Estimating Intermodal Transfer Barriers to Light Rail using Smartcard Data in Seattle, WA

Transit transfers are a necessary inconvenience to riders. They support strong hierarchical networks by connecting various local, regional, and express lines through a variety of modes. This is true in Seattle, where many lines were redrawn to feed into the Link Light Rail network. Previous transfer studies, using surveys, found that perceived safety, distance, and personal health were significant predictors of transfers. This study aims to use smartcard data and generalized linear modeling to estimate which elements of transfers are commonly overcome-and which are not-among riders boarding the Link Light Rail in Seattle and its suburbs. The aims of this research are twofold: (1) critical analysis of attributes of transfer barriers so that the future station area could serve improved riders' accessibility; (2) equity of transfer barriers among the users by analyzing the user breakdown of the origin lines and the destination. We use Seattle's One Regional Card for All smartcard data among the Link Light Rail riders in the Seattle metropolitan area in 2019, and applied a negative binomial generalized linear model. The model suggests that walking distance and walking grade have significant effects on transfers. For the users' equity analysis, the disabled population tends to transfer less, while the low-income and youth riders populations tend to transfer more often. Future research could incorporate a more mixed-methods approach to confirm some of these findings or include station amenities, such as live schedule updates for common transfer lines.