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An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories

Author

Listed:
  • Francisco Melo Pereira

    (COPELABS, University Lusofona, 1749-024 Lisbon, Portugal
    These authors contributed equally to this work.)

  • Rute C. Sofia

    (Fortiss GmbH—Research Institute of the Free State of Bavaria for Software Intensive Systems and Services, 80805 Munich, Germany
    These authors contributed equally to this work.)

Abstract

This paper provides an analysis of two machine learning algorithms, density-based spatial clustering of applications with noise (DBSCAN) and the local outlier factor (LOF), applied in the detection of outliers in the context of a continuous framework for the detection of points of interest (PoI) . This framework has as input mobile trajectories of users that are continuously fed to the framework in close to real time. Such frameworks are today still in their infancy and highly required in large-scale sensing deployments, e.g., Smart City planning deployments, where individual anonymous trajectories of mobile users can be useful to better develop urban planning. The paper’s contributions are twofold. Firstly, the paper provides the functional design for the overall PoI detection framework. Secondly, the paper analyses the performance of DBSCAN and LOF for outlier detection considering two different datasets, a dense and large dataset with over 170 mobile phone-based trajectories and a smaller and sparser dataset, involving 3 users and 36 trajectories. Results achieved show that LOF exhibits the best performance across the different datasets, thus showing better suitability for outlier detection in the context of frameworks that perform PoI detection in close to real time.

Suggested Citation

  • Francisco Melo Pereira & Rute C. Sofia, 2022. "An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories," Future Internet, MDPI, vol. 15(1), pages 1-19, December.
    • Handle: RePEc:gam:jftint:v:15:y:2022:i:1:p:4-:d:1012472
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    References listed on IDEAS

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    1. Markus Goldstein & Seiichi Uchida, 2016. "A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-31, April.
    2. Akbar Ali & Nasir Ayub & Muhammad Shiraz & Niamat Ullah & Abdullah Gani & Muhammad Ahsan Qureshi, 2021. "Traffic Efficiency Models for Urban Traffic Management Using Mobile Crowd Sensing: A Survey," Sustainability, MDPI, vol. 13(23), pages 1-18, November.
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