RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study

Abstract

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.

Customer Segmentation Using K-Means Clustering Algorithm and RFM Model

“…In the literature, RFM Model and the K-means algorithm are widely used for customer segmentation [1][2][3][4][5][6][8][9][10][11][12][13][14]. Zhao et al [1] combined both methods and the additional Apriori algorithm to segment customers and provided a recommendation system using historical sales data.…”

“…Huang et al [11] improved the RFM model by extending the community (C) dimension to represent community relations to introduce the value of social interaction to the educational ecommerce system and achieved accuracy by the modification. Chen et al [9] investigated the travel patterns of using public transport via subway and bike-sharing using the RFM model and K-means clustering.…”

“…One of the most popular clustering algorithms, K-Means, is used for clustering customers with similar behaviors. In the literature, significant applications of K-Means are seen as customer segmentation which assists in specifying customer segments, such as most expensive spenders, inexpensive spenders, average spenders, etc., in improving the marketing strategies [8][9][10][11][12][13][14]. The k-means algorithm is an unsupervised learning algorithm used to group data into the optimal number of clusters, where similar data is located in the same cluster, whereas different data are assigned to different clusters.…”