Volume 42 Issue 1
Feb.  2024
Turn off MathJax
Article Contents
ZHU Chengyuan, Bai Kaidi, ZHAO Zhigang. Optimization of Dynamic Multi-Runway Use Strategy Considering Spatio-Temporal Characteristics of Airspace[J]. Journal of Transport Information and Safety, 2024, 42(1): 94-104. doi: 10.3963/j.jssn.1674-4861.2024.01.011
Citation: ZHU Chengyuan, Bai Kaidi, ZHAO Zhigang. Optimization of Dynamic Multi-Runway Use Strategy Considering Spatio-Temporal Characteristics of Airspace[J]. Journal of Transport Information and Safety, 2024, 42(1): 94-104. doi: 10.3963/j.jssn.1674-4861.2024.01.011

Optimization of Dynamic Multi-Runway Use Strategy Considering Spatio-Temporal Characteristics of Airspace

doi: 10.3963/j.jssn.1674-4861.2024.01.011
  • Received Date: 2023-08-07
    Available Online: 2024-05-31
  • The inefficient operation of the airfield area in multi-runway airports leads to an imbalance between airspace capacity and service efficiency of runway. This question further causes frequent traffic congestion and flight delays in the terminal area. Aiming at this issue, this paper utilizes the Total Airspace and Airport Modeler (TAAM) to establish an airspace simulation model. The model is used to investigate the impact of dynamic transitions between different configurations on the spatial and temporal characteristics of the terminal area, such as traffic flow direction and sector operation. Based on the result, a dynamic multi-runway use strategy optimization method is proposed, considering the traffic ratio at the arrival and departure waypoint and the distribution of arrival and departure aircraft during different operational periods. The airspace simulation models under different runway configurations scenarios are simulated using TAAM. According to the simulation outcomes, the correlation functions between the workload and the equivalent number of aircraft flights under different runway configuration are derived through fitting, taking into account various factors such as the impact of aircraft movement, altitude changes, handover coordination, and conflict resolution on the workload. With the average flight time, average delay time, and workload in the terminal area as optimization goals, a multi-runway use strategy optimization model is established. A multi-objective non-dominated sorting genetic algorithm (NSGA-Ⅱ) based on the Base Aircraft Data (BADA) is designed. Combining the actual operating conditions of the example airport, five scenarios are set up for simulation calculations, including no operating restrictions, operating direction restrictions, and operating configuration restrictions, etc. The Pareto optimal solution set for each scenario is evaluated to determine the optimal runway usage strategy under different scenarios, and TAAM is used for simulation comparison and verification. The results show that compared to the only runway configuration, the service efficiency of the runway usage strategy without operating restrictions and with operating direction restrictions is improved by 10.15% and 5.01%, the workload is reduced by 3.91% and 3.4%, and the average delay time is reduced by 28.86% and 19.46%.

     

  • loading
  • [1]
    PINOL H, BEASLEY J E. Scatter search and bionomic algorithms for the aircraft landing problem[J]. European Journal of Operational Research, 2006, 171(2): 439-462. doi: 10.1016/j.ejor.2004.09.040
    [2]
    LEE H, BALAKRISHNAN H. Fuel cost, delay and throughput tradeoffs in runway scheduling[C]. 2008 American Control Conference, Washington, American: IEEE, 2008.
    [3]
    BALAKRISHNAN H, CHANDRAN B G. Algorithms for scheduling runway operations under constrained position shifting[J]. Operations Research, 2010, 58(6): 1650-1665. doi: 10.1287/opre.1100.0869
    [4]
    ANDREEVA-MORI A, SUZUKI S, ITOH E. Rule derivation for arrival aircraft sequencing[J]. Aerospace Science and Technology, 2013, 30: 200-209. doi: 10.1016/j.ast.2013.08.004
    [5]
    马园园, 胡明华, 张洪海, 等. 多机场终端区进场航班协同排序方法[J]. 航空学报, 2015, 36(7): 2279-2290. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201507020.htm

    MA Y Y, HU M H, ZHANG H H, et al. Optimized method for collaborative arrival sequencing and scheduling in metroplex terminal area[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(7): 2279-2290. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201507020.htm
    [6]
    魏明, 孙博, 吴维. 考虑多跑道运行模式的进离场航班排序优化模型[J]. 工业工程与管理, 2021, 26(5): 68-73. https://www.cnki.com.cn/Article/CJFDTOTAL-GYGC202105009.htm

    WEI M, SUN B, WU W. An optimization model for inbound and outbound flight scheduling with consideration of multi-runway operation mode[J]. Industrial Engineering and Management, 2021, 26(5): 68-73. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GYGC202105009.htm
    [7]
    王宁, 翟文鹏. 基于点融合的多跑道进场航班排序[J]. 交通信息与安全, 2021, 39(6): 108-116. doi: 10.3963/j.jssn.1674-4861.2021.06.013

    WANG N, ZHAI W P. A method based on point fusion procedure for scheduling arrival flights on multiple runways[J]. Journal of Transport Information and Safety, 2021, 39(6): 108-116. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.06.013
    [8]
    陈可嘉, 司徒腾宽, 林鸿熙. 考虑跑道复杂依赖关系的多目标飞机排序模型[J]. 南京航空航天大学学报, 2023, 55(6): 1025-1032. https://www.cnki.com.cn/Article/CJFDTOTAL-NJHK202306009.htm

    CHEN K J, SITU T K, LIN H X. Multi-objective aircraft sequencing model considering complex interdependent runways[J]. Journal of Nanjing University of Aeronautics and Astronautics, 2023, 55(6): 1025-1032. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-NJHK202306009.htm
    [9]
    GILBO, E P. Airport capacity: representation, estimation, optimization[J]. Control Systems Technology IEEE Transactions on, 1993, 1(3): 144-154. doi: 10.1109/87.251882
    [10]
    CHRISTOPHER W, MICHAEL D, REX K. A runway configuration management model with marginally decreasing transition capacities[J]. Advances in Operations Research, 2010, 2(1): 1-21.
    [11]
    BERTSIMAS D, FRANKOVICH M, ODONI A. Optimal selection of airport runway configurations[J]. Operations Research, 2011, 59(6): 1407-1419. doi: 10.1287/opre.1110.0956
    [12]
    尹嘉男, 胡明华, 张洪海, 等. 多跑道协同运行模式优化方法[J]. 航空学报, 2014, 35(3): 795-806. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201403021.htm

    YIN J N, HU M H, ZHANG H H, et al. Optimization approach for collaborative operating modes of multi-runway systems[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(3): 795-806. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201403021.htm
    [13]
    孙海勇. 多跑道运行策略与仿真研究[D]. 天津: 中国民航大学, 2021.

    SUN H Y. Research on multi-runway operation strategy and simulation[D]. Tianjin: Civil Aviation University of China, 2021. (in Chinese)
    [14]
    张颖, 阿音格, 尹嘉男, 等. 基于随机规划的跑道运行模式及容量优化决策[J]. 航空计算技术, 2022, 52(5): 46-50. https://www.cnki.com.cn/Article/CJFDTOTAL-HKJJ202205011.htm

    ZHANG Y, A Y G, YIN J N, et al. Runway operation configuration and capacity optimized decision based on stochastic programming[J]. Aeronautical Computing Technique, 2022, 52(5): 46-50. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HKJJ202205011.htm
    [15]
    中国民用航空总局. 平行跑道同时仪表运行管理规定: CCAR-98TM[S]. 北京: 中国民用航空总局公报, 2004.

    Civil Aviation Administration of China. Regulation for simultaneous instrument operation on parallel runways: CCAR-98TM[S]. Beijing: Communique of the Civil Aviation Administration of China, 2004. (in Chinese)
    [16]
    王莉莉, 于凯笛. 基于航班计划的进近扇区预战术开合策略研究[J]. 飞行力学, 2022, 40(4): 87-94. https://www.cnki.com.cn/Article/CJFDTOTAL-FHLX202204014.htm

    WANG L L, YU K D. Research on pre-tactical operating strategy of approach sector based on flight plan[J]. Flight Dynamics, 2022, 40(4): 87-94. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-FHLX202204014.htm
    [17]
    朱承元, 张澈, 管建华. 基于改进支持向量机的空域交通态势识别方法[J]. 交通信息与安全, 2023, 41(2): 76-85. doi: 10.3963/j.jssn.1674-4861.2023.02.008

    ZHU C Y, ZHANG C, GUAN J H. 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. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2023.02.008
    [18]
    ZELINSKI S. Precision arrival scheduling for tactical reconfiguration[C]. 2013 Aviation Technology, Integration, and Operations Conference, Los Angle, American: AIAA, 2013.
    [19]
    DEB K, AGRAWAL S, PRATAP A, et al. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-Ⅱ[J]. IEEE Trans. Evolutionary Computation, 2002, 6(2): 187-197.
    [20]
    韩鹤. 基于NSGA-Ⅱ的气象灾害应急储备库选址与资源调度模型研究[D]. 南京: 南京信息工程大学, 2017.

    HAN H. Reserch on emergency reserve location and resource scheduling model under the meteorological disaster based on NSGA-Ⅱ[D]. Nanjing: Nanjing University of Information Science and Technology, 2017. (in Chinese)
    [21]
    左青海, 杨凡, 潘卫军. 民用航空器尾流重新分类发展综述[J]. 河南科技, 2020, 39(26): 65-70. https://www.cnki.com.cn/Article/CJFDTOTAL-HNKJ202026028.htm

    ZUO Q H, YANG F, PAN W J. Review on the development of aircraft wake turbulence re-categorization[J]. Henan Science and Technology, 2020, 39(26): 65-70. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HNKJ202026028.htm
  • 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(10)

    Article Metrics

    Article views (171) PDF downloads(27) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return