A Dynamic Pushback Control Model for Departure Flights Considering Reapplication Intervals
-
摘要: 针对大型枢纽机场航班离港过程的长时间滑行道排队等待导致大量燃油消耗和废气排放的问题，研究了1种在传统N-Control策略基础上改进的、带有复申请间隔的线性推出控制模型（N-control-linear policy，NCLP）。通过设定最优滑行道排队长度阈值及控制点参数，当实时排队长度超出该阈值时，模型能够逐根据滑行道容量实时状态动态调整推出许可率，实现高效动态离港控制。采用与跑道服务时间相等的航班推出复申请间隔，建立了滑行道排队系统与登机口虚拟排队系统模型，构建了滑行道燃油消耗与登机口占用惩罚的综合成本目标函数。提出了1种基于连续时间马尔可夫链的优化算法，实现滑行道容量与动态推出控制策略的双层循环，进而确定燃油消耗与登机口惩罚成本之间的最优滑行道排队阈值。以北京首都国际机场的实际运行数据为对象开展仿真实验，结果表明：当滑行道排队长度达到最优阈值时，NCLP控制策略相较于无控制情况和传统N-Control策略具有显著优势；与N-Control策略相比，该模型最高可将全天平均滑行等待时间由9.51 min减少至6.94 min，燃油消耗和总运行成本能够分别降低27.07%和23.91%，验证了提出模型减少机场滑行燃油消耗的有效性。Abstract: Long taxiway queues during the departure process of flights at large hub airports will cause large fuel consumption and exhaust emissions. Aiming to tackle this problem, an improved N-control linear policy (NCLP) with multiple application intervals is proposed based on the traditional N-Control strategy. The model can gradually reduce the flight exit permission rate when the real-time queue length exceeds the set/predefined/configured optimal taxiway queue length threshold, achieving more flexible dynamic departure control. The taxiway queuing system and boarding gate virtual queuing system are built by setting the intervals for flight resubmission equal to the runway service time. A comprehensive cost objective function for taxiway fuel consumption and boarding gate occupancy penalty is constructed. A continuous time Markov chain based optimization algorithm is proposed to achieve a dual loop between taxiway capacity and dynamic pushback control strategy, thus determining the optimal taxiway queue threshold between fuel consumption and gate penalty cost. A simulation experiment is conducted on the actual operating data of Beijing Capital International Airport. The results show that when the length of the taxiway queue reaches the optimal threshold, the NCLP control strategy has significant advantages over the uncontrolled situation and the traditional N-Control strategy. This model can reduce the average taxi waiting time from 9.51 min to 6.94 min throughout the day, and reduce fuel consumption and total operating costs by 27.07% and 23.91% respectively compared with the N-Control strategy, which verifies the effectiveness of the proposed model in reducing airport taxi fuel consumption.
-
Key words:
- traffic control /
- airport operations /
- dynamic pushback /
- pushback rate /
- Markov chain /
- fuel cost
-
图 1 NCLP策略与N-control控制策略曲线
Figure 1. NCLP strategy and N-control control strategy curve
图 7 NCLP策略下登机口等待时间及滑行等待时间
Figure 7. Gate-hold time and taxiway waiting time under NCLP
图 8 N-control策略和NCLP策略的登机口等待总数量
Figure 8. Total number of waiting positions for N-control and NCLP
图 9 N-control策略和NCLP策略的登机口等待分时数量
Figure 9. Gate-hold times of each hour for N-control and NCLP
图 10 NCLP控制策略下燃油成本与总成本减少率对比
Figure 10. Comparison of fuel cost and total cost reduction rate under NCLP
表 1 符号及定义
Table 1. Symbols and definitions
符号 定义 T 时间窗 λ 推出申请率，即允许当前航班推出的概率 j 航班索引，j = 1, 2, ···n U 航班申请推出序列 α 控制点参数 N 滑行道排队长度阈值 Nmax 滑行道排队长度容量，应满足N ≤ Nmax c 每分钟每航班的燃油消耗成本 G 登机口停留时间 Gmax 最大登机口停留时间/min Wj 航班j的滑行道等待时间 a、b 惩罚函数曲线待确定参数 M 航班数量 n 当前滑行道排队长度 μ 跑道服务率 CT 离港过程的总成本 Ct 航班j的滑行道燃油消耗成本 Pj 航班j因超过等待登机口时间nG而产生的惩罚成本 nG 登机口等待队列中的第n架航班 Ca 申请推出次数 Cb 申请重新推出次数 tt 跑道服务时间，tt = 1/μ tr 复申请间隔 
下载: 导出CSV
表 2 无控制模型、N-control模型和NCLP模型的结果
Table 2. Results of uncontrolled model, N-control model, and NCLP model
策略 α N CT/元 G/min W/min 燃油减少率/% 成本减少率/% 无控制 830554.30 13.20 N-control 1 7 602641.56 3.7 9.51 0.9 8 602641.56 3.7 9.51 0 0 0.8 8 556086.48 4.53 8.68 8.73 7.73 0.7 9 556086.48 4.53 8.68 8.73 7.73 0.6 9 511200.43 5.25 7.96 16.30 15.17 NCLP控制策略 0.5 10 511200.43 5.25 7.96 16.30 15.17 0.4 12 511200.43 5.25 7.96 16.30 15.17 0.3 13 485681.32 5.9 7.31 23.13 19.41 0.2 14 470689.45 6.05 7.16 24.71 21.90 0.1 15 460050.11 6.21 7 26.39 23.66 0 15 458566.85 6.27 6.94 27.02 23.91
下载: 导出CSV
表 3 不同日期无控制模型、N-control模型和NCLP模型的结果
Table 3. Results of uncontrolled model, N-control model, and NCLP model on other days
日期 策略 α N CT/元 G/min W/min 燃油减少率/% 成本减少率/% 2013年11月18日 无控制 818599.35 13.01 N-control 1 7 586421.67 3.69 9.32 NCLP控制策略 0.5 10 500152.88 5.22 7.79 16.09 14.89 0 15 445973.68 6.24 6.77 26.81 24.24 2013年11月19日 无控制 811678.07 12.9 N-control 1 7 579500.39 3.69 9.21 NCLP控制策略 0.5 10 491681.10 5.34 7.56 17.35 15.15 0 15 438823.19 6.25 6.65 26.92 24.28
下载: 导出CSV
-
[1] EVERTSE C, VISSER H G. Real-time airport surface movement planning: minimizing aircraft emissions[J]. Transportation Research Part C: Emerging Technologies, 2017, 79: 224-241. doi: 10.1016/j.trc.2017.03.018 [2] DÖNMEZ K. Airport Ground Optimizer(AGO): a decision support system initiative for air traffic controllers with optimization and decision-aid algorithms[J]. Journal of Air Transport Management, 2024, 119: 102648. doi: 10.1016/j.jairtraman.2024.102648 [3] SIMAIAKIS I, SANDBERG M, BALAKRISHNAN H. Dynamic control of airport departures: Algorithm development and field evaluation[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(1): 285-295. doi: 10.1109/TITS.2013.2278484 [4] HAO L, RYERSON M S, KANG L, et al. Estimating fuel burn impacts of taxi-out delay with implications for gate-hold benefits[J]. Transportation Research Part C: Emerging Technologies, 2017, 80: 454-466. doi: 10.1016/j.trc.2016.05.015 [5] PUJET N, ODONI A R, DELAWARE F. Input-output modeling and control of the departure process of congested airports[J]. Air Traffic Control Quarterly, 2000, 8(1): 1-32. doi: 10.2514/atcq.8.1.1 [6] ZHANG J, WANG L. Application of dynamic pushback control strategy in an international airport[J]. Case Studies on Transport Policy, 2022, 10(2), 789-797. [7] BALAKRISHNA P, GANESAN R, SHERRY L. Accuracy of reinforcement learning algorithms for predicting aircraft taxi-out times: a case-study of Tampa Bay departures[J]. Transportation Research Part C: Emerging Technologies, 2010, 18(6): 950-962. doi: 10.1016/j.trc.2010.03.003 [8] LIAN G, ZHANG Y, DESAI J, et al. Predicting taxi-out time at congested airports with optimization-based support vector regression Methods[J]. Mathematical Problems in Engineering, 2018(1): 7509508. [9] SIMAIAKIS I, BALAKRISHNAN H. A queuing model of the airport departure process[J]. Transportation Science, 2016, 50 (1): 94-109. doi: 10.1287/trsc.2015.0603 [10] BAO J, KANG J, ZHANG J, et al. A dynamic control method for airport ground movement optimization considering adaptive traffic situation and data-driven conflict priority[J]. Transportation Research Part C: Emerging Technologies, 2025, 124: 102753. [11] FERNANDES H F, MÜLLER C. Optimization of the waiting time and makespan in aircraft departures: a real-time non-iterative sequencing model[J]. Journal of Air Transport Management, 2019, 79: 101686. doi: 10.1016/j.jairtraman.2019.101686 [12] 刘丽华, 张亚平, 邢志伟, 等. 基于离散差分进化的飞机推出决策优化[J]. 交通运输系统工程与信息, 2016, 16(6): 196-203.LIU L H, ZHANG Y P, XING Z W, et al. Optimization of aircraft launch decision based on discrete difference evolution[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(6): 196-203. (in Chinese) [13] LI H, CHEN Z. Integration of pushback control system with other airport systems[J]. Journal of Air Transport Management, 2023, 108: 102345. [14] BURGAIN P, PINON O J, FERON E, et al. Optimizing pushback decisions to valuate airport surface surveillance information[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(1): 180-192. doi: 10.1109/TITS.2011.2166388 [15] SANDBERG M J, SIMAIAKIS I, BALAKRISHNAN H, et al. A decision support tool for the pushback rate control of airport departures[J]. IEEE Transactions on Human-Machine Systems, 2014, 44(3): 416-421. doi: 10.1109/THMS.2014.2305906 [16] 张亚平, 刘丽华, 郝斯琪, 等. 基于弹性滑行容量的飞机有效推出时隙研究[J]. 武汉理工大学学报, 2016, 40(6): 937-942.ZHANG Y P, LIU L H, HAO S Q, XING Z W. Research on effective aircraft rollout time slot based on elastic taxiing capacity[J]. Journal of Wuhan University of Technology, 2016, 40(6): 937-942. (in Chinese) [17] 赵向领, 唐建勋, 卢飞, 等. 基于改进离散差分算法的航班延迟推出策略分析[J]. 交通运输系统工程与信息, 2015, 15 (6): 114-120.ZHAO X L, TANG J X, LU F, HAN B, et al. Analysis of flight delay introduction strategy based on improved discrete difference algorithm[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(6): 114-120. (in Chinese) [18] DESAI J, LIAN G, SRIVATHSAN S. Dynamic departure pushback control at airports: part a-linear penalty-based algorithms and policies[J]. Naval Research Logistics, 2024, 71 (7): 22189. [19] 廉冠, 张亚平, 邢志伟, 等. 飞机动态推出控制策略仿真优化模型[J]. 南京航空航天大学学报, 2018, 50(6): 776-782.LIAN G, ZHANG Y P, XING Z W, et al. Simulation optimization model for aircraft dynamic rollout control strategy[J]. Journal of Nanjing University of Aeronautics and Astronautics, 2018, 50(6): 776-782. (in Chinese) [20] LIAN G, WU Y, LUO W, et al. A two-stage optimization method for multi-runway departure sequencing based on continuous-time Markov chain[J]. Aerospace, 2025, 12(4): 12040273. [21] 朱承元, 白锴迪, 赵志刚. 考虑空域时空特性的动态多跑道使用策略优化方法[J]. 交通信息与安全, 2024, 42(01): 94-104. doi: 10.3963/j.jssn.1674-4861.2024.01.011ZHU C Y, BAI K D, ZHAO Z G. Optimization of dynamic multi-runway use strategy considering spatio-temporal characteristics of airspace[J]. Journal of Transport Information and Safety, 2024, 42(1): 94-104. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2024.01.011 [22] KANG J, BAO J, ZHANG Z, et al. Dynamic routing and scheduling approach for aircraft taxi automation with adaptive surface situation[J]. Journal of Aerospace Information Systems, 2025, 22(3): 189-201. doi: 10.2514/1.I011486 [23] WANG X, BROWNLEE A E, WEISZER M, et al A chance-constrained programming model for airport ground movement optimisation with taxi time uncertainties[J]. Transportation Research Part C: Emerging Technologies, 2021, 132: 103382. doi: 10.1016/j.trc.2021.103382 [24] ALI H, PHAM D T, ALAM S. Toward greener and sustainable airside operations: a deep reinforcement learning approach to pushback rate control for mixed-mode runways[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(11): 18354-18367. doi: 10.1109/TITS.2024.3433428 -
点击查看大图
图(10) / 表(3)
计量
- 文章访问数: 2
- HTML全文浏览量: 2
- PDF下载量: 0
- 被引次数: 0