Volume 41 Issue 2
Apr.  2023
Turn off MathJax
Article Contents
Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(2): 76-85
ZHU Chengyuan, ZHANG Che, GUAN Jianhua. A Method for Monitoring Traffic State in the Airspace Based on an Improved Support Vector Machine[J]. Journal of Transport Information and Safety, 2023, 41(2): 76-85. doi: 10.3963/j.jssn.1674-4861.2023.02.008
Citation: ZHU Chengyuan, ZHANG Che, GUAN Jianhua. A Method for Monitoring Traffic State in the Airspace Based on an Improved Support Vector Machine[J]. Journal of Transport Information and Safety, 2023, 41(2): 76-85. doi: 10.3963/j.jssn.1674-4861.2023.02.008
shu

A Method for Monitoring Traffic State in the Airspace Based on an Improved Support Vector Machine

doi: 10.3963/j.jssn.1674-4861.2023.02.008

  • College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China

  • Received Date: 2022-03-07
    Available Online: 2023-06-19
    Abstract
  • This paper quantitatively analyzes the methods for monitoring airspace traffic state from the perspective of the workload of air traffic controllers, due to the difficult measurement of such a factor in current studies.In response to the need of monitoring airspace traffic state more efficiently, an airspace simulation model is developedbased onTotal Airspace and Airport Modeller (TAAM) software and a method for identifying traffic state in the rasterized airspace scenario is proposed based on an improved support vector machine (SVM). Based on real-world operation experience of controllers, the shape and size of grids are determined by comparing different rasterization schemes. Target airspace is rasterized by taking the hexagon with a side length of 25 km as the smallest unit. Considering a variety of controller's workloads and the distribution of navigation facilities, a set of indicators for describing traffic states of the airspace is developed.Ak-means clustering algorithm is used to generate prior classified data by aggregating simulated sample data. A traffic state model for the airspace, developed based on the sparrow-search algorithm (SSA) and SVM, iscalled SSA-SVM.The solution set is divided according to the fitness. Moreover, key parameters of the model, including kernel parameters σ and penalty coefficients C, are optimized to determine a combination of parameters, which can increase the generalization capability of the model and avoid overfitting. Traffic states in the rasterized airspace are divided into four levels. Simulations are conductedfor the control airspace ofthe City of Xi'an. Study results show that the proposed SSA-SVM model can mitigate the overfitting problem, but not by the proposed genetic algorithm and support vector machine (GA-SVM) model.The average accuracy of classification is improved by 2.50%, and the accuracy of classification is improved by 1.73%. Among the tested 176 grids, the number of congested, crowded, and steady grids are 26, 18, and 51, respectively. Compared with the partition method for the complex airspace based on controller experience, the convergencerate of the proposed model is as high as 95%, which verifies the effectiveness of the proposed method for identifying airspace traffic state and reducing the workload of air traffic controllers.

     

    • FullText(HTML)
  • loading
    • References(21)
  • [1]
    MENON P K, SWERIDUK G D, BILIMORIA K D. A new approach for modeling, analysis and control of air traffic flow[J]. AIAA Journal of Guidance, Control and Dynamics, 2004, 27 (5): 737-744. doi: 10.2514/1.2556
    [2]
    程承旗, 吴飞龙, 王嵘, 等. 地球空间参考网格系统建设初探[J]. 北京大学学报(自然科学版), 2016, 52 (6): 1041-1049. https://www.cnki.com.cn/Article/CJFDTOTAL-BJDZ201606009.htm

    CHENG C Q, WU F L, WANG R, et al. Study on globe spatial grid reference system construction[J]. Journal of Peking University (Natural Science Edition), 2016, 52 (6) : 1041-1049. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-BJDZ201606009.htm
    [3]
    徐鑫宇, 万路军, 陈平, 等. 基于GeoSOT网格的空域栅格化表征方法[J]. 空军工程大学学报(自然科学版), 2021, 22(2): 15-22. https://www.cnki.com.cn/Article/CJFDTOTAL-KJGC202102003.htm

    XU X Y, WAN L J, CHEN P, et al. An airspace raster repre sentation method based on GeoSOT grid[J]. Journal of Air Force Engineering University(Natural Science Edition), 2021, 22 (2): 15-22. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KJGC202102003.htm
    [4]
    张兆宁, 黎新华, 王莉莉. 基于飞行跟驰模型的纵向安全间隔计算方法[J]. 交通运输工程学报, 2008 (3): 73-76. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC200803019.htm

    ZHANG Z N, LI X H, WANG L L. Computational method of longitudinal safety separation based on flight following theory[J]. Journal of Traffic and Transportation Engineering, 2008(3): 73-76. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC200803019.htm
    [5]
    王灵丽, 黄敏, 高亮. 基于聚类算法的交通网络节点重要性评价方法研究[J]. 交通信息与安全, 2020, 38 (2): 80-88. doi: 10.3963/j.jssn.1674-4861.2020.02.010

    WANG L L, HUANG M, GAO L. Methods of importance evaluation of traffic network node based on clustering algorithms[J]. Journal of Transport Information and Safety, 2020, 38 (2): 80-88. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2020.02.010
    [6]
    LEE K, FERON E, PRITCHETT A. Describing airspace complexity: Airspace response to disturbances[J]. Journal of Guidance, Control and Dynamics, 2009, 32 (1): 210-222. doi: 10.2514/1.36308
    [7]
    徐肖豪, 任杰, 李楠. 基于FCM的终端区交通态势识别[J]. 航空计算技术, 2014, 44 (1): 1-8. https://www.cnki.com.cn/Article/CJFDTOTAL-HKJJ201401001.htm

    XU X H, REN J, LI N. Identification of terminal area traffic situation based on FCM[J]. Aeronautical Computing Technique, 2014, 44 (1): 1-8. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HKJJ201401001.htm
    [8]
    李楠, 任杰, 徐肖豪. 终端区交通态势识别研究[J]. 科学技术与工程, 2014, 14 (11): 256-261. https://www.cnki.com.cn/Article/CJFDTOTAL-KXJS201411057.htm

    LI N, REN J, XU X H. Identification of terminal area traffic situation[J]. Science Technology and Engineering, 2014, 14(11): 256-261. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KXJS201411057.htm
    [9]
    赵嶷飞, 吕立萱, 张勰. 空域扇区运行状态评估[J]. 科学技术与工程, 2014, 14 (32): 105-109. https://www.cnki.com.cn/Article/CJFDTOTAL-KXJS201432023.htm

    ZHAO Y F, LYU L X, ZHANG X. Status evaluation of airspace sector operation[J]. Science Technology and Engineering, 2014, 14 (32): 105-109. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KXJS201432023.htm
    [10]
    李桂毅. 基于航迹数据的航路网络交通运行态势识别与预测技术研究[D]. 南京: 南京航空航天大学, 2018.

    LI G Y. Research on traffic operation status identification and prediction technology of air route network based on flight trajectory data[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2018. (in Chinese)
    [11]
    王利航. 终端区交通态势研究[D]. 天津: 中国民航大学, 2020.

    WANG L H. Research on traffic situation in terminal control area[D]. Tianjin: Civil Aviation University of China, 2020. (in Chinese)
    [12]
    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/OL]. 北京航空航天大学学报: (1-10)[2022-01-13]. https://kns. cnki. net/kcms/detail/11.2625. V. 20211222. 0902. 001. html
    [13]
    岳仁田, 赵嶷飞, 罗云. 空中交通拥挤判别指标的建立与应用[J]. 中国民航大学学报, 2008 (3): 30-35. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGMH200803008.htm

    YUE R T, ZHAO Y F, LUO Y. Construction and application of congestion discrimination index for air traffic[J]. Journal of Civil Aviation University of China, 2008(3): 30-35. (inChinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGMH200803008.htm
    [14]
    袁立罡, 胡明华, 张洪海, 等. 融合先验经验聚类的终端区交通流相态识别[J]. 交通运输工程学报, 2016, 16(5): 83-94. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC201605010.htm

    YUAN L G, HU M H, ZHANG H H, et al. Phase-state identification of traffic flow in terminal area incorporated with prior experience clustering[J]. Journal of Traffic and Transportation Engineering, 2016, 16 (5): 83-94. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC201605010.htm
    [15]
    李桂毅, 胡明华, 张洪海. 基于FCM-SVM方法的时空航路网交通状态识别研究[J]. 武汉理工大学学报(交通科学与工程版), 2018, 42 (4): 569-573, 578. https://www.cnki.com.cn/Article/CJFDTOTAL-JTKJ201804008.htm

    LI G Y, HU M H, ZHANG H H. Research on traffic state identification of spatio-temporal air route network based on FCM-SVM method[J]. Journal of Wuhan University of Technology(Transportation science&Engineering), 2018, 42(4): 569-573, 578. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JTKJ201804008.htm
    [16]
    李桂毅, 胡明华, 郑哲. 基于FCM-粗糙集的多扇区交通拥挤识别方法研究[J]. 交通运输系统工程与信息, 2017, 17(6): 141-146. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201706021.htm

    LI G Y, HU M H, ZHENG Z. Multi-sector traffic congestion identification method based on FCM-rough sets[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17 (6): 141-146. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201706021.htm
    [17]
    YOUSEFI A, DONOHUE G. Temporal and spatial distribution of airspace complexity for air traffic controller workload-based sectorization[C]. AIAA 4th Aviation Technology, Integration and Operation Forum. Chicago, USA: AIAA, 2004.
    [18]
    宁亚美. 基于复杂度指标的终端管制区划分研究[D]. 天津: 中国民航大学, 2020.

    NING Y M. Research on the division of terminal control area based on complexity index[D]. Tianjin: Civil Aviation University of China, 2020. (in Chinese)
    [19]
    YIN Z, HOU J. Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes[J]. Neurocomputing, 2016, 174 (9): 643-650.
    [20]
    李巧茹, 郝恩强, 陈亮, 等. 遗传算法优化支持向量机的城市交通状态识别[J]. 重庆交通大学学报(自然科学版), 2020, 39 (8): 1-5, 13. https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT202008001.htm

    LI Q R, HAO E Q, CHEN L, et al. Urban traffic state recognition based on genetic algorithm optimized support vector machine[J]. Journal of Chongqing Jiaotong University(Natural Science Edition), 2020, 39 (8): 1-5, 13. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT202008001.htm
    [21]
    薛建凯. 1种新型的群智能优化技术的研究与应用[D]. 上海: 东华大学, 2020.

    XUE J K. Research and application of a noval swarm intelligence optimization technique: sparrow search algorithm[D]. Shanghai: Donghua University, 2020. (in Chinese)
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(12)  / Tables(2)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (792) PDF downloads(29) Cited by()
    Proportional views
    Related

    Copyright Editorial Office of Transport Information and Safety鄂ICP备05003336号-2

    Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA (430063)

    Tel：027-86580355 Email：jtjsj@vip.163.com

    Supported by: Beijing Renhe Information Technology Co. Ltd  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return