IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i11p1896-d1672794.html
   My bibliography  Save this article

Leveraging Advanced Mathematical Methods in Artificial Intelligence to Explore Heterogeneity and Asymmetry in Cross-Border Travel Satisfaction

Author

Listed:
  • Yan Xu

    (Institute of Urban and Sustainable Development, City University of Macau, Macau SAR 999078, China
    Faculty of Innovation and Design, City University of Macau, Macau SAR 999078, China)

  • Huajie Yang

    (Institute of Urban and Sustainable Development, City University of Macau, Macau SAR 999078, China)

  • Zibin Ye

    (Institute of Urban and Sustainable Development, City University of Macau, Macau SAR 999078, China)

  • Xiaobo Ma

    (Department of Civil & Architectural Engineering & Mechanics, The University of Arizona, Tucson, AZ 85721, USA)

  • Lei Tong

    (School of Tourism and Urban-Rural Planning, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Xinyi Yu

    (Institute of Urban and Sustainable Development, City University of Macau, Macau SAR 999078, China)

Abstract

The cross-border port serves as a crucial cross-border travel connecting mainland China with Hong Kong and Macau, directly impacting the overall satisfaction of cross-border travel. While previous studies on neighborhoods, communities, and other areas have thoroughly examined the heterogeneity and asymmetry in satisfaction, research on the satisfaction of cross-border travel at ports remains notably limited. This paper explores the heterogeneity and asymmetry of cross-border travel satisfaction using gradient boosted decision trees (GBDT) and k-means cluster analysis under the framework of three-factor theory, aiming to demonstrate the latest scientific research results on the fundamental theories and applications of artificial intelligence. The results show prevalent asymmetric relationships between factors and cross-border travel satisfaction, with the factor structure exhibiting heterogeneity across different groups. High-income individuals were more likely to prioritize the reliability of cross-border travel, whereas low-income individuals tended to emphasize the convenience of travel. Finally, this paper proposes improvement priorities for different types of passengers, reflecting the practical application of advanced mathematical methods in artificial intelligence to drive intelligent decision-making.

Suggested Citation

  • Yan Xu & Huajie Yang & Zibin Ye & Xiaobo Ma & Lei Tong & Xinyi Yu, 2025. "Leveraging Advanced Mathematical Methods in Artificial Intelligence to Explore Heterogeneity and Asymmetry in Cross-Border Travel Satisfaction," Mathematics, MDPI, vol. 13(11), pages 1-23, June.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:11:p:1896-:d:1672794
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/11/1896/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/11/1896/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chiambaretto, Paul & Baudelaire, Clément & Lavril, Thibaut, 2013. "Measuring the willingness-to-pay of air-rail intermodal passengers," Journal of Air Transport Management, Elsevier, vol. 26(C), pages 50-54.
    2. Wu, Xinyi & Cao, Jason & Huting, Joel, 2018. "Using three-factor theory to identify improvement priorities for express and local bus services: An application of regression with dummy variables in the Twin Cities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 184-196.
    3. Kurt Matzler & Elmar Sauerwein & Kenneth Heischmidt, 2003. "Importance-performance analysis revisited: the role of the factor structure of customer satisfaction," The Service Industries Journal, Taylor & Francis Journals, vol. 23(2), pages 112-129, March.
    4. Yuan, Yalong & Yang, Min & Feng, Tao & Rasouli, Soora & Li, Dawei & Ruan, Xinpei, 2021. "Heterogeneity in passenger satisfaction with air-rail integration services: Results of a finite mixture partial least squares model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 133-158.
    5. Abenoza, Roberto F. & Cats, Oded & Susilo, Yusak O., 2017. "Travel satisfaction with public transport: Determinants, user classes, regional disparities and their evolution," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 64-84.
    6. Juan I. Ramos, 2025. "Piecewise Analytical Approximation Methods for Initial-Value Problems of Nonlinear Ordinary Differential Equations," Mathematics, MDPI, vol. 13(3), pages 1-20, January.
    7. Claudia Cappello & Antonella Congedi & Sandra De Iaco & Leonardo Mariella, 2025. "Traditional Prediction Techniques and Machine Learning Approaches for Financial Time Series Analysis," Mathematics, MDPI, vol. 13(3), pages 1-21, February.
    8. Jiayi Xu & Mario Di Nardo & Shi Yin, 2024. "Improved Swarm Intelligence-Based Logistics Distribution Optimizer: Decision Support for Multimodal Transportation of Cross-Border E-Commerce," Mathematics, MDPI, vol. 12(5), pages 1-20, March.
    9. Yifeng Ren & Min Yang & Enhui Chen & Long Cheng & Yalong Yuan, 2024. "Exploring passengers’ choice of transfer city in air-to-rail intermodal travel using an interpretable ensemble machine learning approach," Transportation, Springer, vol. 51(4), pages 1493-1523, August.
    10. Wang, Jue & Chandra, Kevin & Du, Coco & Ding, Weizhen & Wu, Xun, 2021. "Assessing the Potential of Cross-border regional innovation Systems:A case study of the Hong Kong -Shenzhen region," Technology in Society, Elsevier, vol. 65(C).
    11. Yang, Min & Wang, Zheyuan & Cheng, Long & Chen, Enhui, 2022. "Exploring satisfaction with air-HSR intermodal services: A Bayesian network analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 156(C), pages 69-89.
    12. Philippe Gerber & Marius Thériault & Christophe Enaux & Samuel Carpentier-Postel, 2020. "Links between Attitudes, Mode Choice, and Travel Satisfaction: A Cross-Border Long-Commute Case Study," Sustainability, MDPI, vol. 12(21), pages 1-20, November.
    13. Esmailpour, Javad & Aghabayk, Kayvan & Abrari Vajari, Mohammad & De Gruyter, Chris, 2020. "Importance – Performance Analysis (IPA) of bus service attributes: A case study in a developing country," Transportation Research Part A: Policy and Practice, Elsevier, vol. 142(C), pages 129-150.
    14. Tuan, Vu Anh & Van Truong, Nguyen & Tetsuo, Shimizu & An, Nguyen Ngoc, 2022. "Public transport service quality: Policy prioritization strategy in the importance-performance analysis and the three-factor theory frameworks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 118-134.
    15. Luo, Shuli & He, Sylvia Y. & Grant-Muller, Susan & Song, Linqi, 2023. "Influential factors in customer satisfaction of transit services: Using crowdsourced data to capture the heterogeneity across individuals, space and time," Transport Policy, Elsevier, vol. 131(C), pages 173-183.
    16. Singh, Harpreet & Kathuria, Ankit, 2023. "Heterogeneity in passenger satisfaction of bus rapid transit system among age and gender groups: A PLS-SEM Multi-group analysis," Transport Policy, Elsevier, vol. 141(C), pages 27-41.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Raky Julio & Andres Monzon & Yusak O. Susilo, 2024. "Identifying key elements for user satisfaction of bike-sharing systems: a combination of direct and indirect evaluations," Transportation, Springer, vol. 51(2), pages 407-438, April.
    2. Yuan, Yalong & Yang, Min & Feng, Tao & Ma, Yafeng & Ren, Yifeng & Ruan, Xinpei, 2022. "Heterogeneity in the transfer time of air-rail intermodal passengers based on ticket booking data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 533-552.
    3. Wang, Xiaoquan & Han, Jiaxin & Yin, Chaoying & Shao, Chunfu & Zhang, Junyi, 2025. "Built environment and travel satisfaction revisited: Differences between consonant and dissonant travelers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 192(C).
    4. Rubensson, Isak & Börjesson, Maria, 2018. "Satisfaction with crowding in public transport," Working papers in Transport Economics 2018:6, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    5. Chen, Lin & Yao, Enjian & Yang, Yang & Pan, Long & Liu, ShaSha, 2024. "Understanding passengers' intermodal travel behavior to improve air-rail service: A case study of Beijing-Tianjin-Hebei urban agglomeration," Journal of Air Transport Management, Elsevier, vol. 118(C).
    6. Danica Babić & Milica Kalić & Milan Janić & Slavica Dožić & Katarina Kukić, 2022. "Integrated Door-to-Door Transport Services for Air Passengers: From Intermodality to Multimodality," Sustainability, MDPI, vol. 14(11), pages 1-20, May.
    7. Yifeng Ren & Min Yang & Enhui Chen & Long Cheng & Yalong Yuan, 2024. "Exploring passengers’ choice of transfer city in air-to-rail intermodal travel using an interpretable ensemble machine learning approach," Transportation, Springer, vol. 51(4), pages 1493-1523, August.
    8. Chee, Pei Nen Esther & Susilo, Yusak O. & Wong, Yiik Diew, 2020. "Determinants of intention-to-use first-/last-mile automated bus service," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 350-375.
    9. Huan, Ning & Yamamoto, Toshiyuki & Yao, Enjian, 2023. "Seamless air-HSR intermodal solution: Behavioural model-based scheduling of airline timetable and airfare," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    10. Yulu Zhao & Xinye Xu & Gangwei Cai & Zhetao Hu & Yan Hong, 2022. "Promoting Strategies for Healthy Environments in University Halls of Residence under Regular Epidemic Prevention and Control: An Importance—Performance Analysis from Zhejiang, China," IJERPH, MDPI, vol. 19(23), pages 1-19, November.
    11. Tuan, Vu Anh & Van Truong, Nguyen & Tetsuo, Shimizu & An, Nguyen Ngoc, 2022. "Public transport service quality: Policy prioritization strategy in the importance-performance analysis and the three-factor theory frameworks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 118-134.
    12. Hussain, Atif & Shafiq, Adnan & Awan, Muhammad Usman & Hashmi, Junaid Iqbal, 2025. "A text mining study of online reviews to understand intercity bus service quality," Transport Policy, Elsevier, vol. 162(C), pages 325-335.
    13. Wang, Yucheng & Gao, Yanan, 2022. "Travel satisfaction and travel well-being: Which is more related to travel choice behaviour in the post COVID-19 pandemic? Evidence from public transport travellers in Xi’an, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 218-233.
    14. Yang, Min & Wang, Zheyuan & Cheng, Long & Chen, Enhui, 2022. "Exploring satisfaction with air-HSR intermodal services: A Bayesian network analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 156(C), pages 69-89.
    15. Yuan, Yalong & Yang, Min & Feng, Tao & Rasouli, Soora & Li, Dawei & Ruan, Xinpei, 2021. "Heterogeneity in passenger satisfaction with air-rail integration services: Results of a finite mixture partial least squares model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 133-158.
    16. Barbora Mazúrová & Ján Kollár & Gabriela Nedelová, 2021. "Travel Mode of Commuting in Context of Subjective Well-Being—Experience from Slovakia," Sustainability, MDPI, vol. 13(6), pages 1-17, March.
    17. NaHyun Lee & Bong-Seok Kim, 2023. "Differences of Host Country-Destination Image Assessment for International Students According to Risk Perception in COVID-19 Tourism," SAGE Open, , vol. 13(3), pages 21582440231, July.
    18. Yu Du & Xian Ji & Chenxi Dou & Rui Wang, 2024. "Boosting Winter Green Travel: Prioritizing Built Environment Enhancements for Shared Bike Users Accessing Public Transit in the First/Last Mile Using Machine Learning and Grounded Theory," Sustainability, MDPI, vol. 16(22), pages 1-25, November.
    19. Jiacong Wu & Yu Wang & Ru Zhang & Jing Cai, 2018. "An Approach to Discovering Product/Service Improvement Priorities: Using Dynamic Importance-Performance Analysis," Sustainability, MDPI, vol. 10(10), pages 1-26, October.
    20. Pumpe, Andreas & Vallée, Franz, 2015. "The Total Landed Cost Concept. Begging for Answers," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Blecker, Thorsten & Kersten, Wolfgang & Ringle, Christian M. (ed.), Operational Excellence in Logistics and Supply Chains: Optimization Methods, Data-driven Approaches and Security Insights. Proceedings of the Hamburg , volume 22, pages 31-54, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:13:y:2025:i:11:p:1896-:d:1672794. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.