Throughout California, using public transit often requires moving between operators with different payment mediums and scheduling specifications. While some metropolitan regions offer integrated payment options they don’t work for all travelers and still require a transit agency issued card and/or account. At the same time, when connecting to services not within the integrated portion, travelers must have a second, third or even additional means of payment for each transit system use. The California Integrated Travel Project aims to allow transit users to pay for transit throughout the state of California with whichever card based, or electronic means of payment they prefer. However, for some passengers some of these options are not available. In this study we explore how these un and underbanked passengers will be able to access open payment systems. We collected intercept survey data at transit stops in the Davis-Sacramento-Woodland area of California to assess the potential for un and underbanked passengers to use digital payment tools, such as tap cards, and smartphone-based apps. We find that these riders typically pay with cash now but have some interest in payments with prepaid debit cards, and other means of payments. Accessibility for un and underbanked passengers is a known priority of Cal-ITP, and as the state takes the next steps towards open payments systems throughout California’s transit systems, the outcomes of this study will help ensure the needs of these passengers are met.

This dataset was collected through an intercept survey at public transit stops in the Sacramento area of California.

Incomplete cases have been removed, identifying information has been removed. Other processing is limited to recoding variables based on participant responses, and creating binary variables from those that allowed "check all that appy" responses.

There are missing values due to item non-response for some variables. These are primarily coded as NA for use in the data processing and analysis environment R.

Many variables have been converted to factors, applying an order to the values, that might not otherwise have them or that would not be represented by text descriptions of the levels. This includes things like "no", and "yes", or more complex sets of valued text data such as "high school", "associates", "bachelors", "masters", "graduate degree".