Passenger scoring for free-pass promotion in public transportation

Abstract

The focus of promotions targeted to increase the use of public transportation concentrates on increasing the attractiveness of it, particularly by decreasing transportation fares. To serve that purpose, this paper proposes a novel passenger scoring model, namely RFLT (recency, frequency, loyalty, and time), for offering a free-pass promotion in public transportation. It presents the comparison results of RFLT and wRFLT (weighted version) using a real-world dataset obtained by a near field communication (NFC) mobile payment application. The experimental results show that the w-RFLT model provides a more balanced score distribution than the RFLT model, and the frequency parameter (F), among four metrics (R, F, L, and T), influences the scoring results the most. The results of this study can be used to establish an efficient policy for increasing public transportation ridership.

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

“…e algorithm finds optimal groups (clusters) of customers, transactions, or other behaviours and things with high similarities and characteristics within the clusters. Many applications of cluster analysis have been applied in various industries, such as banking [33][34][35], energy supply [36], agriculture, food [37][38][39], health and insurance [40][41][42][43], telecom [44][45][46][47][48], postal service [49,50], transportation [14,17,26,29,[51][52][53][54][55][56], and retail [38,[57][58][59][60][61][62]. Furthermore, other researchers focus on customer relationship management (CRM) models and adopt them in K-means clustering.…”

“…In Chiang [54]; the concept of RFM was applied to discover valuable airline travellers, and the association rules led to identifying the optimal target markets. Also, based on the insights mentioned above, determining traveller values in each type of transportation study has its characteristics that are not fully met by the same model [56].…”