LE Mei-long, ZHENG Wen-juan, HU Yu-ming. Airline flight frequency optimization based on multiple travel paths[J]. Journal of Traffic and Transportation Engineering, 2020, 20(5): 217-226. doi: 10.19818/j.cnki.1671-1637.2020.05.018
Citation: LE Mei-long, ZHENG Wen-juan, HU Yu-ming. Airline flight frequency optimization based on multiple travel paths[J]. Journal of Traffic and Transportation Engineering, 2020, 20(5): 217-226. doi: 10.19818/j.cnki.1671-1637.2020.05.018

Airline flight frequency optimization based on multiple travel paths

doi: 10.19818/j.cnki.1671-1637.2020.05.018
Funds:

National Natural Science Foundation of China 71874081

Natural Science Foundation of Jiangsu Province BK20151479

More Information
  • Author Bio:

    LE Mei-long(1964-), male, professor, PhD, lemeilong@126.com

    ZHENG Wen-Juan(1993-), female, graduate student, 1316823801@qq.com

  • Received Date: 2020-04-08
  • Publish Date: 2020-10-25
  • 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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  • [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.1671-1637.2013.04.012

    WU 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.2095-3844.2019.01.027

    JIANG 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.2018.03.001

    YANG 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.1003-5192.2007.05.013

    ZHU 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.1671-1637.2020.02.010

    YU 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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