基于空中交通密度的进场航班动态协同排序方法

doi:10.7527/S1000-6893.2020.23717

电子电气工程与控制

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

基于空中交通密度的进场航班动态协同排序方法

刘继新^1,2, 江灏^1,2, 董欣放^1,2, 兰思洁^1,2, 王浩哲³

1. 南京航空航天大学民航学院, 南京 211106;
2. 国家空管飞行流量管理技术重点实验室, 南京 211106;
3. 中国民用航空华北地区空中交通管理局天津分局, 天津 300300

收稿日期:2019-12-08 修回日期:2020-02-07 出版日期:2020-07-15 发布日期:2020-04-10
通讯作者: 江灏 E-mail:2219463078@qq.com

Dynamic collaborative sequencing method for arrival flights based on air traffic density

LIU Jixin^1,2, JIANG Hao^1,2, DONG Xinfang^1,2, LAN Sijie^1,2, WANG Haozhe³

1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
2. National Key Laboratory of Air Traffic Flow Management, Nanjing 211106, China;
3. Air Traffic Management Bureau of Tianjin Civil Aviation Administration, Tianjin 300300, China

Received:2019-12-08 Revised:2020-02-07 Online:2020-07-15 Published:2020-04-10

摘要/Abstract

摘要： 为适应协同决策（CDM）需要，考虑空管、航空公司和机场的诉求，对进场航班动态协同排序问题进行了系统的研究。设计了一种进场航班动态排序方法，提出了一种时隙交换方法，建立了基于空中交通密度的进场航班协同排序模型，设计了精英保留的遗传算法和带精英策略的快速非支配排序遗传算法以求解所建模型，寻求进场航班动态协同排序的最优解。仿真结果表明，较基于滚动时域控制（RHC）方法，动态协同方法所得结果与排序开始时间无关，所需排序次数平均减少26.4%，且排序效率更高。较先到先服务（FCFS）方法，动态协同方法在高密度条件下各排序阶段最后一个进场航班的落地时间平均提前199.8 s；中密度条件下各排序阶段航班延误总时间平均减少29.9%，航班延误均衡性平均提高34.4%；低密度条件在航班正常率及航班延误公平性得到保证的前提下，满足时隙交换规则的排序阶段均增加了1种进场航班排序模式。所提方法可对进场航班进行优化排序，显著提高跑道容量，有效提升航班延误均衡性和航班延误公平性，契合协同决策理念，可实现三方协同排序。

关键词: 空中交通管制, 协同决策, 进场排序, 时隙交换, 多目标优化, 遗传算法

Abstract: To meet the needs of Collaborative Decision Making (CDM), dynamic collaborative sequencing of arrival flights is systematically studied, considering the demands of air traffic control units, airlines and airports. A dynamic sequencing method for arrival flights is designed, a slot exchange method is proposed, and a collaborative sequencing model based on air traffic density is built. A genetic algorithm with the elitist reservation and a fast non-dominated sorting genetic algorithm with the elitist strategy are designed to achieve the optimal solution of dynamic collaborative sequencing of arrival flights. Compared with those of the Receding Horizon Control (RHC) method, the results of the dynamic collaborative method are independent of the start time of sequencing with the required sequencing times reduced by 26.4% on average, leading to higher sequencing efficiency. Compared with that of the First Come First Service (FCFS) method, under the condition of high density, the landing time of the last arrival flight in each sequencing stage is 199.8 s ahead of schedule on average with the dynamic collaboration method; under the condition of medium density, the total flight delay of each sequencing stage is reduced by 29.9% on average, while the flight delay equilibrium is increased by 34.4% on average; under the condition of low density, with the premise that the punctuality rate of arrival flights and the fairness of flight delays are guaranteed, one sequencing mode of arrival flights is added if the sequencing stage satisfies the slot exchange rules. The proposed method can optimize the sequencing of arrival flights, significantly enhancing the runway capacity and effectively improving the flight delay equilibrium and fairness. In line with the concept of collaborative decision making, this method can achieve collaborative sequencing of ATC, airlines and airports.

Key words: air traffic control, collaborative decision making, arrival sequencing, slot exchange, multi-objective optimization, genetic algorithm

中图分类号:

刘继新, 江灏, 董欣放, 兰思洁, 王浩哲. 基于空中交通密度的进场航班动态协同排序方法[J]. 航空学报, 2020, 41(7): 323717-323717.

LIU Jixin, JIANG Hao, DONG Xinfang, LAN Sijie, WANG Haozhe. Dynamic collaborative sequencing method for arrival flights based on air traffic density[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(7): 323717-323717.

参考文献 22

[1]	中国民用航空局. 2018年民航行业发展统计公报[R]. 北京:中国民用航空局, 2019. Civil Aviation Administration of China. Development statistics bulletin of civil aviation industry in 2018[R]. Beijing:Civil Aviation Administration of China, 2019(in Chinese).
[2]	HU X B, CHEN W H. Receding horizon control for aircraft arrival sequencing and scheduling[J]. IEEE Transactions on Intelligent Transportation Systems, 2005, 6(2):189-197.
[3]	HU X B, PAOLO E D. Binary-representation-based genetic algorithm for arrival sequencing and scheduling[J]. IEEE Transaction on Intelligent Transportation Systems, 2008, 9(2):301-310.
[4]	HU X B, PAOLO E D. An efficient genetic algorithm with uniform crossover for air traffic control[J]. Computers & Operations Research, 2009, 36(1):245-259.
[5]	张启钱, 胡明华, 施赛锋. 基于RHC的航班着落调度多目标优化算法[J]. 南京航空航天大学学报, 2012, 44(3):393-398. ZHANG Q Q, HU M H, SHI S F. Multi-object optimization algorithm for aircraft landing based on receding horizon control strategy[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2012, 44(3):393-398(in Chinese).
[6]	王菲, 张军峰, 葛腾腾, 等. 基于分支定界的离场航空器动态排序[J]. 南京航空航天大学学报, 2015, 47(4):547-552. WANG F, ZHANG J F, GE T T, et al. Dynamic departure sequencing based on branch and bound algorithm[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2015, 47(4):547-552(in Chinese).
[7]	张军峰, 王菲, 葛腾腾. 基于分支定界法的进场航空器动态排序与调度[J]. 系统仿真学报, 2016, 28(8):1909-1914. ZHANG J F, WANG F, GE T T. Dynamic arrival sequencing & scheduling based on branch & bound algorithm[J]. Journal of System Simulation, 2016, 28(8):1909-1914(in Chinese).
[8]	王菲. 多跑道机场进离场航空器协同排序研究[D]. 南京:南京航空航天大学, 2016. WANG F. Research on collaborative sequencing of arrival and departure aircrafts in multi-runway airport[D]. Nanjing:Nanjing University of Aeronautics and Astronautics, 2016(in Chinese).
[9]	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. 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).
[10]	马园园, 胡明华, 尹嘉男, 等. 多机场终端区进离场交通流协同排序方法[J]. 航空学报, 2017, 38(2):225-237. MA Y Y, HU M H, YIN J N, et al. Collaborative sequencing and scheduling method for arrival and departure traffic flow in multi-airport terminal area[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(2):225-237(in Chinese).
[11]	葛腾腾. 多机场终端空域进离场协同调度研究[D]. 南京:南京航空航天大学, 2017. GE T T. Research on collaborative scheduling of arrival and departure aircrafts in multi-airport terminal area[D]. Nanjing:Nanjing University of Aeronautics and Astronautics, 2017(in Chinese).
[12]	张军峰, 葛腾腾, 郑志祥. 多机场终端区进离场航班协同排序研究[J]. 交通运输系统工程与信息, 2017, 17(2):197-204. ZHANG J F, GE T T, ZHENG Z X. Collaborative arrival and departure sequencing for multi-airport terminal area[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(2):197-204(in Chinese).
[13]	SALEHIPOUR A, MODARRES M, NAENI L M. An efficient hybrid meta-heuristic for aircraft landing problem[J]. Computers & Operations Research, 2013, 40:207-213.
[14]	ZHAN Z H, ZHANG J, LI Y, et al. An efficient ant colony system based on receding horizon control for the aircraft arrival sequencing and schedule problem[J]. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(2):399-412.
[15]	GULSAH H, GHAITH R, AMEER H A, 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.
[16]	CAO Y, RATHINAM S, SUN D F. Greedy-heuristic-aided mixed-integer linear programming approach for arrival scheduling[J]. Journal of Aerospace Information Systems, 2013, 10(7):323-336.
[17]	PINOL H, BEASLEY J E. Scatter search and bionomic algorithms for the aircraft landing problem[J]. European Journal of Operational Research, 2006, 171:439-62.
[18]	中国民用航空局. 民用航空空中交通管理规则:CCAR-93-R5[S]. 北京:中国民用航空局, 2018. Civil Aviation Administration of China. Civil aviation air traffic management rules:CCAR-93-R5[S]. Beijing:Civil Aviation Administration of China, 2018(in Chinese).
[19]	中国民用航空局. 民航航班正常统计办法:CCAR-93-R5[S]. 北京:中国民用航空局, 2012. Civil Aviation Administration of China. Measures for normal statistics of civil aviation flights:CCAR-93-R5[S]. Beijing:Civil Aviation Administration of China, 2012(in Chinese).
[20]	赵嶷飞, 陈凯, 刘刚, 等. 一种新的扇区拥挤告警指标及应用[J]. 中国安全科学学报, 2009, 19(3):103-107. ZHAO Y F, CHEN K, LIU G, et al. A new flow alert index for sector congestion and its application[J]. China Safety Science Journal, 2009, 19(3):103-107(in Chinese).
[21]	DE JONG K A. An analysis of the behavior of a class of genetic adaptive systems[D]. Michigan:University of Michigan, 1975.
[22]	DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multi-objective genetic algorithm:NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2):182-197.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于空中交通密度的进场航班动态协同排序方法

Dynamic collaborative sequencing method for arrival flights based on air traffic density

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献 22

相关文章 15

编辑推荐

Metrics

本文评价

[1]	崔壮壮, 原昕, 赵国庆, 井思梦, 招启军. 共轴刚性旋翼高速直升机前飞性能操纵策略影响[J]. 航空学报, 2024, 45(9): 529256-529256.
[2]	杨倩, 郑皓冉, 程显达, 董威. 基于引气控制的热气防冰优化设计方法[J]. 航空学报, 2023, 44(S2): 729285-729285.
[3]	丁文俊, 柴亚军, 侯冬冬, 王驰宇, 张国宗, 毛昭勇. AUV&UAV跨域协同搜索与跟踪路径规划[J]. 航空学报, 2023, 44(21): 528471-528471.
[4]	董杰忠, 楚武利, 张皓光, 罗波, 晏松. 基于因果网络分析的扩压叶栅波浪形前缘控制机理[J]. 航空学报, 2023, 44(19): 128336-128336.
[5]	毕文豪, 周久力, 段晓波, 张安, 徐双飞. 基于多要素改进NSGA-Ⅱ的小直径制导炸弹空面打击最优火力分配方法[J]. 航空学报, 2023, 44(17): 328116-328116.
[6]	王彬, 郑建军, 刘玮, 王高利. 基于考核目标等效的试验载荷处理方法[J]. 航空学报, 2023, 44(17): 228064-228064.
[7]	梁超, 杨力, 潘成胜, 戚耀文. 卫星网络动态资源图多QoS约束路由算法[J]. 航空学报, 2023, 44(1): 326422-326422.
[8]	邹子缘, 陈琪锋. 基于决策树搜索的空间飞行器集群对抗目标分配方法[J]. 航空学报, 2022, 43(S1): 726910-726910.
[9]	韩凯, 董日昌, 邵丰伟, 龚文斌, 常家超. 基于改进遗传算法的导航卫星星间链路网络动态拓扑优化技术[J]. 航空学报, 2022, 43(9): 326095-326095.
[10]	孙刚, 彭双, 陈浩, 伍江江, 李军. 面向测控数传资源一体化场景的卫星地面站资源多目标优化方法[J]. 航空学报, 2022, 43(9): 326114-326114.
[11]	郑帅, 王子涵, 赵浩然, 杨朋涛, 洪军. 基于差分进化算法的飞机油量传感器布局优化方法[J]. 航空学报, 2022, 43(8): 125809-125809.
[12]	胡嘉欣, 芮姝, 高瑞朝, 苟建军, 龚春林. 飞行器结构布局与尺寸混合优化方法[J]. 航空学报, 2022, 43(5): 225363-225363.
[13]	荆涛, 田锡天. 基于蒙特卡洛-自适应差分进化算法的飞机容差分配多目标优化方法[J]. 航空学报, 2022, 43(3): 425278-425278.
[14]	段焰辉, 周鑫, 高诗婕茜, 林锦星, 张怀宝, 王光学. 基于两步优化策略的结构网格负载平衡[J]. 航空学报, 2021, 42(9): 625751-625751.
[15]	罗庆, 张涛, 单鹏, 张文涛, 刘子豪. 基于改进Q学习的IMA系统重构蓝图生成方法[J]. 航空学报, 2021, 42(8): 525792-525792.