Measuring students’ satisfaction levels for transit services: An application of latent class analysis

Abstract

Past studies have identified the general public’s level of satisfaction with the service attributes of conventional fixed-route transit and ridesharing services, but few have limited their focus to students. This study employs latent class cluster analysis (LCCA) to identify clusters of university students, based on their satisfaction levels of the attributes of conventional fixed-route and ridesharing services, and uses a latent class behavioral model of a sample of university students in Arlington, Texas to explore the heterogeneity of their preferences toward ridesharing services. The results indicate that younger- and lower-income populations are more likely to be satisfied with on-demand ridesharing services than older- and higher-income populations, females are more likely to be satisfied with ridesharing services than males, and domestic students are more likely to be satisfied with ridesharing services than international students. The outcomes of the study will provide transportation planners with new insights about the significance of sociodemographic factors on the satisfaction level of those who use conventional transit and on-demand ridesharing services and will help them incorporate strategies that will make their services more attractive to their potential ridership.