Where to go in Brooklyn: NYC Mobility Patterns from Taxi Rides

  • Juan Carlos Garcia
  • Allan Avendaño
  • Carmen Vaca
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 745)

Abstract

Urban centers attractive for local citizens commonly house local cuisine restaurants or commercial areas. Local authorities are interested in discovering pattern to explain why city residents go to different areas of the city at a given time of the day. We explore a massive dataset of taxi rides, 69 million records in New York city, to uncover attractive places for local residents when going to Brooklyn. First, we obtain the origin destination matrix for New York boroughs. Second, we apply a density based clustering algorithm to detect popular drop-off locations. Next, we automatically find the closest venue, using the Foursquare API, to the most popular destination in each cluster. Our methodology let us to uncover popular destinations in urban areas in any city for which taxi rides information is available.

Keywords

Urban mobility Spatio-temporal data Clustering Points of interest 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Juan Carlos Garcia
    • 1
    • 2
  • Allan Avendaño
    • 1
    • 2
  • Carmen Vaca
    • 1
    • 2
  1. 1.Facultad de Ingeniería en Electricidad y ComputaciónEscuela Superior Politcnica del Litoral, ESPOLGuayaquilEcuador
  2. 2.Universidad de GuayaquilGuayaquilEcuador

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