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

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

Authors: Raya Etminani-Ghasrodashti (Texas A&M Transportation Institute), Muhammad Khan (University of Texas at Arlington), Ronik Ketankumar Patel (University of Texas at Arlington), Sharareh Kermanshachi (University of Texas at Arlington), Jay Michael Rosenberger (University of Texas at Arlington), Apurva Pamidimukkala (University of Texas at Arlington), Greg Hladik (University of Texas at Arlington), Ann Foss (City of Arlington)

Published in: International Journal of Transportation Science and Technology

Publication Date: September 2024

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.