大型机场滑行道航空器交通流特性仿真

薛清文; 陆键; 姜雨

doi:10.13700/j.bh.1001-5965.2018.0399

大型机场滑行道航空器交通流特性仿真

doi: 10.13700/j.bh.1001-5965.2018.0399

薛清文¹,
陆键^1, ,,
姜雨²

1.
同济大学道路与交通工程教育部重点实验室, 上海 201804
2.
南京航空航天大学民航学院, 南京 210016

基金项目:

国家自然科学基金 U1333117

详细信息

作者简介:
薛清文, 女, 博士研究生。主要研究方向:机场场面优化、智能交通

陆键, 男, 博士, 教授, 博士生导师。主要研究方向:智能交通

姜雨, 女, 博士, 副教授, 硕士生导师。主要研究方向:机场规划设计与管理

通讯作者:
陆键, E-mail:jianjohnlu@tongji.edu.cn

中图分类号: V351
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- 被引次数: 0
出版历程
- 收稿日期: 2018-07-02
- 录用日期: 2018-09-07
- 网络出版日期: 2019-03-20

Aircraft taxiway traffic flow characteristic simulation at large airport

XUE Qingwen¹,
LU Jian^{1
, ,},
JIANG Yu²

1.
The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China
2.
School of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Funds:

National Natural Science Foundation of China U1333117

More Information

Corresponding author: LU Jian, E-mail:jianjohnlu@tongji.edu.cn

摘要

摘要:
滑行道是连接跑道和停机坪的纽带，是大型机场航空器场面运行的关键资源。随着场面航空器数量的不断增加，航空器在滑行道区域涌现出特有的交通流特性。基于滑行道航空器运行规则，结合元胞传输模型（CTM），建立宏观的滑行道航空器交通流元胞传输模型，在NetLogo系统动力学仿真平台的基础上，以中国某大型机场滑行道航空器运行为例对模型进行验证，推理滑行道交通流基本参数之间的关系和相变特征。研究表明，滑行道交通流在自由流、同步流和阻塞流3种相态中演变，其中在同步流相态中，随着密度增加，流量从0.15降至0.10架次/min，速度从20降至7.64 m/s，说明流量和速度参数对于密度的变化十分敏感。当离场率与进场率的比例从0.2降至0.15时，3种相态下的临界流量从0.15、0.10降至0.13、0.05架次/min；临界速度在自由流不变，同步流和阻塞流中依次从7.64、1.07降至0.88、0.25 m/s；临界密度在自由流由0.50降至0.46架次/km，而在同步流阻塞流中依次从0.88、6.21增加至3.77、13.17架次/km。本研究揭示滑行道交通流拥堵演变机理，为制定科学合理的滑行道管理控制策略提供理论基础。
- 滑行道 /
- 交通流 /
- 元胞传输模型(CTM) /
- 相态演变 /
- NetLogo
Abstract:
Taxiway is the main resource for airport operation, connecting the runway and apron. The specific traffic flow characteristic emerges with the increase of aircraft at the taxiway. The taxiway traffic flow cell transmission model is established by combining cell transmission model (CTM) and operation regulation of taxiway. Based on the system dynamic simulation platform NetLogo, the model is validated by taking the aircraft taxiway operation at a large airport in China as an example. Then the relationships and phase transition among traffic flow parameters are deduced. The study shows that the taxiway traffic flow transfers among three phases: free, synchronized and block flow. Synchronized flow is very sensitive to the density, where the flux decreases from 0.15 to 0.10 flight/min, velocity decreases from 20 to 7.64 m/s with the increase of density. As the ratio of departure rate and arrival rate decreases from 0.2 to 0.15, the critical value of flux drops from 0.15, 0.10 to 0.13, 0.05 flight/min. The critical value of velocity remains the same in free phase, and drops from 7.64, 1.07 to 0.88, 0.25 m/s for synchronized and block flow. The critical value of density drops from 0.50 to 0.46 flight/km in free phase, but increases from 0.88, 6.21 to 3.77, 13.17 flight/km for synchronized and block flow. The study in this paper reveals the evolution mechanism of taxiway traffic flow congestion, which provides the theory basis for the scientific taxiway management and control strategy.
- taxiway /
- traffic flow /
- cell transmission model (CTM) /
- phase transition /
- NetLogo

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图 1 中国某大型机场场面离散化元胞传输网络

Figure 1. Discretized cell transmission network on ground of a large airport in China

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图 2 链路滑行道元胞传输模型

Figure 2. Taxiway cell link transmission model

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图 3 分散滑行道元胞传输模型

Figure 3. Taxiway cell divergence transmission model

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图 4 汇聚滑行道元胞传输模型

Figure 4. Taxiway cell convergence transmission model

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图 5 基于NetLogo系统动力学平台的交通流元胞传输模型宏观仿真

Figure 5. Traffic flow cell transmission model macroscopic simulation based on system dynamic platform NetLogo

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图 6 滑行道交通流的密度、流量和速度的关系

Figure 6. Relationship among density, flux and velocity of taxiway traffic flow

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图 7 不同比例下的滑行道交通流密度、流量和速度的关系

Figure 7. Relationship among densty, flux and velocity of taxiway traffic flow under different ratios

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表 1 实际推出流量和仿真推出流量的比较

Table 1. Comparison of actual and simulated departure flux

时间/ min	实际推出流量/ (架次·min^-1)	仿真推出流量/ (架次·min^-1)
10	0	0.008
20	0.097	0.096
30	0.139	0.136
40	0.167	0.146
50	0.167	0.149
60	0.167	0.149

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表 2 密度-流量函数关系显著性检验结果(α=95%)

Table 2. Significance test results of density-flux functional relationship (α=95%)

相态	样本数	R²相关系数	F检验
自由流	64	1	0.924 6
同步流	10	0	0.075 4

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表 3 密度-速度函数关系显著性检验结果(α=95%)

Table 3. Significance test results of density-velocity functional relationship (α=95%)

相态	样本数	R²相关系数	F检验
同步流	10	0.920 8	0.969 5
阻塞流	32	0.009 7	0

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表 4 流量-速度函数关系显著性检验结果(α=95%)

Table 4. Significance test results of flux-velocity functional relationship(α=95%)

相态	样本数	R²相关系数	F检验
同步流	10	0.986 1	0.002 0

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表 5 滑行道交通流的关键特征参数

Table 5. Key characteristic parameters of taxiway traffic flow

参数		数值
流量/(架次·min^-1)	q_max	0.15
	q_syn	0.10
	q_jam	0.10
速度/(m·s^-1)	v_free	20.00
	v_syn	7.64
	v_jam	1.07
密度/(架次·km^-1)	k_free	0.50
	k_syn	0.88
	k_jam	6.21

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表 6 不同比例下的滑行道交通流关键特征参数

Table 6. Key characteristic parameters of taxiway traffic flow under different ratios

q_out/q_in	流量/(架次·min^-1)			速度/(m·s^-1)			密度/(架次·km^-1)
q_out/q_in	q_max	q_syn	q_jam	v_free	v_syn	v_jam	k_free	k_syn	k_jam
1.00	0.15			20.0			0.52
0.20	0.15	0.10	0.10	20.0	7.64	1.07	0.50	0.88	6.21
0.15	0.13	0.05	0.05	20.0	0.88	0.25	0.46	3.77	13.17

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参考文献(16)

[1]	MENON P K, SWERIDUK G D, LAM T, et al.Computer-aided eulerian air traffic flow modeling and predictive control[J].Journal of Guidance, Control, and Dynamics, 2006, 29(1):12-19. doi: 10.2514/1.13496
[2]	MENON P K, SWERIDUK G D, BILIMORIA K.New approach for modeling, analysis and control of air traffic flow[J].Journal of Guidance, Control, and Dynamics, 2004, 27(5):737-744. doi: 10.2514/1.2556
[3]	ROBELIN C, SUN D, WU G, et al.En-route air traffic modeling and strategic flow management using mixed integer linear programming[C]//Proceedings of the 2006 American Control Conference.Piscataway, NJ: IEEE Press, 2006: 5257-5262.
[4]	SARAF A P, SLATERG L.Adaptive eulerian modeling for air traffic flow management[J].Journal of Aircraft, 2015, 45(5):1585-1600. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=64109cb3251a4df63013547f93f64d8c
[5]	许炎, 张洪海, 杨磊, 等.基于实测数据的终端区空中交通流特性分析[J].交通运输系统工程与信息, 2015, 15(1):205-211. doi: 10.3969/j.issn.1009-6744.2015.01.034 XU Y, ZHANG H H, YANG L, et al.Analysis of air traffic flow characteristics in airport terminal area based on observed data[J].Journal of Transportation Systems Engineering and Information Technology, 2015, 15(1):205-211(in Chinese). doi: 10.3969/j.issn.1009-6744.2015.01.034
[6]	张洪海, 杨磊, 别翌荟, 等.终端区进场交通流广义跟驰行为与复杂相变分析[J].航空学报, 2015, 36(3):949-961. http://d.old.wanfangdata.com.cn/Periodical/hkxb201503028 ZHANG H H, YANG L, BIE Y H, et al.Analysis on generalized following behavior and complex phase-transition law of approaching traffic flow in terminal airspace[J].Acta Aeronautica et Astronautica Sinica, 2015, 36(3):949-961(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/hkxb201503028
[7]	尹苏皖.大型机场离场运行分析与机位协同管控研究[D].南京: 南京航空航天大学, 2015. YIN S W.Research on departure operation analysis and gate collaborative control at mega-airport[D].Nanjing: Nanjing University of Aeronautics and Astronautics, 2015.
[8]	杨磊, 胡明华, 尹苏皖, 等.大型繁忙机场场面离场交通流拥堵特征分析[J].航空学报, 2016, 37(6):1921-1930. http://d.old.wanfangdata.com.cn/Periodical/hkxb201606022 YANG L, HU M H, YIN S W, et al.Characteristics analysis of departure traffic flow congestion in mega-airport surface[J].Acta Aeronautica et Astronautica Sinica, 2016, 37(6):1921-1930(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/hkxb201606022
[9]	SUN D F, BAYEN A M.A new Eulerian-Lagrangian large-capacity cell transmission model for en route traffic[J].Journal of Guidance, Control, and Dynamics, 2008, 31(3):616-628. doi: 10.2514/1.31717
[10]	侯文涛.基于SIMMOD仿真的机场拥挤时段推出率控制策略研究[J].中国民航飞行学院学报, 2015, 26(1):38-42. doi: 10.3969/j.issn.1009-4288.2015.01.008 HOU W T.Research on strategy of pushback rate control for airport congestion time based on SIMMOD simulation[J].Journal of Civil Aviation Flight University of China, 2015, 26(1):38-42(in Chinese). doi: 10.3969/j.issn.1009-4288.2015.01.008
[11]	ZHANG X H.Analysis of KATL operation simulated by TAAM[J].Applied Mechanics & Materials, 2015, 713-715:1601-1604. http://cn.bing.com/academic/profile?id=09b8d10a97cef905c1fece75a1235b1a&encoded=0&v=paper_preview&mkt=zh-cn
[12]	BRAILSFORD S, CHURILOV L, DANGERFIELD B.Discrete-event simulation and system dynamics for management decision making[M].Hoboken:Wiley, 2014:248-279.
[13]	WAI P A, OSTROUKH A V.Development of simulation model mixed system in the AnyLogic software[J].International Journal of Advanced Studies, 2015, 4(4):48-53. doi: 10.12731/2227-930X-2014-4-7
[14]	LI J, YANG Y, ZHAO Q.Study on passenger flow simulation in urban subway station based on AnyLogic[J].Journal of Software, 2014, 9(1):140-146. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=Doaj000003630075
[15]	CHIACCHIO F, PENNISI M, PENNISI M, et al.Agent-based modeling of the immune system:NetLogo, a promising framework[J].Biomed Research International, 2014, 2014:907171. http://cn.bing.com/academic/profile?id=635d0d6e09d8921398fcc60868263136&encoded=0&v=paper_preview&mkt=zh-cn
[16]	KAHN K.An Introduction to agent-based modeling:Modeling natural, social, and engineered complex systems with NetLogo[J].Physics Today, 2015, 68(8):55.