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The VLDB Journal

, 20:695 | Cite as

Unveiling the complexity of human mobility by querying and mining massive trajectory data

  • Fosca Giannotti
  • Mirco Nanni
  • Dino Pedreschi
  • Fabio Pinelli
  • Chiara Renso
  • Salvatore Rinzivillo
  • Roberto Trasarti
Special Issue Paper

Abstract

The technologies of mobile communications pervade our society and wireless networks sense the movement of people, generating large volumes of mobility data, such as mobile phone call records and Global Positioning System (GPS) tracks. In this work, we illustrate the striking analytical power of massive collections of trajectory data in unveiling the complexity of human mobility. We present the results of a large-scale experiment, based on the detailed trajectories of tens of thousands private cars with on-board GPS receivers, tracked during weeks of ordinary mobile activity. We illustrate the knowledge discovery process that, based on these data, addresses some fundamental questions of mobility analysts: what are the frequent patterns of people’s travels? How big attractors and extraordinary events influence mobility? How to predict areas of dense traffic in the near future? How to characterize traffic jams and congestions? We also describe M-Atlas, the querying and mining language and system that makes this analytical process possible, providing the mechanisms to master the complexity of transforming raw GPS tracks into mobility knowledge. M-Atlas is centered onto the concept of a trajectory, and the mobility knowledge discovery process can be specified by M-Atlas queries that realize data transformations, data-driven estimation of the parameters of the mining methods, the quality assessment of the obtained results, the quantitative and visual exploration of the discovered behavioral patterns and models, the composition of mined patterns, models and data with further analyses and mining, and the incremental mining strategies to address scalability.

Keywords

Spatio-temporal data mining Trajectories Mobility patterns Movement analysis 

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Fosca Giannotti
    • 1
    • 3
  • Mirco Nanni
    • 1
  • Dino Pedreschi
    • 2
    • 3
  • Fabio Pinelli
    • 1
  • Chiara Renso
    • 1
  • Salvatore Rinzivillo
    • 1
  • Roberto Trasarti
    • 1
  1. 1.KDD Lab, ISTI-CNRPisaItaly
  2. 2.KDD Lab, University of PisaPisaItaly
  3. 3.CCNR, Northeastern UniversityBostonUSA

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