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

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  • 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
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    References listed on IDEAS

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