Unmet demand team. From left to right. TLC Staff: Ben Kurland, Chair Meera Joshi, Jeff Garber. Student team: Alexey Kalinin, Anita Ahmed, Pooneh Famili, Xin Tang, Ziman Zhou. Faculty advisors: Huy Vo, Kaan Ozbay.

The New York City Taxi and Limousine Commission (TLC) features their work with CUSP graduate students during the Urban Science Intensive (USI) capstone program. 

When you get in a yellow or green taxi in New York City, the cab is outfitted with equipment that automatically records the time and location of every pickup and drop-off. Since 2009, the Taxi and Limousine Commission has used this information extensively to help create data-driven policies, find items forgotten in taxicabs, and investigate passenger complaints.

Originally, the public could request a redacted version of this information through the Freedom of Information Law. Due to the volume of requests the TLC received, we began to proactively publish the datasets online on a 6-month cycle. In order to protect passenger privacy, the TLC removes vehicle and driver identifiers and aggregates the pickup and drop-off locations to larger taxi zones. This data is a treasure trove of information for journalists, startups, urban planners, and academics — and the TLC loves to see it being used in novel ways to improve the city.

One notable user of taxi data is New York University’s Center for Urban Science and Progress (CUSP). CUSP is a data science school focusing on urban informatics, a field that uses data to better understand how cities work. This data can range from the quality of the air you breathe to the time you spend commuting. The school allows city agencies and industry partners to sponsor capstone projects where students use their new skills to analyze or solve problems using a data-driven approach. Over the last 4 months, the TLC mentored two teams of data science students. The teams were made of 4 to 5 students applying advanced data science techniques on TLC data to answer important and complex city planning questions. As mentors, TLC staff provided the teams with historical taxi trip data and helped to define the project scopes.