Research on Passenger Clustering Based on Shopping Behavior During High-Speed Rail Journeys

XingYu Su (), XinSheng Ke (), Hongquan Ren, Qiao Li, Jianqiu Huang, Yina Li, Qingrui Meng () and Ping Yin ()
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XingYu Su: Beijing Jiaotong University
XinSheng Ke: Beijing Jiaotong University
Hongquan Ren: Beijing-Shanghai High Speed Railway Co., Ltd.
Qiao Li: Beijing-Shanghai High Speed Railway Co., Ltd.
Jianqiu Huang: Beijing-Shanghai High Speed Railway Co., Ltd.
Yina Li: Beijing-Shanghai High Speed Railway Co., Ltd.
Qingrui Meng: Beijing Jiaotong University
Ping Yin: Beijing Jiaotong University

A chapter in LISS 2024, 2025, pp 497-507 from Springer

Abstract: Abstract High-speed rail has become a preferred mode of transportation for many people today, making it crucial to provide targeted shopping services at stations and on board. However, most research on transportation shopping services focuses on airports, with few studies addressing shopping services in the context of high-speed rail. This study employs subspace clustering to segment target populations. The results indicate that the four identified groups differ in high-speed rail shopping intentions, behavior characteristics, and demographic features. Based on hypothesis testing, we analyzed and summarized potential characteristics and patterns influencing passengers’ shopping intentions during high-speed rail journeys. These findings can help high-speed rail operators develop customized marketing strategies for different passenger groups.

Keywords: high-speed rail travel; shopping intentions; characteristics of high-speed rail travel; population segmentation; high-dimensional clustering (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-96-9697-0_39

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DOI: 10.1007/978-981-96-9697-0_39

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