Sponsor
This project was funded by the National Institute for Transportation and Communities (NITC). Funding was also provided by City of Eugene, OR, City of Gresham, OR, Lane Transit District, and Clevor Consulting Group, RTD Denver.
Document Type
Report
Publication Date
5-2021
Subjects
Local transit -- Fares -- Automation, Smart cities, Transportation -- Planning, Local transit accessibility
Abstract
Many transit agencies plan to automate their fare collection and limit the use of cash for payment, with the goals of improving boarding and data collection while lowering operating costs. Still, about 10% of adults in the United States lack a bank account or credit card, and many either rely on restrictive cell-phone data plans or don’t have access to internet or a smartphone, making it very challenging for these riders to ride. In light of these potential challenges, this project was developed by a group of transit agencies and consultants interested in the barriers faced by riders, and what they could do to address them. The project asks three questions: 1. who are most at risk of being excluded as agencies transition to automated payment systems?; 2. what practices are being used to reduce the numbers of riders affected?; and 3. how effective are those practices? The project explores these issues in the cities of Denver, Colorado, and Eugene and Portland–Gresham, Oregon. We examine the first question about transit users’ experiences with emerging technologies through small group conversations and a larger sample survey of riders in our three cities. Our analysis reveals which transit users are most at risk of being excluded if cash payment options were limited. We find that a significant number of riders (~30%) currently pay cash on-board buses. If on-board cash options were removed, most of these riders report being able to switch to other payment options, though many imagine they will continue to use cash in some other way (at retail or ticket vending machines). A small share of riders, however, claim they would not be able to ride any longer. Older and lower-income riders are more at risk of exclusion as they often lack access to smartphones or internet. We answered the second question using a national scan of agency practice, including the practices of those supporting this project. To address our third question, we develop a cost-effectiveness framework which captures the costs and benefits created by systems involved in fare collection, especially those that add cash acceptance capabilities. Our framework combines a qualitative and quantitative analysis. The qualitative analysis captures how systems foster access to fare payment generally, but also how systems exacerbate inequalities. The quantitative analysis measures the total costs and revenues of different fare payment configurations. Combined, the model can be used to evaluate different systems which enable cash acceptance. We then use this model to explore case scenarios using the examples of Denver, Eugene, and Portland–Gresham. The model shows that generally, efforts to add cash acceptance are quite cost-effective at retaining riders who may be challenged by the conversion to automated fare payment. The model shows that adding a retail network to facilitate fare payment as well as preserving cash acceptance on board buses through the farebox are highly effective solutions. The model is customizable for any agency and similar analyses can be run for different configurations of fare collection systems.
DOI
10.15760/trec.261
Persistent Identifier
https://archives.pdx.edu/ds/psu/36387
Recommended Citation
Golub, A., J. MacArthur, C. Brakewood and A. Brown. Applying an Equity Lens to Automated Payment Solutions for Public Transportation. NITC-RR-1268. Portland, OR: Transportation Research and Education Center (TREC), 2021. https://dx.doi.org/10.15760/trec.261
Description
This is a final report, NITC-RR-1268, from the NITC program of TREC at Portland State University, and can be found online at: https://nitc.trec.pdx.edu/research/project/1268.
The research data is available online at: https://doi.org/10.15760/TREC_datasets.14