OpenStreetCab: Harnessing Mobility Data for Transparency in Urban Transport
CDS fellow Tassos Noulas explains how his app helps you save $$$ by comparing taxi prices from Uber, Lyft, & yellow cabs
Ride-sharing apps like Uber and Lyft initially promised to save us big bucks in cities with notoriously high cab fares.
But with the introduction of surge pricing and the lack of strong data regulations in the taxi industry, it’s unclear whether these apps are making a meaningful difference.
How can you be sure that you’re getting the best deal possible?
Enter Open Street Cab, an app created by CDS fellow Tassos Noulas in collaboration with researchers in the UK and Belgium.
At last week’s Moore-Sloan Research Lunch Seminar, Noulas explained how their app — which operates on a Python server —uses Uber and Lyft APIs and yellow taxi data to compare the predicted prices for a given trip, and then advises the user on which option is cheapest.
Users submit their trip queries into the app, which consults the Uber and Lyft APIs in real time, and then captures the estimated price of the trip from each service.
To predict the price of yellow cabs, the app draws from previous data sets released by the government as well as real-time traffic information, and then simulates the trip within the context of that data to provide a price estimate.
The average savings per trip that riders stand to gain using their app is $7.26.
Operating in New York, Chicago, London, and Manchester, Open Street Cab has been around for roughly three years now, gained over 10,000 active users.
The app is now exploring the possibility of predicting surge pricing in NYC based on the human mobility data that they have collected, as well as starting a sister app in Cambridge, UK — but for taxi drivers.
The new app, Noulas explained, will visualize taxi cab data in real time, and show drivers where there are taxi shortages in the city.
Learn more about Open Street Cab here.
by Cherrie Kwok