New York City Taxi Trips and Tolls: A Test for Fare Fraud?

1. New York City Taxi Trips and Tolls:A Test for Fare Fraud? David A. King, Ph.D. Columbia University Jonathan R. Peters, Ph.D. The College of Staten Island & The CUNY Graduate School International Association of Transportation Regulators 2012 Annual Conference Washington, DC November 16, 2012

2. New York City Medallion Data • In March 2005, the New York City Taxi and Limousine Commission (NYC TLC) issued a Request for Proposals to develop and deploy a new system of fare collection for their medallion (Yellow) cab fleet (12,533 medallions). • The system was designed to provide both credit card based fare collection and enhanced passenger information. • Included in the technology package was an electronic trip sheet function that recorded the origin, destination, fare, payment method and other information. • This system was initially operational in February 2008 and fully deployed as of January 2009. • This data is recorded on a trip by trip basis and is stored in NYC TLC databases.

3. Full NYC Taxicab GPS DataConsisting of378,532,118 Records for the 18 months from February 2008 to November 2010

4. Data Analysis Data received from NYC TLC in November 2010 Geocoding and Analysis occurred at The City University of New York High Performance Computing Facility Work is challenging due to data size. Data size allows tremendous opportunity in terms of market analysis Random sampling and other statistical techniques applied as needed.

5. NYC Transportation NYC is an interesting transportation market Multilayered transport system: Subway Bus Commuter Rail Private Automobile Taxi Limo/Black Car/Van/Paratransit Ferry Walking Bicycle Freight – truck, rail & maritime High Levels of Road Pricing: 25% of U.S. Tolls collected on NYC Routes

8. Fraud in Taxi Trips GPS Sources and Methods

9. Why do we care about fraud? • Protect Resident Users • Protect Tourist Users • Manage fleet effectively • Maintain public confidence in services provided, good value and safe transport • Maintain public support for existing fleet operators • Promote the region as a good place to do business (perception of overall corruption) • Taxis are an important part of the regional transportation system and need to function well.

10. A Credence Good “Surgeons belong to a class of experts—including computer engineers, car mechanics, taxi-drivers and others—who enjoy a fortunate position in relation to their customers. Not only do they provide a valued service (a cab ride, a repair, an operation), they also tell the customer what service she needs (a long trip, an engine overhaul, a hysterectomy). Their services are known as “credence goods”, because customers take it on faith that the supplier has given them what they need, and no more.” from “Sawbones, cowboys and cheats” The Economist April 12, 2006

11. Fraud Process • Fraud has gone on for many years – in many industries. • Fraud on both sides – users and taxis • Detection of Fraud • Interception/Policing of Fraud • Prosecution and punishment of fraud • Today’s GPS meter systems have the potential to examine operator behavior and identify high potential fraud transactions.

12. An Example of Potential Fraud One of the authors took a cab to JFK Airport and the medallion cab had this sign posted and the GPS – TPEP fare device off. Sign Text: Welcome A Board Dear Passengers, I really appreciate your cash payments..

13. Can We Identify Fraud in Taxi Trips? • A potential natural experiment. • How could we identify fraud in trips? • 1) Short trips with high fees • 2) Trips taking odd routes* • 3) Trips that have long travel times and short travel distances • 4) Trips with long wheel turning distances and short “Crow Fly” distance. • 5) Surcharge during non-surcharge time

14. Manhattan Taxi Trips • Taxi trips that begin and end south of 59th Street in Manhattan represent about 50% of total taxi trips. • About 80% of all NYC taxi trips begin or end in Manhattan south of 110th Street. • Higher concentrations in business areas. • Higher concentrations in higher income residential areas.

15. Potential Trips with Fraud • What should we be looking for? • In one case, we could look for trips that have odd routes (Item 2). • In the case of New York City, the location of toll bridges and the central business district offers us a natural experiment. • Trips inside of Manhattan south of 59th Street should not normally require the use of a toll bridges – but do some and why?

32. Toll Vs Non-Toll Trips • No difference in the number of passengers • Toll payers trips are much more expensive • Toll user pay toll cost plus a bit • Toll users tip more • Toll users pay less surcharges • Toll user pay much more in terms of total fare • Toll trips are about 3.46 minutes longer • Toll trips are over twice as long on a mileage basis • Toll users pay much more per mile of travel • Toll users travel slower by about 1 mile per hour than non-toll trips

33. How Many Trips Does this Represent? • 3,000,000 Record Sample • 1,638 Toll Manhattan to Manhattan Trips • About 550,000 Total Trips per day • 304 Potential Fraud Trips of Odd Route type per day. • This means 110,948 trips per year • $22.82 price differential for an odd trip • $2,531,830 in annual costs to users for this type of questionable trip.

40. Out of Area Trips • The authors also identified many trips that begin in areas that one would not expect to be served by New York City medallion taxis. • In particular, adjacent areas of New Jersey appear to have many trip origins outside the boundary of New York City – 360,000 trips per year. • Not regulated by New York City – but regulated by New Jersey taxi regulators – not allowed – but apparently happening.

44. Conclusions • GPS Tracking allows us to examine trip behavior and look for unusual patterns. • Some trips with odd trip characteristics appear to be questionable as credence goods. • Based on a mechanized evaluation, the regulatory authority could flag odd trips and look for repetitive behavior in a set of drivers. • Driver and Medallion information would allow us to draw more definitive conclusions. • This may form the root of deeper fraud analysis in the industry.

45. Questions? Jonathan.peters@csi.cuny.edu