Carsharing travel distance and its associated factors: A case study of Seoul, South Korea

Abstract

This study explores the mobility patterns of carsharing members from their trip distance perspective and its associated factors with a specific focus on its members' personal, usage, and stations' locational characteristics. Using Seoul as a case study, one-month rental transaction datasets provided by two-way carsharing operators were used as a data source. The multilevel mixed-effect modeling approach was applied to remedy spatial heterogeneity across station locations that affect the distance traveled by each rental. In addition, a classification among the carsharing members based on trip distance was conducted using regression tree to obtain clusters of the most homogenous member groups. The multilevel model results confirmed the important roles played by the station location and individual-level factors that affect mobility patterns of carsharing members. Individual-level characteristics showed that members in their 50s and female travel longer. Similarly, rentals made on non-workdays and in the morning showed longer travel distances. The station-level characteristics indicate that carsharing stations' proximity to public transit and leisure areas positively affects trip distances, suggesting the effect of the built environment and land use on the travel patterns of carsharing members. By combining carsharing transaction and their stations’ built environment data, this study suggests a new interface for city officials and carsharing operators to work together for achieving their sustainable mobility objectives together.