Optimization of Dynamic Multi-Runway Use Strategy Considering Spatio-Temporal Characteristics of Airspace
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摘要: 多跑道机场飞行区运行效率低下会导致空域-跑道系统容流供需失衡，进而造成终端区空域交通拥堵、航班延误现象频发。为提升多跑道机场终端区运行效率，借助全空域与机场模型软件（total airspace and airport modeler，TAAM）建立空域仿真模型，针对不同运行模式动态转换下对终端区交通流走向、扇区开合等空域时空特性的影响进行分析，提出1种考虑不同运行时段内终端区机场走廊口流量配比和进离港流量分布的动态多跑道使用策略优化方法。首先，使用TAAM综合考虑不同跑道运行模式下各扇区内航班流量、高度变更、移交协调及冲突解脱对管制负荷的影响，拟合得出不同跑道运行模式下基于当量航空器架次的各扇区管制负荷函数。以终端区内航班平均飞行时间、平均延误时间及管制员工作负荷为优化目标，建立了跑道使用策略优化模型。设计了1种基于航空器基本性能数据库（the base aircraft data，BADA）的多目标非支配排序遗传算法(NSGA-Ⅱ)，并结合机场实际运行条件在无运行限制、运行方向限制、运行模式限制等5种场景下进行仿真计算。对各场景Pareto最优解集进行评价得出不同场景下最优跑道使用策略，并使用TAAM进行仿真对比验证。结果表明：无运行限制和运行方向限制相较于单一跑道运行模式的航班服务效率提升10.15%，5.01%；管制员工作负荷减少3.91%，3.4%；延误时间减少28.86%，19.46%。Abstract: 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%.
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Key words:
- air transportation /
- air traffic control /
- runway usage strategy /
- NSGA-Ⅱ /
- TAAM
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图 3 进近管制区部分扇区示意图
Figure 3. Schematic diagram of some sectors of the approach control area
图 4 机场进近管制区及部分进场程序示意图
Figure 4. Illustration of the airport approach control area and some approach procedures
图 7 算例机场进近管制区部分结构示意图
Figure 7. Schematic diagram of part of the airport approach control area
图 11 跑道15min起降架次对比
Figure 11. Comparison of runway takeoff and landing sorties in 15min
图 12 不同情景下平均飞行时间与平均延误时间
Figure 12. Average flight time and average delay time under different scenarios
表 1 基本运行模式及间隔要求
Table 1. Basic operating mode and interval requirements
跑道间距/m 运行模式 ≥1 035 独立平行仪表进近 ≥915 相关平行仪表进近 ≥760 隔离平行运行 ≥760 独立平行离场 
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表 2 不同机型尾流间隔标准
Table 2. Wake interval standards for different models
单位: km 前机 后机 重 中 轻 重 7.4 9.3 11.1 中 / / 9.3 轻 / / /
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表 3 部分可行跑道运行模式
Table 3. Partial viable runway operation mode
序号 可行跑道运行模式 1 36L/36R类隔离运行，01/36L独立进近，01/36R隔离运行 2 36L/36R类隔离运行，01/36L独立离场，01/36R独立平行进近 3 36L/36R类隔离运行，36L/01相关进近，36R/01隔离运行 4 36L/36R类隔离运行，36L/01独立进近，36R/01隔离运行、独立离场 ⋮ ⋮ 26 18L/18R类隔离运行，19/18L独立平行进近，19/18R隔离运行 27 18R/18L类隔离运行，19/18R独立离场，19/18L独立平行进近 28 18L/18R类隔离运行，18L/19独立进近，18R/19隔离运行、独立离场
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表 4 部分航班信息
Table 4. Partial flight information
航班号 机型 飞行类型 走廊口点 到达走廊口点时间 预计使用跑道时间 T0001 A320 A 6 14：41 14：54 T0002 A320 A 10 14：45 14：54 T0003 A320 D 11 14：52 15：05 T0004 A320 A 10 15：05 15：14 T0005 A320 D 11 15：13 15：26 ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ T0199 B738 A 10 16：19 16：28 T0200 B738 D 3 18：20 18：31 T0201 A20N D 1 18：21 18：32
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表 5 灵活跑道使用策略
Table 5. Flexible runway usage strategy
时间段 跑道使用策略 14：30—16：30 18L/18R类隔离运行，19/18L独立离场，19/18R独立平行进近 16：30—19：30 36L/36R类隔离运行，01/36L独立离场，01/36R独立平行进近 19：30—20：30 36L/36R类隔离运行，36L/01独立离场，36R/01隔离运行
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表 6 部分航班时刻表
Table 6. Partial flight schedule
航班号 走廊口 预计使用跑道时间 飞行模式 实际使用跑道时间 使用跑道 T0002 2 15：00：00 进 14：51：04 18R T0111 11 14：52：00 离 14：52：00 19 T0004 2 15：01：00 进 14：53：30 19 T0003 10 15：02：00 进 14：54：58 18R ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ T0053 10 16：36：00 进 16：38：59 36L T0124 5 16：37：00 离 16：38：59 01 T0052 1 16：40：00 进 16：41：29 01
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表 7 4种机场运行情景
Table 7. 4 airport operation scenarios
序号 情景内容 情景1 气象条件仅能满足36方向运行 情景2 18：00后仅能满足18方向运行 情景3 仅使用4号运行模式 情景4 仅使用28号运行模式
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表 8 不同情景跑道使用策略
Table 8. Runway use strategies in different scenarios
序号 时间段 跑道使用策略 1 14：30—17：00 36L/36R类隔离运行，36L/01独立进近，36R/01隔离运行 17：00—18：30 36L/36R类隔离运行，36L/01相关进近，36R/01隔离运行 18：30—20：30 36L/36R类隔离运行，36L/01独立进近，36R/01隔离运行 2 14：30—16：30 18L/18R类隔离运行，19/18L独立离场，19/18R独立平行进近 16：30—18：00 36L/36R类隔离运行，01/36L独立离场，01/36R独立平行进近 18：00—20：30 18L/18R类隔离运行，19/18L独立离场，19/18R独立平行进近 3 14：30—20：30 36L/36R类隔离运行，36L/01独立进近，36R/01隔离运行、独立离场 4 14：30—20：30 18L/18R类隔离运行，18L/19独立进近，18R/19隔离运行、独立离场
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表 9 各情景下最优策略结果对比
Table 9. Comparison of optimal strategy results in each scenario
平均飞行时间/min 平均延误时间/s 管制员负荷/架次 无限制 14.34 83.95 95.91 情景1 15.16 95.05 96.41 情景2 15.84 102.39 98.81 情景3 15.41 96.82 99.82 情景4 15.96 118.02 99.12
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表 10 不同情景各时间片扇区平均负荷
Table 10. The average load of different time segments in different scenarios
单位: 架次 时间片 灵活 情景1 情景2 情景3 情景4 1 79.69 98.46 100.80 108.00 111.60 2 117.00 136.55 180.09 156.46 182.45 3 114.66 143.91 164.25 149.54 157.70 4 90.23 105.73 111.01 104.45 90.61 5 81.86 98.84 118.44 86.45 78.25 6 122.20 126.02 170.42 120.11 126.41 7 140.06 131.76 184.45 131.76 144.65 8 140.02 128.23 134.78 129.22 140.02 9 102.42 90.22 93.62 94.88 98.10 10 35.26 33.23 33.79 33.23 54.29 11 3.40 3.22 3.22 3.80 4.61 12 0.00 0.00 0.00 0.00 0.00 平均 85.57 91.35 107.91 93.17 99.05
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[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.htmMA 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.htmWEI 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.1674-4861.2021.06.013WANG 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.htmCHEN 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.htmYIN 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.htmZHANG 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.htmWANG 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.1674-4861.2023.02.008ZHU 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.htmZUO 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 -
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