Understanding Public Transit Rider Satisfaction Using Clustering and Bayesian Regression Methods

Public transit rider satisfaction is well-studied in the academic literature and transit industry. Numerous studies have focused on the factors that drive overall satisfaction and thus provide ample insights to transit agencies on investment priorities. However, there is less published research on the difference in satisfaction across transit mode (light rail, commuter rail, bus), bus route-type (express, arterial bus rapid transit, local service), or demographic groups. This study builds the body of research by providing a comprehensive assessment of public transit rider satisfaction among Metro Transit riders in the Minneapolis/St. Paul metropolitan area. Additionally, it proposes a methodology for analyzing surveys that addresses the categorical and interdependent nature of survey data – a process that employs Gower’s distance and a partitioning around medoids (PAM) clustering algorithm to segment riders based on attitudes along with a Bayesian logistic regression model to profile the unique identified clusters. Light rail, arterial bus rapid transit, express, and particularly commuter rail riders were much more likely to be satisfied when compared to local bus riders. Satisfaction tended to increase with age, low and high-income riders were more satisfied than middle income riders, people of color tended to have slightly lower satisfaction than white riders, while riders who reported having a disability were somewhat more satisfied. Transit reliant riders tended to be less satisfied, whereas new transit riders (less than two years of riding experience) were more satisfied than more experienced riders. Riders who had experienced various forms of street harassment on transit were less satisfied.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AP015 Standing Committee on Transit Capacity and Quality of Service. Alternate title: Attitudinal Segmentation of Public Transit Riders with Clustering and Bayesian Regression to Understand Heterogeneity in Satisfaction
  • Authors:
    • Huting, Joel
    • Ky, Kim Eng
    • Lind, Eric
    • Freese, Rebecca
    • Pansch, Joshua
  • Conference:
  • Date: 2018

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 17p

Subject/Index Terms

Filing Info

  • Accession Number: 01659981
  • Record Type: Publication
  • Report/Paper Numbers: 18-05209
  • Files: TRIS, TRB, ATRI
  • Created Date: Jan 8 2018 11:19AM