Statistical Analysis of Fare Compliance

Friday, February 27, 2015 - 10:00am

About the Event

On the Hiawatha Light-Rail Line and Northstar Commuter Line, Metro Transit uses a proof-of-payment system with barrier-free stations. Trains and station platforms are paid zones, and patrons are expected to carry proof of payment at all times when on the trains or the platform. Compliance is enforced by Metro Transit Police performing spot checks and issuing citations to patrons lacking proof of a valid paid fare. Metro Transit also performs random sampling to estimate noncompliance and ridership. Metro Transit needs to estimate and manage fare compliance to calculate missed revenue and undertake efforts to increase revenue via better compliance.

This presentation provided an overview of a project that developed a suite of methodologies for estimating compliance by using data collected for other purposes (ticket sales, tagged rides, mobile vaildator used by Metro Transit Police, and audits), sampling, and crowd sourcing. All transit agencies that use proof-of-payment systems can benefit from such methodologies. At the present time, transit agencies audit and estimate fare compliance without the benefit of statistically sound methodologies for doing so.


Diwakar Gupta is a professor in the Department of Industrial and Systems Engineering at the University of Minnesota. His transportation research focuses on stochastic models for supply chain management, emphasizing coordination of decentralized supply chains. He directs the Supply Chain Operations and Research Laboratory (SCORLAB), carrying out cutting-edge research in management and operations. He also carries out research on the design and control of manufacturing systems and revenue management in manufacturing and service industries.