基于旅客支付意愿随机性的卖高率估算方法

中国民航大学学报 ›› 2025, Vol. 43 ›› Issue (5): 82-89.

基于旅客支付意愿随机性的卖高率估算方法

1. 中国民航大学计算机科学与技术学院，天津 300300； 2. 成都航空有限公司市场营销中心，成都 610200

收稿日期:2023-05-27 修回日期:2023-10-29 出版日期:2025-11-17 发布日期:2025-11-17
作者简介:樊玮（1968— ），男，陕西咸阳人，教授，博士，研究方向为人工智能方法、运筹优化在民航的应用
基金资助:
厦门航空科技创新项目（20200618010301）

Estimation method of sell-up potential based on the stochastic nature of

passengers′ willingness to pay

1. College of Computer Science and Technology, CAUC, Tianjin 300300, China; 2. Marketing Center, Chengdu Airlines CO., Ltd.,

Chengdu 610200, China

Received:2023-05-27 Revised:2023-10-29 Online:2025-11-17 Published:2025-11-17

摘要/Abstract

摘要：

本文针对麻省理工学院的卖高率估算方法忽略旅客支付意愿的随机性，存在对中等级舱位需求低估的问题，

提出了一种考虑旅客支付意愿概率分布律的卖高率估算方法。首先，假设旅客支付意愿服从正态分布，进而

基于其概率特性推导卖高率估算模型，随后从历史数据中拟合旅客支付意愿分布参数，实现卖高率的估算。

为验证该方法的有效性，本文借鉴国外著名仿真系统思想，使用高斯混合分布统计旅客到达数据，设计实现

了航班旅客到达模拟软件，进行仿真评估。结合 3 种代表性航线真实数据的系统仿真表明，本文方法能有效

加强中等级舱位控制，增加航班收益。

关键词:

卖高率, 支付意愿, 舱位需求预测, 收益管理

Abstract:

Regarding the issue that the stochastic nature of passengers′ willingness to pay is neglected by the sell-up potential estimation method of the Massachusetts Institute of Technology, which results in the underestimation of

the demand for intermediate class cabin, a sell-up potential estimation method that takes into account the probability distribution law of passengers′ willingness to pay was proposed. Firstly, assuming that passengers′ willingness to pay follows a normal distribution. Then, based on its probability characteristics, an estimation model

for sell-up potential was derived. Subsequently, the parameters of the distribution of passengers′ willingness to

pay were fitted from historical data to realize the estimation of the sell-up potential. To validate the effectiveness

of the proposed method, this article draws on the famous simulation system ideas from abroad, using Gaussian

mixture distribution to statistically analyze the data of passengers arrival, and a flight passenger arrival simulation software was designed and implemented for simulation evaluation. The system simulation combining real

data from three representative flight routes showed that the method proposed in this paper can effectively

strengthen the control of intermediate-class cabin and increase flight revenue.

Key words:

sell-up potential, willingness to pay, cabin class demand forecasting, revenue management

中图分类号:

樊玮 , 张永翔 .

基于旅客支付意愿随机性的卖高率估算方法

[J]. 中国民航大学学报, 2025, 43(5): 82-89.

FAN Wei , ZHANG Yongxiang.

Estimation method of sell-up potential based on the stochastic nature of

passengers′ willingness to pay

[J]. Journal of Civil Aviation University of China, 2025, 43(5): 82-89.

参考文献 22

[1]	GALLEGO G, TOPALOGLU H. Revenue management and pricing ana－
	lytics[M]. New York: Springer, 2019.
[2]	林贵华, 唐艳, 陈拼博. 随机需求下航空公司收益管理定价模型[J]. 系统工程, 2022, 40(2): 109-117.
[3]	熊浩, 鄢慧丽. 考虑旅客跨区间流转的机票多预定区间折扣优化模型研究[J]. 中国管理科学, 2019, 27(8): 172-180.
[4]	樊玮, 苏秋波. 基于分布估计算法的多航段座位分配模型[J]. 信息与控制, 2012, 41(6): 774-778, 785.
[5]	胡玉生, 李金林, 冉伦, 等. 基于乘客惰性行为的平行航班动态定价[J]. 运筹与管理, 2018, 27(4): 112-117.
[6]	霍佳震, 秦瑛. 旅客需求双向转移的多等级舱位控制优化问题[J].系统工程, 2016, 34(1): 116-121.
[7]	WEATHERFORD L. The history of unconstraining models in revenue
	management[J]. Journal of Revenue and Pricing Management, 2016, 15(3): 222-228.
[8]	FIIG T, WEATHERFORD L R, WITTMAN M D. Can demand forecast
	accuracy be linked to airline revenue?[J]. Journal of Revenue and Pricing Management, 2019, 18(4): 291-305.
	[9]
	BELOBABA P P. Did LCCs save airline revenue management?[J]. Journal of Revenue and Pricing Management, 2011, 10(1): 19-22.
[10]	LE M L, LU M H. Network revenue management with dependent demand under overlapping segments[C]//2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
	(ICNC-FSKD), July 28-30, 2018, Huangshan, China. IEEE, 2018: 677-684.
[11]	TALLURI K, VAN RYZIN G. Revenue management under a general
	discrete choice model of consumer behavior[J]. Management Science,2004, 50(1): 15-33.
[12]	ALGERS S, BESER M. Modelling choice of flight and booking class-a
	study using stated preference and revealed preference data[J]. International Journal of Services Technology and Management, 2001, 2 (1/2):28-45.
[13]	梅虎, 朱金福, 汪侠. 基于旅客舱位选择的航空收益管理[J]. 系统工程, 2006, 24(9): 11-17.
[14]	FUKUSHI M, DELGADO F, RAVEAU S, et al. CHAIRS: a choice based air transport simulator applied to airline competition and revenue management[J]. Transportation Research Part A: Policy and Practice,
	2022, 155: 297-315.
[15]	CHIAMBARETTO P. Air passengers′willingness to pay for ancillary
	services on long-haul flights[J]. Transportation Research Part E: Logistics and Transportation Review, 2021, 147: 102234.
[16]	CARRIER E, WEATHERFORD L. Implementation of MNL choice models in the passenger origin-destination simulator (PODS)[J]. Journal of Revenue and Pricing Management, 2015, 14(6): 400-407.
[17]	WEATHERFORD L R. Intelligent aggressiveness: using forecast
	multipliers, hybrid forecasting, fare adjustment, and unconstraining
	methods to increase revenue[J]. Decision Sciences, 2017, 48(3): 391-419.
[18]	樊玮, 杨同杰, 衡红军. 基于卖高行为的航班舱位优化[J]. 计算机工程与设计, 2015, 36(7): 1948-1952.
[19]	王洪建. 航班座位控制模型实现与分析[J]. 软件工程, 2022, 25(2):26-28.
[20]	JIANG H Y, MIGLIONICO G. Airline network revenue management
	with buy-up[J]. Optimization, 2014, 63(6): 849-865.
[21]	BELOBABA P P. Optimal vs. heuristic methods for nested seat allocation[C]//Presentation at ORSA/TIMS Joint National Meeting, Brussels,Belgium. 1992: 28-53.
[22]	HOPPERSTAD C, ZERBIB G, BELOBABA P P. Methods for estimating
	sell-up[C]//AGIFORS Revenue Management and Distribution Study
	Group Meeting, Cancun, Mexico. 2006.