A Study on Optimization of Operation Procedures in the Cul-de-sac Area of Terminals in Large Hub Airports
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摘要: 大型枢纽机场广泛采用指廊式航站楼构型以获得更多的近机位，但因时空资源受限、供需关系不对等，指廊间形成的U型港湾区域逐渐成为机场高效运行的瓶颈。为解决高峰时段航空器进出港湾区受限的问题，研究了港湾区航空器的运行优化程序。通过解析港湾区的结构特性和运行现状，构建面向港湾区的交通工程技术集，分别提出基于现状和工程技术优化后的航班编组运行程序。考虑航空器在港湾区的运行规则，建立港湾区全时段、全流程的运行优化模型，为实现高效求解，设计基于安全距离因子的动态编组算法。为比较优化前后的提升效果，基于广州白云机场西北港湾区实际航班数据进行验证，分别对比实际条件下的运行、编组运行，以及工程技术优化后的编组运行3种情况。结果表明：编组运行及工程技术优化后编组运行的全时段航班平均运行时间较现状分别降低11.37%，14.45%。其中，离港航班平均运行时间分别降低6.47%，10.13%；进港航班平均运行时间分别降低20.27%，22.31%。延误分析中，航班延误时间分别降低45.94%，58.42%。基于现状及工程技术优化后的全时段航班编组数量分别为29组和34组，港湾区高峰运行时段最多可实现3组航班编组运行，验证了对航班进行编组运行程序优化提升复杂构型与复杂运行场景下港湾区整体运行效率的有效性。Abstract: Large hub airports have widely adopted the finger corridor terminal configuration to obtain more contact stands. However, due to limited spatiotemporal resources and an imbalance in supply and demand, the U-shaped Cul-de-sac area formed between finger corridors has gradually become a bottleneck for efficient airport operations. To address restricted entry and exit of aircraft in the Cul-de-sac areas during peak hours, this study proposes optimization procedures for aircraft operations. By analyzing the structural characteristics and operational status of the Cul-de-sac areas, transportation engineering technologies are developed, and the formation operation procedures are proposed based on the current status and engineering-optimized technology. Considering the operational rules of aircraft in the Cul-de-sac area, an optimization model for the entire operational process is established, and a dynamic marshalling algorithm incorporating safety distance factors is designed to enhance efficiency. To compare operational efficiency before and after the optimization, validation is conducted using actual flight data from the Cul-de-sac area on the northwest side of Guangzhou Baiyun Airport. Three operational scenarios are compared: actual operations, formation operations and engineering-optimized formation operations. The results show that, compared with the baseline operations, the average operation time of full-time flights is reduced by 11.37% under formation operations and by 14.45% under engineering-optimized formation operations. Specifically, the average operation time of departure flights is reduced by 6.47% and 10.13%, while that of arrival flights is reduced by 20.27% and 22.31%. Moreover, flight delays are reduced by 45.94% and 58.42% respectively. Based on the current operations and the op-timized engineering technology, the number of flight groupings during the entire period is 29 and 34, respectively. The maximum number of flight groupings during peak hours is 3, which verifies the effectiveness of optimizing the flight grouping procedure in improving overall operational efficiency in the Cul-de-sac area under complex configu-rations and scenarios.
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图 2 港湾区航空器运行9类冲突
Figure 2. Nine categories of conflicts in aircraft operations in the Cul-de-sac area
图 6 西北港湾区全天航班流量分布
Figure 6. The northwest Cul-de-sac area all day flight traffic distribution
图 7 西北港湾区交通工程技术集优化图
Figure 7. The northwest Cul-de-sac area transportation engineering technology set optimization map
图 9 3种情况下全时段平均运行时间对比
Figure 9. Comparison of full-time average running time for three scenarios
图 10 3种情况下离港平均运行时间对比
Figure 10. Comparison of average departure running times under three scenarios
图 11 3种情况下进港平均运行时间对比
Figure 11. Comparison of average arrival running times under three scenarios
图 12 全时段航班平均延误时间对比
Figure 12. Comparison of average delay time of flights during the whole time period
图 13 全时段航班编组累计组数
Figure 13. Cumulative number of flight formations for the full time slot
表 1 西北港湾区部分航班数据
Table 1. Partial flight data from the northwest Cul-de-sac area
机位号 航班号 进/离港时间 航班属性 256 CZ3810 00：05 A 266 CZ8776 00：10 D 265 CZ3430 00：15 A 268 CZ3233 00：30 D 266 CZ3508 00：35 A 269 CZ6247 00：35 D 
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[1] 吴浩宁, 郭雁池, 牧彤, 等. 北京新机场站坪滑行道运行模式仿真研究[J]. 交通运输系统工程与信息, 2016, 16(3): 214-220, 234.WU H N, GUO Y C, MU T. Apron taxiway operation mode and simulation evaluation in Beijing new airport[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(3): 214-220, 234. (in Chinese) [2] 杨太阳, 欧阳杰. 枢纽机场空侧港湾区运行优化研究[J]. 综合运输, 2023, 45(3): 46-52.YANG T Y, OUYANG J. Airside cul-de-sac operational optimization in hub-airport[J]. China Transportation Review, 2023, 45(3): 46-52. (in Chinese) [3] LIU Y, HU M, YIN J, et al. Optimization design and performance evaluation of U-shaped area operation procedures in complex apron[J]. Aerospace, 2023, 10(2): 161. doi: 10.3390/aerospace10020161 [4] 刘禹汐, 刘继新, 田文. 复杂机坪运行模式下的停机位分配协同优化[J]. 哈尔滨商业大学学报(自然科学版), 2023, 39 (5): 619-627.LIU Y X, LIU J X, T W. Collaborative optimization of gate assignment in complex apron operation mode[J]. Journal of Harbin University of Commerce (Natural Sciences Edition), 2023, 39(5): 619-627. (in Chinese) [5] 刘禹汐, 刘继新, 田文. 基于机坪运行安全的停机位预分配综合优化[J]. 航空计算技术, 2023, 53(4): 61-65.LIU Y X, LIU J X, T W. Comprehensive optimization of gate pre-allocation based on apron operating safety[J]. Aeronautical Computing Technique, 2023, 53(4): 61-65. (in Chinese) [6] OUYANG J, ZHU C Q, TANG X W, et al. Multi-stand grouped operations method in airport bay area based on deep reinforcement learning[J]. Aerospace, 2025, 12(5), 398. doi: 10.3390/aerospace12050398 [7] 常祎雯, 王雪锋, 李品. 基于数据分析的机场港湾冲突检测算法研究[J]. 航空计算技术, 2022, 52(4): 61-64, 69.CHANG Y W, WANG X F, LI P. Research on airport harbor conflict detection algorithm based on data analysis[J]. Aeronautical Computing Technique, 2022, 52(4): 61-64, 69. (in Chinese) [8] 姬莉莉, 周督异, 王兴隆. 基于航空器态势的三通道U形港湾区运行冲突识别[J]. 安全与环境学报, 2025, 25(2): 546-557.JI L L, ZHOU D Y, WANG X L. Conflict identification in three-channel U-shaped bay area operation based on aircraft situational awareness[J]. Journal of Safety and Environment, 2025, 25(2): 546-557. (in Chinese) [9] 李润, 梁其钊. 广州白云机场协同运行效能分析与提质增效措施[J]. 民航管理, 2021(5): 52-57.LI R, LIANG Q Z. Analysis on collaboration effectiveness and measures to improve quality and efficiency in Guangzhou Baiyun airport[J]. Civil Aviation Management, 2021(5): 52-57. (in Chinese) [10] 陈江, 施新颜, 刘晏滔. 虹桥机场飞机推出程序设计优化研究及应用[J]. 上海空港, 2011, 12: 10-14.CHEN J, SHI X Y, LIU Y T. Research and application of optimization of aircraft pushback procedure design at Hongqiao airport[J]. Shanghai Airports, 2011, 12: 10-14. (in Chinese) [11] LINGEN W V, ROLING P C. Modelling the effects of gate planning on apron congestion[C]. AIAA Aviation 2019 Forum, Dallas, USA: AIAA, 2019. [12] 潘卫军, 张启阳, 朱新平. 基于停止点前移的推出程序优化设计[J]. 航空计算技术, 2021, 51(5): 38-41, 45.PAN W J, ZHANG Q Y, ZHU X P. Optimal design of push-back program based on stop position moving forward[J]. Aeronautical Computing Technique, 2021, 51(5): 38-41, 45. (in Chinese) [13] 李治寒, 朱新平, 张天雄, 等. 大型机场单通道U型机坪区推出等待点优化设计[J]. 科学技术与工程, 2023, 23(29): 12744-12752.LI Z H, ZHU X P, ZHANG T X. Optimal design of push-back spot in single-aisle U-shaped apron area of large airports[J]. Science Technology and Engineering, 2023, 23(29): 12744-12752. (in Chinese) [14] 陈华群, 朱兴澳, 于海旺. 基于蒙特卡罗轨迹偏差的机场临时机位划设优化[J]. 中国安全科学学报, 2023, 33(11): 82-88.CHEN H Q, ZHU X A, YU H W. Allocation optimization of temporary parking in airport based on Monte Carlo trajectory deviation[J]. China Safety Science Journal, 2023, 33(11): 82-88. (in Chinese) [15] 张宝成, 冉博文. 大规模飞机排班问题研究综述[J]. 交通信息与安全, 2024, 42(1): 1-10, 27. doi: 10.3963/j.jssn.1674-4861.2024.01.001ZHANG B C, RAN B W. A review of studies on large-scale aircraft scheduling problems[J]. Journal of Transport Information and Safety, 2024, 42(1): 1-10, 27. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2024.01.001 [16] 薛钰婷, 周航, 姜子棋, 等. 面向U型机坪的停机位分配优化研究[J]. 航空计算技术, 2022, 52(6): 59-63.XUE Y T, ZHOU H, JIANG Z Q, et al. Optimization of gate assignment for U-shaped aprons[J]. Aeronautical Computing Technique, 2022, 52(6): 59-63. (in Chinese) [17] 程小慷, 曹誉丹, 崔宇婕. 基于改进PSO的离港航班绿色滑行路径优化[J]. 航空计算技术, 2023, 53(3): 25-29.CHENG X K, CAO Y D, CUI Y J. Green taxi path optimization of departing flights based on improved PSO algorithm[J]. Aeronautical Computing Technique, 2023, 53(3): 25-29. (in Chinese) [18] 程琳, 宁翊森, 宋茂灿. 拉格朗日松弛启发式算法求解时空网络下的弧路径问题[J]. 交通运输工程学报, 2022, 22 (4): 273-284.CHENG L, NING Y S, SONG M C. Lagrangian relaxation heuristic algorithm of arc routing problem under time-space network[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 273-284. (in Chinese) [19] 寇伟彬, 于凯任, 王佳玉, 等. 韧性导向的机场航空器滑行路径及停机位联合优化[J/OL]. (2024-02-27)[2024-11-18]. https://doi.org/10.13700/j.bh.1001-5965.2023.0801 .KOU W B, YU K R, WANG J Y, et al. Resilience-oriented joint optimization of aircraft taxiing route and apron assignment in airport[J/OL](. 2024-02-27)[2024-11-18].https://doi.org/10.13700/j.bh.1001-5965.2023.0801 .[20] HANCERLIOGULLARI G, RABADI G, Al-SALEM A H, et al. Greedy algorithms and metaheuristics for a multiple runway combined arrival-departure aircraft sequencing problem[J]. Journal of Air Transport Management, 2013, 32: 39-48 [21] 姜雨, 刘振宇, 胡志韬, 等. 大型机场进场航空器联合调度模型[J]. 交通运输工程学报, 2022, 22(1): 205-215.JIANG Y, LIU Z Y, HU Z T, et al. Coordinated scheduling model of arriving aircraft at large airport[J]. Journal of Traffic and Transportation Engineering, 2022, 22(1): 205-215. (in Chinese) -
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