Airline flight frequency optimization based on multiple travel paths
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摘要: 将航空运输网络抽象为多层级网络结构, 构建了确定航空公司某一城市对某条路径航班频率的两阶段规划模型: 第一阶段从旅客选择行为的角度, 考虑旅客对旅行时间、过站时间、计划延误时间、票价等因素的价值感知, 构建旅客旅行负效用函数, 进而基于多项式Logit模型构建计算旅客选择某个航空公司某个城市对某条路径概率的旅客路径选择模型; 第二阶段从航空公司的角度, 以总收益最大化为目标函数, 基于行程多路径, 并考虑航空公司总运力限制, 尽可能地让每条路径的运力供给等于需求, 构建了确定路径航班频率的线性规划模型; 提出了求解两阶段模型的迭代算法。研究结果表明: 提出的算法能够在8次迭代之后达到收敛, 可以在较短的时间内得到最优解; 随着算法的收敛, 构建的两阶段规划模型在航线存在市场竞争且整体运力不足的情况下优先将运力安排到收益最高的航线上, 提升航空公司整体收益; 对于包含多个航节的航线, 构建的两阶段模型更能体现旅客选择行为在航班频率配置中发挥的作用; 对于包含一个航节的航线, 需求随航班频率的变动幅度较小, 随着迭代次数的增加, 需求航班频率弹性系数逐渐变小, 对于包含多个航节的航线, 在航线总需求一定的情况下, 需求随航班频率的变动幅度较大, 由于市场竞争存在航班频率不变需求骤减的情形。可见, 所提出的模型和算法能够有效提升航空公司收益。Abstract: A multiple-layer network was abstracted from the airline's air transport network, and a two-stage planning model was built to determine the flight frequency along a certain route by an airline for a specific city pair. In the first stage, a negative utility function of travel was constructed on the basis of the passengers' selection behavior by considering their perception of travel time, transfer time, delay time, and ticket price. Subsequently, a polynomial Logit model was adopted to create a route selection model in order to calculate the probability of passengers selecting a certain route by an airline for a specific city pair. In the second stage, a linear planning model was established to determine the flight frequency from the airline's perspective. The overall objective was to maximize the total revenue, the multiple travel paths, the total carrier capacity of the airline, and the balance between the carrier supply and demand for each path were considered. An iterative algorithm was presented to solve the proposed two-stage model. Analysis result shows that the convergence can be achieved after 8 iterations, and thus, the optimal solution can be reached within a short time. As the solutions converge, the proposed two-stage planning model prioritizes the routes with the highest revenue to improve the overall revenue in cases where there is market competition and insufficient overall capacity. For the routes with multiple segments, the two-stage model can more clearly present the role of the passengers' selection behavior related to the flight frequency determination. For the routes with only one segment, there is less variation in demand with respect to the change in the flight frequency. As the number of iterations increases, the demand tends to become decreasingly sensitive to the flight frequency. For the routes with multiple segments, the variation in the demand with change in the flight frequency is considerably higher in the cases when the total demand is fixed. Conversely, the demand decreases sharply when the flight frequency remains unchanged due to the market competition. Therefore, the presented model and algorithm can effectively improve the airline revenue.
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表 1 网络中航节上的航班数据
Table 1. Flight data at segments in network
航空公司 航节 单向每周航班班次 距离/km 飞行时间/h 座位数 南方航空 深圳—武汉 28 906 1.933 179 深圳—郑州 35 1 320 2.583 160 郑州—呼和浩特 19 1 127 2.333 170 深圳—南京 7 726 1.417 160 东方航空 深圳—武汉 21 906 1.967 169 深圳—南京 21 1 127 2.250 174 南京—呼和浩特 7 1 191 2.333 174 深圳航空 深圳—武汉 7 906 1.917 157 武汉—呼和浩特 7 1 138 2.167 157 深圳—郑州 21 1 320 2.667 152 深圳—南京 42 1 127 2.333 160 海南航空 武汉—呼和浩特 7 1 138 2.167 163 深圳—郑州 14 1 320 2.583 163 郑州—呼和浩特 7 726 1.500 185 深圳—南京 14 1 127 2.500 163 东海航空 深圳—南京 7 1 127 2.333 159 深圳—郑州 7 1 320 2.583 159 西部航空 深圳—郑州 7 1 320 2.500 153 深圳—南京 7 1 127 2.250 179 天津航空 郑州—呼和浩特 12 726 1.500 105 北部湾航空 郑州—呼和浩特 3 726 1.500 106 山东航空 郑州—呼和浩特 7 726 1.417 167 首都航空 郑州—呼和浩特 6 726 1.417 219 厦门航空 南京—呼和浩特 2 1 191 2.500 169 中国航空 深圳—武汉 7 906 1.833 158 
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表 2 城市对上的需求
Table 2. Demands of city pairs
城市对 深圳—武汉 武汉—呼和浩特 深圳—郑州 郑州—呼和浩特 深圳—南京 南京—呼和浩特 深圳—呼和浩特 客座率/% 91 89 90 95 71 74 74 实际需求/人次 9 511 1 993 14 937 6 182 12 962 1 245 2 543 潜在需求/人次 11 889 2 491 18 672 7 728 16 203 1 556 3 179
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表 3 目标航空公司每周航班班次迭代结果
Table 3. Iterative result of weekly flight frequencies for target airlines
迭代次数 机型 深圳—武汉 武汉—呼和浩特 深圳—郑州 深圳—南京 0 B738 7 7 21 42 A320 0 0 0 0 1 B738 0 1 29 23 A320 9 9 2 15 2 B738 12 13 13 20 A320 1 0 20 17 3 B738 9 1 31 14 A320 7 13 4 22 4 B738 5 14 18 20 A320 14 0 16 15 5 B738 21 11 18 9 A320 2 0 16 25 6 B738 20 7 5 29 A320 6 2 29 5 7 B738 20 8 20 11 A320 8 0 14 22 8 B738 9 7 34 5 A320 20 0 1 27
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表 4 需求航班频率弹性系数
Table 4. Demands elasticity coefficients of flight frequency
迭代次数 深圳—武汉 武汉—呼和浩特 深圳—郑州 深圳—南京 深圳—武汉—呼和浩特 0 ∞ 0.839 0.221 0.248 0.004 1 0.892 0.895 0.598 0.849 ∞ 2 0.392 0.162 0.006 0.749 0.145 3 0.951 - 0.234 0.671 ∞ 4 0.655 0.094 ∞ 0.651 ∞ 5 0.640 0.114 ∞ ∞ ∞ 6 0.631 0.131 ∞ 0.850 ∞ 7 0.654 0.146 ∞ 0.462 ∞
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表 5 模型结果对比
Table 5. Comparison of model results
模型 航空公司 路径需求人数 过站时间/h 计划延误时间/h 每周航班班次 票价/美元 飞行时间/h 改进前 南方航空 572 1 4.5 7 149.3 4.90 东方航空 552 1 4.5 7 146.3 4.58 深圳航空 725 1 4.5 7 142.9 4.08 改进后 南方航空 895 0 5.2 7 149.3 4.90 东方航空 684 0 6.0 7 146.3 4.58 深圳航空 181 0 9.0 7 142.9 4.08
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[1] 吴刚, 夏洪山, 高强. 机场群运行方式下的航班时刻与频率优化模型[J]. 交通运输工程学报, 2013, 13(4): 79-86. doi: 10.3969/j.issn.1671-1637.2013.04.012WU Gang, XIA Hong-shan, GAO Qiang. Optimization model of flight time and frequency under operation mode of multi-airports system[J]. Journal of Traffic and Transportation Engineering, 2013, 13(4): 79-86. (in Chinese). doi: 10.3969/j.issn.1671-1637.2013.04.012 [2] ZHU Bo, WU Gang, GAO Qiang. Optimization on flight frequencies and timetable in a multiple-airport system under hub congestion[J]. Journal of Computational Information Systems, 2014, 10(6): 2587-2596. [3] 姜思露, 朱金福, 孔明星, 等. 基于旅客计划延误的航班频率优化研究[J]. 武汉理工大学学报(交通科学与工程版), 2019, 43(1): 136-140. doi: 10.3963/j.issn.2095-3844.2019.01.027JIANG Si-lu, ZHU Jin-fu, KONG Ming-xing, et al. Research of flight frequency optimization based on passenger schedule delay[J]. Journal of Wuhan University of Technology (Transportation Science and Engineering), 2019, 43(1): 136-140. (in Chinese). doi: 10.3963/j.issn.2095-3844.2019.01.027 [4] WEN Y H. Airline cargo alliance and allied flight frequency analysis using the fuzzy cooperative game and flight frequency programming[C]//IEEE. 6th IEEE International Conference on Advanced Logistics and Transport. New York: IEEE, 2018: 59-67. [5] HSU C I, WEN Y H. Airline flight frequency determination in response to competitive interactions using fuzzy logic[J]. Mathematical and Computer Modelling, 2005, 42(11/12): 1207-1224. [6] VAZE V, BAMHART C. Modeling airline frequency competition for airport congestion mitigation[J]. Transportation Science, 2012, 46(4): 512-535. doi: 10.1287/trsc.1120.0412 [7] HANSEN M, LIU Y. Airline competition and market frequency: a comparison of the s-curve and schedule delay models[J]. Transportation Research Part B: Methodological, 2015, 78: 301-317. doi: 10.1016/j.trb.2015.04.012 [8] PAI V. On the factors that affect airline flight frequency and aircraft size[J]. Journal of Air Transport Management, 2010, 16(4): 160-177. [9] DONG Guang-ling, YAO Yu, HE Chi, et al. On dynamic frequency index of flight motion table used in guided weapon's simulation[C]//IEEE. 31st Chinese Control Conference. New York: IEEE, 2012: 7669-7673. [10] YAN S, WANG C R. The planning of aircraft routes and flight frequencies in an airline network operations[J]. Journal of Advanced Transportation, 2001, 35(1): 33-46. doi: 10.1002/atr.5670350104 [11] JUNG S Y, YOO K E. Passenger airline choice behavior for domestic short-haul travel in South Korea[J]. Journal of Air Transport Management, 2014, 38: 43-47. doi: 10.1016/j.jairtraman.2013.12.017 [12] YANG C W, LU J L, HSU C Y. Modeling joint airport and route choice behavior for international and metropolitan airports[J]. Journal of Air Transport Management, 2014, 39: 89-95. doi: 10.1016/j.jairtraman.2014.05.001 [13] SERRA D, COLOME R. Consumer choice and optimal locations models: formulations and heuristics[J]. Papers in Regional Science, 2010, 80: 439-464. [14] AN Bo, CHEN Hai-peng, PARK N, et al. Data-driven frequency-based airline profit maximization[J]. ACM Transactions on Intelligent Systems and Technology, 2017, 8(4): 61-1-28. [15] HSU C I, WEN Y H. Determining flight frequencies on an airline network with demand-supply interactions[J]. Transportation Research Part E: Logistics and Transportation Review, 2003, 39(6): 417-441. doi: 10.1016/S1366-5545(02)00060-1 [16] PELEGRÍN B P, HERNÁNDEZ P F, GARCÍA J D P. On the location of new routes to a destination for airline expansion[J]. European Journal of Industrial Engineering, 2016, 10(5): 664-681. doi: 10.1504/EJIE.2016.078806 [17] LI Zhi-chun, LAM W H K, WONG S C, et al. Optimal route allocation in a liberalizing airline market[J]. Transportation Research Part B: Methodological, 2010, 44(7): 886-902. doi: 10.1016/j.trb.2009.12.013 [18] 杨新湦, 裴一麟. 基于双层规划的航空公司航线航班优化研究[J]. 航空计算技术, 2018, 48(3): 1-7. doi: 10.3969/j.issn.1671-654X.2018.03.001YANG Xin-sheng, PEI Yi-lin. Study on route network and flight schedule based on bi-level planning model[J]. Aeronautical Computing Technique, 2018, 48(3): 1-7. (in Chinese). doi: 10.3969/j.issn.1671-654X.2018.03.001 [19] 朱星辉, 朱金福, 姜涛. 分阶段多航空公司竞争下航班频率研究[J]. 预测, 2007, 26(5): 71-74. doi: 10.3969/j.issn.1003-5192.2007.05.013ZHU Xing-hui, ZHU Jin-fu, JIANG Tao. Flight frequency determination on multi-airline competition by two stage[J]. Forecasting, 2007, 26(5): 71-74. (in Chinese). doi: 10.3969/j.issn.1003-5192.2007.05.013 [20] 姜思露. 航班频率和机型指派综合优化研究[D]. 南京: 南京航空航空大学, 2019.JIANG Si-lu, Research on integrated optimization of flight frequency and fleet assignment[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2019. (in Chinese). [21] 朱勐辉. 基于虹吸效应的航班频率与机票价格优化研究[D]. 南京: 南京航空航空大学, 2018.ZHU Meng-hui. The research of flight frequency and ticket price optimization based on siphon effect[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2018. (in Chinese). [22] DU Wen-bo, ZHOU Xing-lian, LORDAN O, et al. Analysis of the Chinese airline network as multi-layer networks[J]. Transportation Research Part E: Logistics and Transportation Review, 2016, 89: 108-116. doi: 10.1016/j.tre.2016.03.009 [23] HONG Chen, ZHANG Jun, CAO Xian-bin, et al. Structural properties of the Chinese air transportation multilayer network[J]. Chaos Solitons and Fractals, 2016, 86: 28-34. doi: 10.1016/j.chaos.2016.01.027 [24] 余朝军, 江驹, 徐海燕, 等. 基于改进遗传算法的航班-登机口分配多目标优化[J]. 交通运输工程学报, 2020, 20(2): 121-130. doi: 10.19818/j.cnki.1671-1637.2020.02.010YU Chao-jun, JIANG Ju, XU Hai-yan, et al. Multi-objective optimization of flight-gate assignment based on improved genetic algorithm[J]. Journal of Traffic and Transportation Engineering, 2020, 20(2): 121-130. (in Chinese). doi: 10.19818/j.cnki.1671-1637.2020.02.010 [25] PIETRANTONIO H. Urban travel demand modeling: from individual choices to general equilibrium[J]. Computers and Mathematics with Applications, 1995, 35(5): 94-105. [26] LIU Wan-ming, YANG Wen-dong, ZHU Xing-hui. Cooperative game study of airlines based on flight frequency optimization[J]. Journal of Applied Mathematics, 2014, 2014: 1-5. [27] SWAN W M, ADLER N. Aircraft trip cost parameters: a function of stage length and seat capacity[J]. Transportation Research Part E: Logistics and Transportation Review, 2006, 42(2): 105-115. doi: 10.1016/j.tre.2005.09.004 [28] PELS E, NIJKAMP P, RIETVELD P, et al. Airport and airline competition for passengers departing from a large metropolitan area[J]. Journal of Urban Economics, 2000, 48(1): 29-45. doi: 10.1006/juec.1999.2156 [29] MUHAMMET D, NIHAN C D, EMINE A. Airline new route selection based on interval type-2 fuzzy MCDM: a case study of new route between Turkey-North American region destinations[J]. Journal of Air Transport Management, 2017, 59: 83-99. [30] 张远强, 史国友, 李松. 基于在线有向无环图的船舶轨迹压缩算法[J]. 交通运输工程学报, 2020, 20(4): 227-236.ZHANG Yuan-qiang, SHI Guo-you, LI Song. Compression algorithm of Ship trajectory based on online directed acyclic graph[J]. 2020, 20(4): 227-236. (in Chinese). -
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