Open Knowledge International

NYC Ride Sharing Research

  • Ben Wellington
Chris Goldberg

##The problem Ride-sharing services, like Uber and Lyft are those services in which passengers ride in a private vehicle driven by the owner, for a fee or for free. The widespread adoption of ride-sharing services drastically changes how people get from place to place and as a result creates new policy challenges. Ride-sharing and transportation policy is made doubly difficult because private enterprises who own ride-sharing services inherently evade regulation.

The extent to which ride-sharing enterprises are obligated to share their data for the scrutiny and well-being of the public sphere is doubtful. Additionally, there is increased need to provide environmentally friendly, and economically efficient transportation services on the part of governments, as they have historically regulated transportation for safety and environmental reasons.

As technologies evolve and redefine services that were once primarily within government purview there will need to be data that can triangulated in order to make sound policy decisions. Thus, there is greater necessity for governments and society at large to creatively use data to assess the impact of transportation services both old and new in ways impossible during the pre-digital and pre-mobile era.

##The Solution While getting data from private enterprises, will be difficult for the foreseeable future, mobile and public transit data can shed light on transportation services, public and private alike.

Increasingly state and municipal governments are releasing data that show with granularity how transportations services are affecting our everyday lives. Open datasets that tell us the why, when, what, where of how people use public and private transport can make for more informed policy debates and decisions. Much debate has arisen over the societal impact of ride-sharing services such as Uber and Lyft. An intuitive argument is that ride-sharing, puts more cars on the road and thus, all things being equal, contributes to greater traffic.

Ben Wellington, computer scientist and visiting professor of city and regional planning at the Pratt Institute used open datasets of cab ride speeds as a proxy for measuring New York City traffic both before and after the monumental increase in ride-sharing services starting in 2014.

This simple temporal examination of taxi rides revealed that taxi speeds were increasing between 2011 to 2013 when ride-sharing services were coming into popular use and that the marked decrease in taxi speeds started well before substantial speed limit reductions in November of 2014. Moreover we find that the rate of decrease in taxi ride speeds lessens considerably in 2014 even though the use of ride-sharing services has approximately doubled.

Illustrating this study is not meant to make any claim as to ride-sharing’s impact, but rather to show how open data can test basic assumptions and reveal a level complexity that opposing sides of any policy debate would rather not subject themselves to.

Data Used: 2014-2015 Taxi data via, 2008-2013 Taxi data via

Credit: TLC Open Data, FOIL

Go to website  
  • Improve this page Edit on Github Help and instructions