The Metropolitan Transportation Authority (MTA), which operates New York City’s subway, has started employing artificial intelligence (AI) to combat the growing issue of fare evasion, a problem that costs the city hundreds of millions each year. The AI surveillance software, created by the Barcelona-based company AWAAIT, has been in use in seven subway stations...
NYC Subway Measures Fare Evasion Using AI
MTA’s New Approach to Fare Evasion: A Focus on Education, Equity, Environment, and Enforcement
The Four E's of MTA's Anti-Fare Evasion Strategy Unveiled in the Blue-Ribbon Panel's Inaugural Report
The Metropolitan Transportation Authority (MTA) Blue-Ribbon Panel on Fare Evasion released its first report, outlining a number of proposed improvements, during a press conference on May 17th. The report was intended to address the growing issue of fare evasion. The report notably devotes four pages (55–64) to discussing four areas where the MTA intends...
MTA’s Pioneering Methodologies to Measure Fare Evasion
The synthesis of traditional surveys and state-of-the-art technology creates a robust blueprint for fare evasion measurement
On May 17, the Blue-Ribbon Panel for Fare Evasion of the Metropolitan Transportation Authority (MTA) issued a report detailing the problem and outlining its two methods for measuring it. According to the most recent data, a concerning pattern has emerged: fare evasion rates increased significantly from pre-pandemic levels of 3 to 6% to 13.5%...
The Challenge of Measuring Fraud in Public Transport
Methods, Limitations and the Assistance of AI
Challenges in Measuring Fare Evasion Fraud in public transportation is a major issue that causes significant financial and operational challenges for public transport operators. However, accurately measuring the extent of fare evasion is a challenging task, as current methods of measuring fare evasion have significant limitations. The good news is that artificial intelligence (AI)...
The role of AI Video Analytics for reducing fare evasion in public transport
Transport managers can draw more value from their video surveillance networks by employing intelligent Video Analytics using AI
Making better use of existing video networks Overcoming the challenge of manually analysing video feeds from security cameras is one of the biggest tasks for companies that use video surveillance on their premises. Security agents have to spend many hours checking multiple computer screens, and oftentimes there are not sufficient agents to review all...
Deep Learning Video Analytics for protecting public transport revenues, assets, passengers and mobility
AI Video Analytics helps to tackle fare evasion by analysing video streams in real-time
Fare evasion is the unlawful act of traveling in public transport without paying for the trip. It is a corrosive problem in many public transit systems around the world, causing feelings of unfairness and insecurity among paying passengers, and financial losses worth billions of dollars yearly. Some metro and commuter train operators deploy mass...