Volume 43 Issue 2
Apr.  2025
Turn off MathJax
Article Contents
ZHI Jingwen, ZHANG Junfeng, MA Zao. Operational Risk Assessment of Terminal Airspace with Prevailing Traffic Flow[J]. Journal of Transport Information and Safety, 2025, 43(2): 11-18. doi: 10.3963/j.jssn.1674-4861.2025.02.002
Citation: ZHI Jingwen, ZHANG Junfeng, MA Zao. Operational Risk Assessment of Terminal Airspace with Prevailing Traffic Flow[J]. Journal of Transport Information and Safety, 2025, 43(2): 11-18. doi: 10.3963/j.jssn.1674-4861.2025.02.002

Operational Risk Assessment of Terminal Airspace with Prevailing Traffic Flow

doi: 10.3963/j.jssn.1674-4861.2025.02.002
  • Received Date: 2024-07-30
    Available Online: 2025-09-29
  • The characterization and assessment of operational risks represent critical steps in enhancing airspace operational safety and management capabilities, particularly in structurally complex terminal airspaces with multiple intersecting arrival and departure flows. Traditional collision risk models predominantly focus on preemptive single-aircraft conflict detection and fixed-routes, exhibiting limitations in characterizing terminal airspace operational structures and analyzing post-conflict risks. This study develops a methodology that extracts prevalent traffic flows from historical trajectory data through clustering, which identifies geometric configurations of conflict scenarios and incorporating aircraft maneuvering adjustments. By analyzing the spatiotemporal distribution in risk-prone areas, an assessment metric defined as the expected collision frequency per unit time was constructed to quantify the operational risk. Cumulative aggregation across diverse temporal and spatial scales enables risk quantification and hotspot identification, thereby facilitating operational evaluations, early warnings for high-risk traffic flows and spatiotemporal hotspots, and providing guidance for air traffic control, tactical planning, and airspace construction. A case study of Guangzhou Baiyun Airport validates the model's effectiveness in risk characterization and hotspot detection, Key findings include: Conflict risks between arrival flows constitute the primary risk category; The departure flow of VIBOS requires attention under northbound operation; Temporal risk hotspots are concentrated between 13:00—14:00 and 22:00—02:00.

     

  • loading
  • [1]
    QU Y, HAN S. A method to calculate the collision risk on air-route[C]. International Conference on Management and Service Science, Wuhan, China: IEEE, 2010.
    [2]
    AU S K, BECK J L. Estimation of small failure probabilities in high dimensions by subset simulation[J]. Probabilistic Engineering Mechanics, 2001, 16(4): 263-277. doi: 10.1016/S0266-8920(01)00019-4
    [3]
    刘章. 基于REICH模型的同高度交叉航路碰撞风险研究[J]. 深圳大学学报(理工版), 2020, 37(2): 136-142.

    LIU Z. Collision risk of crossing airlines at the same altitude based on REICH model[J]. Journal of Shenzhen University (Science and Engineering), 2020, 37(2): 136-142. (in Chinese)
    [4]
    王莉莉, 刘鑫宇. 基于自主改航的交叉航班流预先冲突解脱研究[J/OL]. 西南交通大学学报, 2024-03[2024-11-22]. http://kns.cnki.net/kcms/detail/51.1277.U.20240322.1643.006.html.

    WANG L L, LIU X Y. Research on cross-flight flow pre-conflict resolution based on auto-rerouting[J/OL]. Journal of Southwest Jiaoton University, 2024-03[2024-11-22]. http://kns.cnki.net/kcms/detail/51.1277.U.20240322.1643.006.html. (in Chinese)
    [5]
    王莉莉, 阳杰. 基于速度随机分布的低空空域小型无人机碰撞风险评估模型[J]. 交通信息与安全, 2022, 40(4): 64-70. doi: 10.3963/j.jssn.1674-4861.2022.04.007

    WANG L L, YANG J. A collision risk model for small UAVs based on velocity random distribution in low-altitude airspace[J]. Journal of Transport Information and Safety, 2022, 40(4): 64-70. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2022.04.007
    [6]
    陈肯, 杨晓刚. 基于改进Event模型的航路垂直方向碰撞研究[J]. 航空计算技术, 2021, 51(5): 15-18.

    CHEN K, YANG X G. Vertical collision research of air routes based on the improved event model[J]. Aeronautical Computing Technique, 2021, 51(5): 15-18. (in Chinese)
    [7]
    MITICI M, BLOM H A P. Mathematical models for air traffic conflict and collision probability estimation[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(3): 1052-1068.
    [8]
    PAIELLI R A, ERZBERGER H. Conflict probability estimation for free flight[J]. Journal of Guidance, Control, and Dynamics, 1997, 20(3): 588-596. doi: 10.2514/2.4081
    [9]
    王世锦. 繁忙终端空域飞行冲突风险[J]. 南京航空航天大学学报, 2013, 45(4): 538-543.

    WANG S J. Flight conflict risk in busy terminal aiespace[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2013, 45(4): 538-543. (in Chinese)
    [10]
    FIGUET B, MONSTEIN R, WALTERT M, et al. Data-driven mid-air collision risk modelling using extreme-value theory[J]. Aerospace Science and Technology, 2023, 142: 108646. doi: 10.1016/j.ast.2023.108646
    [11]
    KRAUTH T, MORIO J, OLIVE X, et al. Advanced collision risk estimation in terminal manoeuvring areas using a disentangled variational autoencoder for uncertainty quantification[J]. Engineering Applications of Artificial Intelligence, 2024, 133: 108137. doi: 10.1016/j.engappai.2024.108137
    [12]
    BARRATT S T, KOCHENDERFER M J, BOYD S P. Learning probabilistic trajectory models of aircraft in terminal airspace from position data[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(9): 3536-3545.
    [13]
    ZHENG Y. Trajectory data mining: an overview[J]. ACM Transactions on Intelligent Systems and Technology, 2015, 6 (3): 1-41.
    [14]
    REHM F. Clustering of flight tracks[C]. AIAA Infotech@Aerospace (I@A) Conference, Georgia: AIAA, 2011.
    [15]
    ZHAO L, SHI G, A trajectory clustering method based on Douglas-Peucker compression and density for marine traffic pattern recognition[J]. Ocean Engineering, 2019, 172(15): 456-467.
    [16]
    GARIEL M, SRIVASTAVA A N, FERON E. Trajectory clustering and an application to airspace monitoring[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12 (4): 1511-1524. doi: 10.1109/TITS.2011.2160628
    [17]
    CHEN L, NG R. On the marriage of Lp-norms and edit distance[C]. 30th International Conference on Very Large Data Bases, Toronto: VLDB, 2004.
    [18]
    HAN P, WANG W, SHI Q, et al. A combined online-learning model with K-means clustering and GRU neural networks for trajectory prediction[J]. Ad Hoc Networks, 2021, 117: 102476. doi: 10.1016/j.adhoc.2021.102476
    [19]
    LANG A, ERICH S. BETULA: numerically stable CF-trees for BIRCH clustering[C]. International Conference on Similarity Search and Applications, Denmark: Springer, 2020.
    [20]
    DENG D. DBSCAN clustering algorithm based on density[C]. 7th International Forum on Electrical Engineering and Automation, Hefei: IEEE, 2020.
    [21]
    DENG Z, HU Y, ZHU M, et al. A scalable and fast OPTICS for clustering trajectory big data[J]. Cluster Computing, 2015, 18: 549-62. doi: 10.1007/s10586-014-0413-9
    [22]
    MCLNNES L, HEALY J, ASTELS S. Hdbscan: Hierarchical density-based clustering[J]. The Journal of Open Source Software, 2017, 2(11): 205. doi: 10.21105/joss.00205
    [23]
    戴福青, 李解. 基于PBN的中小机场终端区飞行程序优化研究[J]. 科学技术与工程, 2012, 12(34): 9270-9274, 9279.

    DAI F Q, LI J. Flight procedure optimization in terminal area of small and medium-sized airport based on PBN[J]. Science Technology and Engineering, 2012, 12 (34) : 9270-9274, 9279. (in Chinese)
    [24]
    白鹏, 陈霖峰, 王玺, 等. 基于点融合的进场程序优化与航空器排序研究[J]. 中国安全科学学报, 2021, 31(12): 45-52.

    BAI P, CHEN L F, WANG X, et al. Research on arrival procedure optimization and aircraft sequencing based on point merging[J]. China Safety Science Journal, 2021, 31(12): 45-52. (in Chinese)
  • 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(10)  / Tables(2)

    Article Metrics

    Article views (37) PDF downloads(0) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return