RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study
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 transport...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/1108105 |
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| _version_ | 1849304662192160768 |
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| author | Angela H. L. Chen Yun-Chia Liang Wan-Ju Chang Hsuan-Yuan Siauw Vanny Minanda |
| author_facet | Angela H. L. Chen Yun-Chia Liang Wan-Ju Chang Hsuan-Yuan Siauw Vanny Minanda |
| author_sort | Angela H. L. Chen |
| collection | DOAJ |
| description | 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 MRT or bus to YouBike within a particular period. Therefore, 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. There 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. |
| format | Article |
| id | doaj-art-41c385d7cd104c4f92d3de6a1fc630ee |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-41c385d7cd104c4f92d3de6a1fc630ee2025-08-20T03:55:40ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/1108105RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case StudyAngela H. L. Chen0Yun-Chia Liang1Wan-Ju Chang2Hsuan-Yuan Siauw3Vanny Minanda4Department of Industrial and Systems EngineeringDepartment of Industrial Engineering and ManagementDepartment of Industrial and Systems EngineeringDepartment of Industrial and Systems EngineeringDepartment of Industrial Engineering and ManagementPublic 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 MRT or bus to YouBike within a particular period. Therefore, 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. There 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.http://dx.doi.org/10.1155/2022/1108105 |
| spellingShingle | Angela H. L. Chen Yun-Chia Liang Wan-Ju Chang Hsuan-Yuan Siauw Vanny Minanda RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study Journal of Advanced Transportation |
| title | RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study |
| title_full | RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study |
| title_fullStr | RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study |
| title_full_unstemmed | RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study |
| title_short | RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study |
| title_sort | rfm model and k means clustering analysis of transit traveller profiles a case study |
| url | http://dx.doi.org/10.1155/2022/1108105 |
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