Statistical Analysis of Fare Compliance

Abstract

Metro Transit uses a Proof of Payment (PoP) system with barrier-free stations on its Hiawatha Light Rail Line. It measures fare compliance to calculate missed revenue and to determine the effectiveness of its efforts to improve compliance. In this project, researchers employ a suite of statistical methodologies for estimating compliance from ticket sales and tagged rides' data as well as data from the Mobile Phone Validator (MPV) device used by Metro Transit police officers and Patrol Activity Logs. Researchers calculate point and interval estimates of the noncompliance rate and the minimum sample sizes needed to realize a desired degree of precision. As a first step in their analysis, researchers confirm that both ridership and noncompliance rate vary by direction, station, stratum, and day-of-week. These factors also affect the precision of noncompliance rate estimates. Researchers estimated the overall noncompliance rate to be 0.55% for weekdays and 0.7% for weekends. They also found that the noncompliance rate was relatively much higher among cardholders. One of their recommendations was that in order to realize a reasonable precision (95% confidence interval to be no more than 10% of the mean rate), Metro Transit would need to reduce weekday inspection frequency by about 30% but increase weekends' inspection frequency by 50% relative to the current practice. Researchers also found that ticket sales and tagged rides' data (currently automated) could provide reliable estimates of ridership, reducing the need to perform manual counts, and could help improve the pricing of Metro Transit's fare products.