Choice Behavior of Connecting Transportation Modes for
 Arriving Air Passengers Under Synergy Between Product
 Attributes and Group Preferences

doi:10.16097/j.cnki.1009-6744.2025.05.014

Abstract

Abstract: Cross-platform passenger ticket data, after personal information desensitization, cannot directly reveal the decision-making outcomes of connecting transportation modes for arriving airport passengers. To uncover the behavioral mechanisms behind passengers mode choices during airport arrival and departure processes, this paper introduces a synergy mechanism integrating transport product attributes and passenger group preferences. Generalized cost characteristics of inbound flight products are utilized to identify heterogeneous choice preferences across passenger groups. Generalized cost functions for inbound flights and connecting modes are established, incorporating four evaluation metrics: efficiency, economy, reliability, and safety. Sensitivity analysis of influencing factors is performed using Sobol' indices. An improved K-Means clustering algorithm categorizes flight products, while principal component analysis computes feature weights, classifying passengers into three typical groups: economy-oriented, efficiency-oriented, and experience-oriented. Prospect utilities of connecting modes for each passenger group are calculated using generalized cost values. An indifference perception utility threshold is introduced to account for bounded rationality in decision-making, optimized via multinomial Logit model fitting. The final model determines choice probabilities of connecting modes. A case study of passengers arriving at Nanning Wuxi Airport traveling to Nanning Railway Station is conducted using 2024 air and high-speed rail ticket data (January to December). Results show an optimal indifference perception utility threshold of 0.54, with model performance metrics: Mean Absolute Error (MAE) is 12.32, Root Mean Squared Error (RMSE) is 15.35, and Mean Absolute Percentage Error (MAPE) is 8.9%. Daily mode share rates are 20.04% (high-speed rail), 23.15% (bus), 26.25% (taxi), and 30.56% (private vehicles). This study provides references for integrated transportation hubs planning and operation optimizations.

Key words: integrated transportation, connection mode choice, generalized cost, inbound flight passengers, product attributes; group preferences

摘要： 经过个人信息脱敏后的跨平台客票数据，无法直接还原进港航班旅客的接续方式选择决策结果。为揭示旅客在抵离航空枢纽过程中的接续方式选择行为机理，引入运输产品特征与旅客群体偏好的协同机制，利用进港航班产品的广义费用特征，确定对应航班旅客群体的异质性选择偏好。建立进港航班和接续方式的广义费用函数，包含快捷性、经济性、可靠性和安全性这4个评价指标，并通过Sobol'指数进行影响因素的灵敏度分析；利用改进的K-Means聚类算法对航班产品划分类别，通过主成分分析计算特征权重，进而将对应的购票旅客群体命名为经济型、效率型和体验型；代入广义费用值计算每类旅客对不同接续方式的前景效用，考虑在非完全理性决策情境下引入无差异感知效用阈值，寻优至多项Logit模型的最佳拟合效果，最终确定航班旅客的接续方式选择概率。以南宁市吴圩机场进港航班到达旅客前往南宁站作为案例，以2024年1月—12月航空和高铁客票数据为验证集。结果表明：通过模型拟合无差异感知效用最优阈值为0.54，平均绝对误差(MAE)为12.32，均方根误差(RMSE)为15.35，平均绝对百分比误差(MAPE)为8.9%；该区段单日接续方式平均分担率中，高铁占20.04%，大巴占23.15%，出租车占26.25%，私家车占30.56%。本文可为综合交通枢纽规划与运营优化提供参考。

关键词: 综合运输, 接续方式选择, 广义费用, 进港航班旅客, 产品特征, 群体偏好

CLC Number:

WU Xiaoyu, ZHANG Xiaoqiang, CHEN Yuling. Choice Behavior of Connecting Transportation Modes for Arriving Air Passengers Under Synergy Between Product Attributes and Group Preferences[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(5): 158-168.

武笑宇, 张小强, 陈宇凌. 产品特征与群体偏好协同下进港航班旅客接续方式选择行为[J]. 交通运输系统工程与信息, 2025, 25(5): 158-168.

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URL: http://www.tseit.org.cn/EN/10.16097/j.cnki.1009-6744.2025.05.014

http://www.tseit.org.cn/EN/Y2025/V25/I5/158

References

[1] 陈琳,姚恩建,杨扬,等.联程中转旅客的城市枢纽间换乘行为建模[J]. 北京交通大学学报, 2021, 45(4): 79-88. [CHEN L, YAO E J, YANG Y, et al. Modeling of transfer behavior between urban hubs of intermodal passengers[J]. Journal of Beijing Jiaotong University, 2021, 45(4): 79-88.]

[2] KEUMI C, MURAKAMI H. The role of schedule delays on passengers' choice of access modes: A case study of Japan's international hub airports[J]. Transportation Research Part E: Logistics and Transportation Review, 2012, 48(5): 1023-1031.

[3] TORABI F K, ARAGHI Y, VANOORT N, et al. Passengers preferences for using emerging modes as first/last mile transport to and from a multimodal hub case study Delft Campus railway station[J]. Case Studies on Transport Policy, 2022, 10(1): 300-314.

[4] 王子甲,贾慧慧,朱亚迪,等.基于智能卡数据的轨道与公交复合网络通勤方式选择行为研究[J]. 交通运输系统工程与信息, 2022, 22(1): 67-73. [WANG Z J, JIA H H, ZHU Y D, et al. Commuting mode choice behavior in rail and bus composite network based on smart card data [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1): 67-73.]

[5] GAO J J, WANG Y L, ZHOU J C, et al. Combination model of urban tourism transportation based on nested Logit model[J]. Systems Science & Control Engineering, 2022, 10(1): 865-876.

[6] 姚恩建,陈伟迪,卢天伟,等.考虑出行者选择偏好的出行方式选择模型[J]. 北京交通大学学报, 2020, 44 (1): 42-48. [YAO E J, CHEN W D, LU T W, et al. Transportation mode selection model considering traveler's personal preferences[J]. Journal of Beijing Jiaotong University, 2020, 44(1): 42-48.]

[7] 郇宁,姚恩建.城际组合出行行为建模及应用研究进展[J]. 清华大学学报(自然科学版), 2022, 62(7): 1112-1120. [HUAN N, YAO E J. Advances in intermodal travel behaviour modelling[J]. Journal of Tsinghua University(Science and Technology), 2022, 62(7): 1112-1120.]

[8] 韦凌翔,陈红,蒋金亮,等.基于广义费用函数的高铁票价双层规划模型研究[J]. 铁路科学与工程学报, 2015, 12(2): 250-256. [WEI L X, CHEN H, JIANG J L, et al. Study on bi-level programming model of high-speed rail farebased on generalized cost function[J]. Journal of Railway Science and Engineering, 2015, 12(2): 250-256.]

[9] FOLKARD S, TUCKER P. Shift work, safety and productivity[J]. Occupational Medicine-Oxford, 2003, 53(2): 95-101.

[10] MOHLER S R, DILLE J R, GIBBONS H L. The time zone and circadian rhythms in relation to aircraft occupants taking long-distance flights[J]. American Journal of Public Health and the Nation's Health, 1968, 58(8): 1404-1409.

[11] SOBOL I M. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates[J]. Mathematics and Computers in Simulation, 2001, 55(1/3): 271-280.

[12] 朱文忠, 颜颖,聂磊,等.显性差异特征的铁路客运产品谱系设计研究 [J]. 铁道运输与经济, 2022, 44(1): 36-43. [ZHU W Z, YAN Y, NIE L, et al. Pedigree design of railway passenger transport product with dominant differentiation characteristics[J]. Railway Transport and Economy, 2022, 44(1): 36-43.]

[13] 胡心磊, 史峰, 秦进. 面向票价票额综合优化的高速铁路客票分类定价策略 [J]. 铁道学报, 2022, 44(7): 1-10. [HU X L, SHI F, QIN J. Pricing strategy by category for joint optimization of pricing and seat allocation problem in high-speed rail networks[J]. Journal of the China Railway Society, 2022, 44(7): 1-10.]

[14] 郑宣传,魏运,秦勇,等.一种改进K-means模型的城市轨道交通突发事件分级方法 [J]. 交通运输系统工程与信息, 2019, 19(3): 134-140. [ZHENG X C, WEI Y, QIN Y, et al. Classification method of urban rail transit emergencies based on improved K-means algorithm[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(3): 134-140.]

[15] AVINERI E, BOVY P H L. Identification of parameters for a prospect theory model for travel choice analysis[J]. Transportation Research Record, 2008(2082): 141-147.

[16] 丁天贵, 谭衢霖,王雪甜,等.基于云模型和累积前景理论的山区铁路绿色低碳线路优选 [J]. 铁道科学与工程学报, 2024, 21(9): 3822-3830. [DING T G, TAN Q L, WANG X T, et al. Optimal comparison and selection of green and low-carbon railways routes in mountain areas based on cloud model and cumulative prospect theory[J]. Journal of Railway Science and Engineering, 2024, 21(9): 3822-3830.]

Choice Behavior of Connecting Transportation Modes for Arriving Air Passengers Under Synergy Between Product Attributes and Group Preferences

产品特征与群体偏好协同下进港航班旅客接续方式选择行为

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