Fare-Capping Impact Analysis Using Mobile Ticket Data

Abstract

In the wake of the COVID-19 pandemic, transit agencies experienced ridership and revenue losses alongside operating cost increases. At the same time, transit was further highlighted as an essential public good alongside essential workers who relied on transit to conduct a range of services in critical industries, thus emphasizing the role of transit in promoting accessibility, equity, and economic opportunity. In grappling with the current volatile environment, including the ongoing travel and financial uncertainties from the pandemic, transit agencies have attempted to delicately balance fares and financing. In turn, many have considered progressive fare policies such as fare capping. For transit agencies seeking innovative fare changes, fare impact and equity analyses are necessary to better understand how riders respond to fares and changes in fares, and how changes to fares and ridership affect accessibility, equity, revenue, and funding. With the increased use of mobile ticketing, small to mid-sized transit agencies now have access to detailed rider behavior data that can be used to estimate rider responses to fare structure changes. In this paper, we present a multinomial logit fare product choice model that uses rider insight from a sample of mobile ticketing data to account for switching between fare products. We apply the model to forecast the ridership response to incremental fare changes and fare capping.