Research Article RFMModeland K-MeansClusteringAnalysisofTransitTraveller Profiles: A Case Study Angela H. L. Chen , 1 Yun-Chia Liang , 2 Wan-Ju Chang, 1 Hsuan-Yuan Siauw, 1 and Vanny Minanda 2 1 Department of Industrial and Systems Engineering, Chung Yuan Christian University, Taoyuan 320, Taiwan 2 Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan 320, Taiwan Correspondence should be addressed to Angela H. L. Chen; achen@cycu.edu.tw and Yun-Chia Liang; ycliang@saturn.yzu.edu.tw Received 4 April 2022; Revised 6 July 2022; Accepted 7 July 2022; Published 8 August 2022 Academic Editor: Zhixiang Fang Copyright © 2022 Angela H. L. Chen et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Public transportation users increase as the population grows. In Taipei, Taiwan,this tendency is observed by analyzing historical data from the Mass Rapid Transit (MRT) and economy-shared bicycle (known as YouBike) riders. While this trend exists, the Taipei City government promotes green transportation by providing discounts to users who transfer from MRTor bus to YouBike within a particular period. erefore, this study focuses on analyzing the patterns of users in order to identify possible clusters. Clusters of customers can be considered fundamental and competitive factors for the Ministry of Transportation to encourage the use of green transportation and promote a sustainable environment. Based on big data smart card information, this paper proposes using the RFM and K-means clustering algorithm to analyze and construct mode-switching traveller profiles on MRT and YouBike riders. As a result, three distinct clusters of MRT-YouBike riders have been identified: potential, vulnerable, and loyal. ere are also suggestions regarding the most profitable groups, which customers to focus on, and to whom give special offers or promotions to foster loyalty among transit travellers. 1.Introduction Public transport, defined as high-capacity vehicle sharing with fixed routes and schedules, will remain an essential engine to economic activities, social connections, and the standard of living. Due to traffic density and the demands on road infrastructure, land, material, energy, and workforce have been invested in providing transport services and developing its infrastructure. As transport demand con- tinues growing, particularly in fast-developing nations, many cities expand their transportation networks and support infrastructure, indicating how vital the transport system is to economies and social welfare. A 2018 McKinsey report[1] concluded that wealthier cities have greater op- portunities to build advanced transportation systems, but such prosperity does not guarantee the successful devel- opment of such systems. According to the 2020 “Foresight Research Survey,” as many as 81.1% of Taiwanese people have access to private transportation and only 44.4% rely on public transport for their daily commute. In addition, over 80% of those aged 18 or older rely on private transportation, primarily gasoline-powered motorcycles. Evidence has shown that people prefer to travel using their vehicles, which imposes considerable challenges to reducing private trans- portation dependence and encouraging the use of public transportation. However, since people naturally avoid transit they perceive as incompatible with their demands, such transformations are fraught with difficulty. Taipei’s Mass Rapid Transit (MRT), the first subway system built in Taiwan, has already become a hallmark of Taipei City. Residents in Taipei welcomed its arrival and viewed it as an example of the city’s bright future. e Taipei Metro, once known as the Taipei Rapid Transit Corporation (TRTC), is a city government public transit operator. e MRT has made commuting more accessible for people in Taipei; however, its annual ridership from 770 million visits Hindawi Journal of Advanced Transportation Volume 2022, Article ID 1108105, 14 pages https://doi.org/10.1155/2022/1108105