INDUSTRY:
TRANSPORTATION
YEAR:
2023
EXPERIENCE:
EDA, REGRESSION ANALYSIS
WMATA
about.
WMATA (Washington Metropolitan Area Transit Authority) Fare Evasion Analysis
This project investigated fare evasion patterns across Washington, D.C.’s Metrorail system to support decisions on retrofitting newly installed faregates. Using WMATA station-level data from January to June 2023, I built a multiple regression model to identify key factors associated with unpaid entries (“non-tap riders”). The analysis confirmed that fare evasion is more frequent on weekdays and at high-traffic stations with fewer rail lines. The findings offered actionable insights to help prioritize faregate upgrades across 98 stations and improve transit system efficiency.
results.
The regression model revealed that stations with higher weekday ridership and fewer connecting rail lines experienced significantly higher fare evasion rates. In particular, stations with limited transfer options and higher entry volume showed the largest share of “non-tap” entries. These insights helped prioritize a short list of high-impact stations for faregate upgrades, enabling WMATA to allocate retrofit investments more effectively. The model's explanatory power also demonstrated the usefulness of operational data for guiding capital planning decisions.
