Green Cabs vs. Uber in New York City

Lasse Korsholm Poulsen, Daan Dekkers, Nicolaas Wagenaar, Wesley Snijders, Ben Lewinsky, Raghava Rao Mukkamala, Ravi Vatrapu

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


    This paper reports on the process and outcomes of big data analytics of ride records for Green cabs and Uber in the outer boroughs of New York City (NYC), USA. Uber is a new entrant to the taxi market in NYC and is rapidly eating away market share from the NYC Taxi & Limousine Commission's (NYCTLC) Yellow and Green cabs. The problem investigated revolves around where exactly Green cabs are losing market share to Uber outside Manhattan and what, if any, measures can be taken to preserve market share? Two datasets were included in the analysis including all rides of Green cabs and Uber respectively from April-September 2014 in New York excluding Manhattan and NYC's two airports. Tableau was used as the visual analytics tool, and PostgreSQL in combination with PostGIS was used as the data processing engine. Our findings show that the performance of Green cabs in isolated zip codes differ significantly, and that Uber is growing faster than Green cabs in general and especially in the areas close to Manhattan. We discuss meaningful facts from the analysis, outline actionable insights, list valuable outcomes and mention some of the study limitations.
    Original languageEnglish
    Title of host publicationProceedings of the 2016 IEEE International Congress on Big Data. BigData Congress 2016
    EditorsCalton Pu, Geoffrey Fox, Ernesto Damiani
    Number of pages8
    Place of PublicationLos Alamitos, CA
    Publication date2016
    Article number7584941
    ISBN (Print)9781509026227
    ISBN (Electronic)9781509026227
    Publication statusPublished - 2016
    Event5th IEEE International Congress on Big Data: BigData Congress 2016 - San Francisco, CA, United States
    Duration: 27 Jun 20162 Jul 2016
    Conference number: 5


    Conference5th IEEE International Congress on Big Data
    Country/TerritoryUnited States
    CitySan Francisco, CA
    Internet address


    • Uber
    • Big social data
    • Social set analysis
    • Social business
    • Visual analytics
    • Geo-spatial
    • GIS
    • Taxi
    • Green cabs

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