Green Cabs vs. Uber in New York City

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

    Publikation: Kapitel i bog/rapport/konferenceprocesKonferencebidrag i proceedingsForskningpeer review

    Resumé

    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.
    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.
    SprogEngelsk
    TitelProceedings of the 2016 IEEE International Congress on Big Data. BigData Congress 2016
    RedaktørerCalton Pu, Feoffrey Fox, Ernesto Damiani
    Udgivelses stedLos Alamitos, CA
    ForlagIEEE
    Dato2016
    Sider222–229
    ISBN (Trykt)9781509026227
    DOI
    StatusUdgivet - 2016
    Begivenhed5th IEEE International Congress on Big Data: BigData Congress 2016 - San Francisco, CA, USA
    Varighed: 27 jun. 20162 jul. 2016
    Konferencens nummer: 5
    http://www.ieeebigdata.org/2016/

    Konference

    Konference5th IEEE International Congress on Big Data
    Nummer5
    LandUSA
    BySan Francisco, CA
    Periode27/06/201602/07/2016
    Internetadresse

    Bibliografisk note

    CBS Bibliotek har ikke adgang til materialet

    Emneord

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

    Citer dette

    Korsholm Poulsen, L., Dekkers, D., Wagenaar, N., Snijders, W., Lewinsky, B., Mukkamala, R. R., & Vatrapu, R. (2016). Green Cabs vs. Uber in New York City. I C. Pu, F. Fox, & E. Damiani (red.), Proceedings of the 2016 IEEE International Congress on Big Data. BigData Congress 2016 (s. 222–229). Los Alamitos, CA: IEEE. DOI: 10.1109/BigDataCongress.2016.35
    Korsholm Poulsen, Lasse ; Dekkers, Daan ; Wagenaar, Nicolaas ; Snijders, Wesley ; Lewinsky, Ben ; Mukkamala, Raghava Rao ; Vatrapu, Ravi. / Green Cabs vs. Uber in New York City. Proceedings of the 2016 IEEE International Congress on Big Data. BigData Congress 2016. red. / Calton Pu ; Feoffrey Fox ; Ernesto Damiani. Los Alamitos, CA : IEEE, 2016. s. 222–229
    @inproceedings{496fe58f187e48d6a00a546ee2ee120f,
    title = "Green Cabs vs. Uber in New York City",
    abstract = "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.",
    keywords = "Uber, Big social data, Social set analysis, Social business, Visual analytics, Geo-spatial, GIS, Taxi, Green cabs, Uber, Big social data, Social set analysis, Social business, Visual analytics, Geo-spatial, GIS, Taxi, Green cabs",
    author = "{Korsholm Poulsen}, Lasse and Daan Dekkers and Nicolaas Wagenaar and Wesley Snijders and Ben Lewinsky and Mukkamala, {Raghava Rao} and Ravi Vatrapu",
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    Korsholm Poulsen, L, Dekkers, D, Wagenaar, N, Snijders, W, Lewinsky, B, Mukkamala, RR & Vatrapu, R 2016, Green Cabs vs. Uber in New York City. i C Pu, F Fox & E Damiani (red), Proceedings of the 2016 IEEE International Congress on Big Data. BigData Congress 2016. IEEE, Los Alamitos, CA, s. 222–229, 5th IEEE International Congress on Big Data, San Francisco, CA, USA, 27/06/2016. DOI: 10.1109/BigDataCongress.2016.35

    Green Cabs vs. Uber in New York City. / Korsholm Poulsen, Lasse; Dekkers, Daan; Wagenaar, Nicolaas; Snijders, Wesley; Lewinsky, Ben; Mukkamala, Raghava Rao; Vatrapu, Ravi.

    Proceedings of the 2016 IEEE International Congress on Big Data. BigData Congress 2016. red. / Calton Pu; Feoffrey Fox; Ernesto Damiani. Los Alamitos, CA : IEEE, 2016. s. 222–229.

    Publikation: Kapitel i bog/rapport/konferenceprocesKonferencebidrag i proceedingsForskningpeer review

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    AU - Mukkamala,Raghava Rao

    AU - Vatrapu,Ravi

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    PY - 2016

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    N2 - 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.

    AB - 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.

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    Korsholm Poulsen L, Dekkers D, Wagenaar N, Snijders W, Lewinsky B, Mukkamala RR et al. Green Cabs vs. Uber in New York City. I Pu C, Fox F, Damiani E, red., Proceedings of the 2016 IEEE International Congress on Big Data. BigData Congress 2016. Los Alamitos, CA: IEEE. 2016. s. 222–229. Tilgængelig fra, DOI: 10.1109/BigDataCongress.2016.35