On Predicting the Taxi-Passenger Demand: A Real-Time Approach

  • Luis Moreira-Matias
  • João Gama
  • Michel Ferreira
  • João Mendes-Moreira
  • Luis Damas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8154)


Informed driving is becoming a key feature to increase the sustainability of taxi companies. Some recent works are exploring the data broadcasted by each vehicle to provide live information for decision making. In this paper, we propose a method to employ a learning model based on historical GPS data in a real-time environment. Our goal is to predict the spatiotemporal distribution of the Taxi-Passenger demand in a short time horizon. We did so by using learning concepts originally proposed to a well-known online algorithm: the perceptron [1]. The results were promising: we accomplished a satisfactory performance to output the next prediction using a short amount of resources.


taxi-passenger demand online learning data streams GPS data auto-regressive integrated moving average (ARIMA) perceptron 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Luis Moreira-Matias
    • 1
    • 2
    • 3
  • João Gama
    • 2
    • 4
  • Michel Ferreira
    • 1
    • 5
  • João Mendes-Moreira
    • 2
    • 3
  • Luis Damas
    • 6
  1. 1.Instituto de TelecomunicaçõesPortoPortugal
  2. 2.LIAAD-INESC TECPortoPortugal
  3. 3.Dep. de Engenharia Informática, Faculdade de EngenhariaU. PortoPortoPortugal
  4. 4.Faculdade de EconomiaU. PortoPortoPortugal
  5. 5.Dep. de Ciência de Computadores, Faculdade de CiênciasU. PortoPortoPortugal
  6. 6.Avenida de FrançaGeolink, Lda.PortoPortugal

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