User retention tendency of bus routes based on user behavior transition in an area with low mode share of public transport. (July 2020)

• Record Type: Journal Article
• Authors: Hung, Pai-Hsien; Doi, Kenji; Inoi, Hiroto
• Is Part Of: IATSS research. Volume 44:Number 2(2020)
• Page Start: 111
• Page End: 124
• Publication Date: 2020-07
• Subjects: Bus user retention -- Odds ratio -- User behavior transition -- Behavior clustering; Traffic safety -- Periodicals; Transportation and state -- Periodicals; Verkeersveiligheid; Internationale organisaties; Traffic safety; Transportation and state; Periodicals; 363.1256
• DOI: 10.1016/j.iatssr.2019.09.001
• Languages: English
• ISSNs: 0386-1112

Abstract

Abstract: Although both public transport and private modes have their own purposes and safety issues, most people are free to choose either way to make a trip. Previous research states that increasing the mode share of public transport is an important transportation policy to improve traffic safety, and it is a key outcome of demand management indeed. However, rather than merely focusing on increasing its ridership, a more reliable way to reach for the universality of a public transport system is through its customer retention tendency. Research on satisfaction with bus service often focuses on the influence of specific variables. However, numerous variables may influence users' decision-making. To ease their work, managers have no choice but to ignore some unknown variables. Therefore, we now propose a bottom-up procedure, which needs only smart card data, to obtain the odds ratio of usage of a specific bus route. Logistic regression models are calibrated based on four behavior groups, and the significant coefficients of route variables represent the odds ratios of the bus route usage. The calibration of odds ratio does not need any individual personal or individual socio-economical information, but only smart card transaction data. This method will dramatically decrease the cost and time for data collection. Further, the procedure proposed in this study can be encapsulated in software, which managers can then use to assist their planning. Highlights: Bus users have significantly different types of behavior that can be clustered. Usage of bus routes significantly influences the probability of user retention. Different user behavior has an impact on the probability of user retention. The calibration needs only smart card transaction data and bus route characteristics. Consideration of bus route characteristics can assist manager to allocate resources.