时刻处于离场过程的航空器架次
    终止时刻离场瞬时流量d2参考时间片终止时刻处于离场过程的航空器架次
    离场累计流量d3参考时间片内所有离场航班架次
    离场跑道队列长度d4参考时间片内从跑道起飞的航班架次
    离场资源需求指数d5参考时间片内从停机位推出的航班架次
    进场类指标起始时刻进场瞬时流量a1参考时间片起始时刻处于进场过程的航空器架次
    终止时刻进场瞬时流量a2参考时间片终止时刻处于进场过程的航空器架次
    进场累计流量a3参考时间片内所有进场航班架次
    进场跑道队列长度a4参考时间片内在跑道降落的航班架次
    进场资源需求指数a5参考时间片内推入到停机位的航班架次
    下载: 导出CSV

    表  2  指标的基本统计信息

    Table  2.   Basic statistics of indices

    指标统计量/架次
    最小值最大值均值中位数
    d10258.999
    d20238.929
    d313816.9918
    d40178.079
    d50208.018
    a10187.538
    a20177.498
    a303115.6716
    a40178.158
    a50188.188
    下载: 导出CSV

    表  3  度量学习算法学习到的距离矩阵的估计结果

    Table  3.   Estimation results of distance matrix learned by metric learning algorithm

    指标d1d2d3d4d5a1a2a3a4a5
    d10.50−0.09−0.18−0.260.210.070.070.01−0.07−0.06
    d2−0.090.25−0.220.20−0.290.060.060.06−0.06−0.05
    d3−0.18−0.220.77−0.22−0.210.020.020.04−0.02−0.02
    d4−0.260.20−0.220.57−0.08−0.04−0.04−0.010.030.03
    d50.21−0.29−0.21−0.080.34−0.05−0.05−0.020.050.04
    a10.070.060.02−0.04−0.050.36−0.16−0.220.28−0.23
    a20.070.060.02−0.04−0.05−0.160.32−0.21−0.210.29
    a30.010.060.04−0.01−0.02−0.22−0.210.76−0.21−0.22
    a4−0.07−0.06−0.020.030.050.28−0.21−0.210.32−0.17
    a5−0.06−0.05−0.020.030.04−0.230.29−0.22−0.170.35
    下载: 导出CSV

    表  4  关键指标的聚类中心

    Table  4.   Cluster center of key indices

    指标聚类中心
    态势等级1态势等级2态势等级3
    d12.8310.4216.06
    d33.3710.2616.48
    d48.0119.7727.31
    a310.6515.4920.14
    下载: 导出CSV

    表  5  不同态势等级下占比大于5%的跑道配置情况

    Table  5.   Runway configuration accounting for more than 5% under different situation levels

    态势等级跑道配置(进场|离场)占比/%
    1 34R|35R 37.5
    35L|34L 26.2
    17R|16R 10.7
    16L|17L 8.0
    34R,35L|35R 5.9
    2 34R,35L|34L,35R 52.1
    17R,16L|17L,16R 41.6
    3 17R,16L|17L,16R 48.5
    34R,35L|34L,35R 40.7
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-26
  • 录用日期:  2021-12-12
  • 网络出版日期:  2021-12-22
  • 整期出版日期:  2023-07-31

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