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Advancements in Mobility Data Analysis

  • Mirco Nanni
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 728)

Abstract

Some recent advancements in the area of Mobility Data Analysis are discussed, a field in which data mining and machine learning methods are applied to infer descriptive patterns and predictive models from digital traces of (human) movement.

Keywords

Mobility Data mining Trajectory data 

References

  1. 1.
    PETRA: Personal transport advisor: an integrated platform of mobility patterns for Smart Cities to enable demand-adaptive transportation systems. http://petraproject.eu/.
  2. 2.
    Andrienko, G., N. Andrienko, S. Rinzivillo, M. Nanni, D. Pedreschi, and F. Giannotti. 2009. Interactive Visual Clustering of Large Collections of Trajectories. VAST: Symposium on Visual Analytics Science and Technology.CrossRefGoogle Scholar
  3. 3.
    Giannotti, Fosca, Mirco Nanni, Dino Pedreschi, Fabio Pinelli, Chiara Renso, Salvatore Rinzivillo, and Roberto Trasarti. 2011. Unveiling the complexity of human mobility by querying and mining massive trajectory data. The VLDB Journal 20 (5): 695–719.CrossRefGoogle Scholar
  4. 4.
    Guidotti, R., M. Nanni, S. Rinzivillo, D. Pedreschi, and F. Giannotti. 2016. Never drive alone: boosting carpooling with network analysis. Information Systems.Google Scholar
  5. 5.
    Rinzivillo, Salvatore, Lorenzo Gabrielli, Mirco Nanni, Luca Pappalardo, Dino Pedreschi, and Fosca Giannotti. 2014. The purpose of motion: learning activities from individual mobility networks. In International Conference on Data Science and Advanced Analytics, DSAA 2014, Shanghai, China, October 30 - November 1, 2014.Google Scholar
  6. 6.
    Trasarti, R., R. Guidotti, A. Monreale, and F. Giannotti. 2015. Myway: Location prediction via mobility profiling. Information Systems.Google Scholar
  7. 7.
    Trasarti, Roberto, Fabio Pinelli, Mirco Nanni, and Fosca Giannotti. 2011. Mining mobility user profiles for car pooling. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD’11, 1190–1198. New York, NY, USA, ACM.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.ISTI-CNRKDDLabPisaItaly

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