Mining Dense Regions from Vehicular Mobility in Streaming Setting

  • Corrado Loglisci
  • Donato Malerba
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)


The detection of congested areas can play an important role in the development of systems of traffic management. Usually, the problem is investigated under two main perspectives which concern the representation of space and the shape of the dense regions respectively. However, the adoption of movement tracking technologies enables the generation of mobility data in a streaming style, which adds an aspect of complexity not yet addressed in the literature. We propose a computational solution to mine dense regions in the urban space from mobility data streams. Our proposal adopts a stream data mining strategy which enables the detection of two types of dense regions, one based on spatial closeness, the other one based on temporal proximity. We prove the viability of the approach on vehicular data streams in the urban space.


Dense Region Urban Space Dense Road Temporal Proximity Entrepreneurship Ecosystem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Corrado Loglisci
    • 1
  • Donato Malerba
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli Studi di Bari “Aldo Moro”BariItaly

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