飞行区进离场航班跑道和滑行道双层协同优化调度方法

夏朝禹; 刘卫东; 胡明华; 侯昌波; 文轶; 杨柯

doi:10.3963/j.jssn.1674-4861.2024.05.005

飞行区进离场航班跑道和滑行道双层协同优化调度方法

doi: 10.3963/j.jssn.1674-4861.2024.05.005

夏朝禹^{1, 2, 3, ,},
刘卫东²,
胡明华¹,
侯昌波²,
文轶²,
杨柯²

1.
南京航空航天大学民航学院南京 210016
2.
中国民用航空总局第二研究所成都 610041
3.
民用无人驾驶航空器交通管理四川省重点实验室成都 610041

基金项目:

国家重点研发计划项目 2022YFB2602405

成都市“揭榜挂帅”项目 2021-JB00-00025-GX

详细信息

通讯作者:
夏朝禹（1994—），助理研究员. 研究方向：协同式空中交通管理. E-mail：xcycuit@163.com

中图分类号: U8
计量
- 文章访问数: 77
- HTML全文浏览量: 30
- PDF下载量: 12
- 被引次数: 0
出版历程
- 收稿日期: 2024-04-20
- 网络出版日期: 2025-01-22

A Bi-layer Coordinated Optimization Scheduling Method of Runway and Taxiway for Arriving and Departing Flights in Airfield Area

XIA Chaoyu^{1, 2, 3
, ,},
LIU Weidong²,
HU Minghua¹,
HOU Changbo²,
WEN Yi²,
YANG Ke²

1.
Civil Aviation College, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2.
The Second Research Institute of the Civil Aviation Administration of China, Chengdu 610041, China
3.
Civil Unmanned Aircraft Traffic Management Key Laboratory of Sichuan Province, Chengdu 610041, China

摘要

摘要: 针对大型枢纽机场飞行区跑道与滑行道调度尚未形成级联运行模式，导致跨域异质航班流无法实现有限机场容量下统筹规划及合理调度等问题，研究了飞行区进离场航班跑道和滑行道双层协同优化调度方法。在跑道调度阶段，提出考虑跑道更换成本的联合进离场多跑道调度模型，通过最小化更换跑道的无阻碍滑行时间，在保证跑道累计延误量最小的同时尽可能抑制额外产生的滑行时间。在场面滑行调度阶段，最小化航班累计总滑行时间和跑道调度期望离场时间偏差，将复杂场面航班运行的逻辑关系以及管制规则精确抽象为多元线性化约束条件，采用闭环反馈修订机制防止跑道-滑行道协同优化调度中策略不匹配问题。以成都双流国际机场和天府国际机场为实验场景进行模拟验证。仿真结果表明：相比于先到先服务策略，首次迭代单架航班平均跑道延误时间缩短16.9 s，累计航班滑行时间平均可降低14.27%；采用闭环反馈修订机制后平均能够在1.35次迭代内找到相互匹配的调度计划。同时分析了所提3类不同双层协同优化调度策略性能优劣。本文方法有助于推动建立综合化的进离场及场面协同管理技术体系，促使形成以数字驱动为核心的飞行区交通流精细化控制能力。
- 空中交通管理 /
- 进离场与场面调度 /
- 多跑道排序 /
- 滑行规划 /
- 双层协同优化
Abstract: The runway and taxiway scheduling of large hub airports has not yet formed a cascade operation mode, leading to the inability to achieve coordinated planning and reasonable scheduling of cross-domain and heterogeneous flight flows under limited capacity of airports. This paper studies a bi-layer coordinated optimization scheduling method of runway and taxiway for arriving and departing flights in the airfield area. In the runway scheduling stage, a joint arriving and departing scheduling model of multi runways considering the cost of runway changing is proposed, which quantifies and minimizes the cost of unimpeded taxiing time for flights that change runways, and suppresses the extra taxiing time generated as far as possible while ensuring the minimum cumulative delays on runways. In the taxiway scheduling stage, a surface taxiing scheduling model is developed by minimizing the deviation between the total cumulative taxiing time of flights and the expected departure time of runway scheduling, which is used to plan the timing and sorting of arriving and departing flights at each metering point. Finally, a closed-loop mechanism of feedback-revision is adopted to prevent the mismatch in bi-layer coordinated optimization scheduling. Simulation and verification are conducted by taking Shuangliu International Airport and Tianfu International Airport in Chengdu as scenarios. The results show that runway delay time of single aircraft is reduced by 16.9 s and the cumulative flight taxiing time is reduced by 14.27% on average after the first iteration comparing to the first-come-first-served strategy, and that a matching scheduling plan can be found within 1.35 iterations on average if the closed-loop mechanism of feedback-revision is adopted. Meanwhile, the performance of 3 different types of bi-layer coordinated optimization scheduling strategies is analyzed. The method proposed in this paper is helpful to promote the technology system of comprehensive coordinated management for arrival, departure and surface, and to the formation of refined control capabilities of the airfield traffic flows with a digitally-drive core.
- air traffic control /
- integrated arrival and departure and surface scheduling /
- multi-runway sequencing /
- Glide plan /
- bi-layer coordinated optimization

HTML全文

图 1 机场飞行区进离场与场面运行一体化调度过程

Figure 1. Integration of arrival-departure and surface operations scheduling in airfield area

下载: 全尺寸图片幻灯片

图 2 跑道-滑行道协同调度反馈机制

Figure 2. Integrated Runway and Taxiway Scheduling Feedback Mechanism

下载: 全尺寸图片幻灯片

图 3 RCADS调度结果对比FCFS调度结果

Figure 3. Comparison of runway scheduling results between the RCADS and FCFS

下载: 全尺寸图片幻灯片

图 4 ASM调度结果对比FCFS（场面滑行）调度结果

Figure 4. Comparison of surface scheduling results between the ASM and FCFS(Surface taxiing)

下载: 全尺寸图片幻灯片

表 1 连续航班对类型尾流时间间隔

Table 1. Minimum runway separation criteria for various types of the leader-follow aircraft 单位: s

前序进场航班	后序进场航班				前序离场航班	后序离场航班
前序进场航班	轻型	中型	大型	重型	前序离场航班	轻型	中型	大型	重型
轻型	87	76	76	69	轻型	60	60	60	60
中型	145	101	76	69	中型	60	60	60	60
大型	145	101	101	103	大型	60	60	60	60
重型	174	127	127	103	重型	60	60	60	60

前序离场航班	后序进场航班				前序进场航班	后序离场航班
前序离场航班	轻型	中型	大型	重型	前序进场航班	轻型	中型	大型	重型
轻型	112	99	99	99	轻型	70	70	70	70
中型	112	99	99	99	中型	70	70	70	70
大型	112	99	99	99	大型	70	70	70	70
重型	112	99	99	99	重型	70	70	70	70

下载: 导出CSV

表 2 不同案例下航班计划信息

Table 2. Aircraft information for various instances

案例	航班总数/架	每种类型航班数/架				计划时间窗区间	平均架次/min	进/离场航班
案例	航班总数/架	轻型	中型	大型	重型	计划时间窗区间	平均架次/min	进/离场航班
案例1	8	2	4	2	0	[60, 860]	0.60	4/4
案例2	11	1	8	1	1	[100, 780]	0.97	7/4
案例3	11	2	6	2	1	[100, 700]	1.10	4/7
案例4	14	2	8	1	3	[100, 800]	1.20	5/9
案例5	14	3	7	2	2	[100, 800]	1.20	4/10
案例6	16	3	9	1	3	[100, 800]	1.37	7/9
案例7	17	3	10	3	1	[100, 800]	1.46	7/10
案例8	17	4	7	2	3	[100, 800]	1.46	5/12

下载: 导出CSV

表 3 案例5航班飞行计划

Table 3. Flight plan in instance 5

航班号	航班类型	ETA/ETD	航班号	航班类型	ETA/ETD
A1	中型	100	D4	中型	360
A2	大型	140	D5	轻型	420
A3	中型	320	D6	大型	560
A4	重型	400	D7	中型	630
D1	中型	130	D8	中型	700
D2	中型	180	D9	轻型	200
D3	轻型	270	D10	重型	800

下载: 导出CSV

表 4 ZUUU首次迭代中多跑道调度模型性能分析

Table 4. Performance analysis of multi runway scheduling model in the first iteration at ZUUU

案例	累计总延误时/s			单架航班最大延误量/s			GPU计算耗时/s			离场航班累计无阻碍滑行时间/s			对比FCFS算法性能提升
	累计总延误时/s			单架航班最大延误量/s			GPU计算耗时/s			离场航班累计无阻碍滑行时间/s			CADS	RCADS
	FCFS	CADS	RCADS	FCFS	CADS	RCADS	FCFS	CADS	RCADS	FCFS	CADS	RCADS	提升率/%	提升率/%
1	49	0	0	49	0	0	0.23	< 0.5	< 0.5	2 580	2 160	2 160	100	100
2	129	46	46	59	21	21	0.24	2.13	1.64	1 920	1 560	1 560	64.34	64.34
3	304	51	51	90	30	30	0.24	2.28	1.70	4 500	3 060	3 060	83.22	83.22
4	457	126	126	99	60	60	0.23	13.50	12.16	6 120	4 980	4 920	72.43	72.43
5	474	108	114	126	46	46	0.23	17.84	10.02	5 280	5 280	4 260	77.22	75.95
6	1 458	149	149	216	61	61	0.23	28.89	20.44	5 760	3 840	3 840	89.78	89.78
7	565	80	80	148	47	47	0.24	22.09	17.45	5 220	4 380	4 380	85.84	85.84
8	1 217	236	236	184	94	94	0.24	233.14	113.02	7 680	6 300	6 300	74.88	74.88

下载: 导出CSV

表 5 ZUTF首次迭代中多跑道调度模型性能分析

Table 5. Performance analysis of multi runway scheduling model in the first iteration at ZUTF

案例	累计总延误时/s			单架航班最大延误量/s			GPU计算耗时/s			离场航班累计无阻碍滑行时间/s			对比FCFS算法性能提升
	累计总延误时/s			单架航班最大延误量/s			GPU计算耗时/s			离场航班累计无阻碍滑行时间/s			CADS	RCADS
	FCFS	CADS	RCADS	FCFS	CADS	RCADS	FCFS	CADS	RCADS	FCFS	CADS	RCADS	提升率/%	提升率/%
1	32	0	0	32	0	0	< 0.1	0.89	0.69	1 920	1 980	1 920	100	100
2	209	0	9	89	0	9	< 0.1	12.97	7.40	2 160	2 160	1 980	100	95.63
3	99	0	0	69	0	0	< 0.1	8.42	7.91	4 740	4 080	4 080	100	100
4	95	27	57	39	27	30	< 0.1	73.41	31.76	4 380	5 520	4 200	71.58	68.42
5	208	0	60	89	0	50	< 0.1	62.28	42.83	5 820	6 420	4 920	100	71.15
6	153	30	30	57	20	20	< 0.1	52.25	38.32	4 800	4 920	4 560	80.39	80.39
7	273	0	0	90	0	0	< 0.1	170.28	116.31	5 820	5 820	5 820	100	100
8	113	30	40	57	10	30	< 0.1	162.05	111.31	6 660	5 700	5 580	73.45	64.60

下载: 导出CSV

表 6 ASM调度与RCADS跑道调度平均离场调度时间偏差

Table 6. Average departure scheduling time deviation between ASM scheduling and RCADS runway scheduling 单位: s

案例	ZUUU	ZUTF	案例	ZUUU	ZUTF
案例1	0	5.0	案例5	16.4	2.2
案例2	0	14.0	案例6	0	22.0
案例3	16	3.5	案例7	10.2	2.0
案例4	28	8.7	案例8	11.9	4.4

下载: 导出CSV

表 7 协同跑道和滑行道级联优化策略

Table 7. Coordinated optimization scheduling of runway and taxiway

策略	名称	定义
A	FCFS-ASM（FCFS）	跑道调度采用FCFS策略，路径规划采用ASM。迭代反馈时跑道调度采用FCFS策略
B	RCADS-ASM（FCFS）	跑道调度采用RCADS策略，路径规划采用ASM。迭代反馈时跑道调度采用FCFS策略
C	RCADS-ASM（RCADS）	跑道调度采用RCADS策略，路径规划采用ASM。迭代反馈时跑道调度采用RCADS策略

下载: 导出CSV

表 8 ZUUU级联双层协同优化调度有效性分析（标称滑行路径）

Table 8. Effectiveness analysis of ZUUU double-layer collaborative optimization scheduling (nominal path)

案例	优化策略	累计跑道延误时间/s	离场航班停机位等待/s	累计滑行时间/s	离场航班累计滑行时间/s	进场航班累计滑行时间/s	额外迭代次数/次	额外迭代平均跑道调度耗时/s	额外迭代平均场面调度耗时/s
	A	49	585	5 112	1 995	3 117	0
1	B	0	889	4 808	1 691	3 117	0
	C	0	889	4 808	1 691	3 117	0
	A	129	338	7 131	1 582	5 549	0
2	B	46	560	6 909	1 360	5 549	0
	C	46	560	6 909	1 360	5 549	0
	A	870	1 054	6 583	3 938	2 645	2	< 0.1	< 0.1
3	B	163	1 980	5 307	2 662	2 645	1	< 0.1	< 0.1
	C	163	1 980	5 307	2 662	2 645	1	1.97	< 0.1
	A	1 889	833	10 600	6 470	4 130	2	< 0.1	0.10
4	B	500	2 212	8 611	4 286	4 325	1	< 0.1	< 0.1
	C	385	2 907	7 727	3 598	4 129	2	5.39	0.11
	A	692	1 193	7 330	4 685	2 645	2	< 0.1	< 0.1
5	B	278	1 896	6 392	3 662	2 730	1	< 0.1	< 0.1
	C	278	1 896	6 392	3 662	2 730	1	5.97	0.10
	A	2 706	1 292	11 037	5 665	5 372	1	< 0.1	< 0.1
6	B	149	2 721	8 085	3 089	4 996	0
	C	149	2 721	8 085	3 089	4 996	0
	A	749	1 266	10 003	4 342	5 661	1	< 0.1	0.17
7	B	182	1 616	9 368	3 706	5 662	2	< 0.1	0.14
	C	182	1 616	9 368	3 706	5 662	2	13.08	0.14
	A	2 657	1 959	11 305	7 417	3 888	1	< 0.1	< 0.1
8	B	443	2 370	9 608	5 717	3 891	2	< 0.1	< 0.1
	C	445	3 060	8 916	5 025	3 891	2	14.53	< 0.1

下载: 导出CSV

表 9 ZUTF级联双层协同优化调度有效性分析（标称滑行路径）

Table 9. Effectiveness analysis of ZUTF double-layer collaborative optimization scheduling (nominal path)

案例	优化策略	累计跑道延误时间/s	离场航班停机位等待/s	累计滑行时间/s	离场航班累计滑行时间/s	进场航班累计滑行时间/s	额外迭代次数/次	额外迭代平均跑道调度耗时/s	额外迭代平均场面调度耗时/s
	A	52	138	3 214	1 802	1 412	1	< 0.1	< 0.1
1	B	20	138	3 214	1 802	1 412	1	< 0.1	< 0.1
	C	20	138	3 214	1 802	1 412	1	0.58	< 0.1
	A	460	254	4 713	2 137	2 576	2	< 0.1	< 0.1
2	B	9	1 120	3 567	1 040	2 527	0
	C	9	1 120	3 567	1 040	2 527	0
	A	99	684	6 165	4 086	2 079	0
3	B	25	1 226	5 618	3 539	2 079	1	< 0.1	< 0.1
	C	25	1 226	5 618	3 539	2 079	1	6.47	< 0.1
	A	233	709	5 749	3 789	1 960	2	< 0.1	< 0.1
4	B	135	775	5 673	3 713	1 960	2	< 0.1	< 0.1
	C	135	775	5 673	3 713	1 960	2	21.88	< 0.1
	A	291	892	6 706	5 049	1 657	2	< 0.1	< 0.1
5	B	82	1 806	5 753	4 096	1 657	2	< 0.1	< 0.1
	C	82	1 806	5 753	4 096	1 657	2	14.13	< 0.1
	A	344	600	6 431	4 471	1 960	3	< 0.1	0.13
6	B	228	608	6 380	4 420	1 960	3	< 0.1	0.14
	C	228	608	6 380	4 420	1 960	3	11.21	0.14
	A	293	1 095	6 885	4 845	2 040	1	< 0.1	< 0.1
7	B	39	1 005	6 885	4 845	2 040	1	< 0.1	< 0.1
	C	39	1 005	6 885	4 845	2 040	1	31.14	< 0.1
	A	169	1 082	7 694	5 734	1 960	4	< 0.1	0.12
8	B	93	1 836	6 937	4 977	1 960	3	< 0.1	0.13
	C	93	1 836	6 937	4 977	1 960	3	21.58	0.13

下载: 导出CSV

参考文献(20)

[1]	尹嘉男, 马园园, 胡明华. 机场飞行区资源调度问题研究(一): 基本概念与框架[J]. 航空工程进展, 2019, 10 (3): 289-301. YIN J N, MA Y Y, HU M H. The Airfield resource scheduling problem - part Ⅰ: basic concepts and framework[J]. Aviation Engineering Progress, 2019, 10(3): 289-301. (in Chinese)
[2]	BO X, WEIMIN M, HUA K, et al. An efficient ant colony algorithm based on rank 2 matrix approximation method for aircraft arrival departure scheduling problem[J]. Processes, 2022, 10 (9): 1825-1825. doi: 10.3390/pr10091825
[3]	姜雨, 王欢, 樊卫国, 等. 场面航空器滑行时空协同优化模型[J]. 交通运输工程学报, 2019, 19 (1): 127-135. JIANG Y, WANG H, FAN W G, et al. Spatio - temporary cooperative optimization model of surface aircraft taxiing[J]. Journal of Traffic and Transportation Engineering, 2019, 19(1): 127-135. (in Chinese)
[4]	JIANG H, LIU J, ZHOU W. Bi-level programming model for joint scheduling of arrival and departure flights Based on traffic scenario[J]. Transactions of Nanjing University of Aeronautics and Astronautics, 2021, 38(4): 671-684.
[5]	中国民用航空局. 智慧民航建设路线图[R]. 北京: 中国民用航空局, 2022. Civil Aviation Administration of China. Roadmap for smart civil aviation construction[R]. Beijing: Civil Aviation Administration of China, 2022. (in Chinese)
[6]	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. Multi runway approach flight sorting based on point fusion[J]. Journal of Transport Information and Safety, 2021, 39(6): 108-116. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.06.013
[7]	张军峰, 游录宝, 杨春苇, 等. 基于多目标帝国竞争算法的进场排序与调度[J]. 航空学报, 2021, 42 (6): 475-487. ZHANG J F, YOU L B, YANG C W, et al. Arrival sequencing and scheduling based on multi-objective imperialist competitive algorithm[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42 (2): 475-487. (in Chinese)
[8]	张凤, 汤晓鹏, 刘兵飞. 机场飞行区无人驾驶清水车优化调度方法[J]. 交通信息与安全, 2022, 40 (2): 82-90. doi: 10.3963/j.jssn.1674-4861.2022.02.010 ZHANG F, TANG X P, LIU B F. An optimization method for scheduling autonomous potable water service vehicles at airfields[J]. Journal of Transport Information and Safety, 2022, 40(2): 82-90. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2022.02.010
[9]	GING A, ENGELLAND S, CAPPS A, et al. Airspace technology demonstration 2 (atd-2) technology description document (tdd)[R]. Washington, D.C. National Aeronautics and Space Administration, 2018.
[10]	PARKE B K, STEVENS L K S, COUPE W J, et al. Alternatives for scheduling departures for efficient surface metering in ATD-2: exploration in a human-in-the-loop simulation[C]. International Conference on Applied Human Factors and Ergonomics, Cham: Springer, 2019.
[11]	尹嘉男, 马园园, 胡明华. 机场飞行区资源调度问题研究(三): 研究热点与挑战[J]. 航空工程进展, 2019, 10 (5): 577-586. YIN J N, MA Y Y, HU M H. The airfield resource scheduling problem - part Ⅲ: research hot-spots and challenges[J]. Aviation Engineering Progress, 2019, 10(5): 577-586. (in Chinese)
[12]	侯昌波, 夏朝禹, 田文, 等. 机场群一体化运行: 集成进离场与场面运行技术研究综述[J]. 科学技术与工程, 2023, 23 (12): 4943-4957. HOU C B, XIA C Y, TIAN W, et al. Integrated operation of metroplex: a review of integrated arrival and departure and surface operations technology[J]. Science and Technology and Engineering, 2023, 23(12): 4943-4957. (in Chinese)
[13]	陈飞飞, 王敏. 进离场管理系统一体化运行技术研究[J]. 信息化研究, 2017, 43 (5): 46-49. CHEN F F, WANG M. Research on integrated operation technology of entry and exit management system[J]. Journal of Information Technology Research, 2017, 43(5): 46-49. (in Chinese)
[14]	wieland f, tyagi a, kumar v, et al. metrosim: a metroplex wide route planning and airport scheduling yool[C]. AIAA Aviation Technology, Integration, & Operations Conference, Shanghai: AIAA, 2014.
[15]	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 ZY, HU Z T, et al. Coordinated scheduling, model loss, arriving aircraft at large airport[J]. Journal of Traffic and Transportation Engineering, 2022, 22(1): 205-215. (in Chinese)
[16]	EUN Y, JEON D, LEE H, et al. Optimization of airport surface traffic: a case-study of Incheon international airport[C]. 17th AIAA Aviation Technology, Integration, and Operations Conference, Reston: AIAA, 2017.
[17]	MA J, DELAHAYE D, SBIHI M, et al. Integrated optimization of arrival, departure, and surface operations[C]. 8th International Conference for Research in Air Transportation, Reston: FederalAviationAdministration, 2018.
[18]	BOSSON C, XUE M, ZELINSKI S. Optimizing integrated arrival, departure and surface operations under uncertainty[C]. Air Traffic Management Research and Development Seminar, Lisbon : AIAA, 2015.
[19]	XIA C Y, WU Z Y, YANG C R, et al. Combining arrival-departure scheduling with runway configuration mode switches[C]. 2023 IEEE 5^th International Conference on Civil Aviation Safety and Information Technology, Dali: IEEE, 2024.
[20]	江灏, 刘继新. 进离场航班优化调度研究综述[J]. 航空计算技术, 2021, 51 (4): 130-134. JIANG H, LIU J X. Survey review of arrival and departure flight optimal scheduling[J]. Aeronautical Computing Technique, 2021, 51(4): 130-134. (in Chinese)