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

Yan Xu, Huajie Yang (), Zibin Ye, Xiaobo Ma, Lei Tong and Xinyi Yu
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Yan Xu: Institute of Urban and Sustainable Development, 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

Mathematics, 2025, vol. 13, issue 11, 1-23

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.

Keywords: three-factor theory; advanced algorithms in machine learning; cross-border travel; k-means clustering; gradient boosting decision trees; nonlinear effect (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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