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Continuous Trajectory Pattern Mining for Mobility Behaviour Change Detection

Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

With the emergence of ubiquitous movement tracking technologies, developing systems which continuously monitor or even influence the mobility behaviour of individuals in order to increase its sustainability is now possible. Currently, however, most approaches do not move beyond merely describing the status quo of the observed mobility behaviour, and require an expert to assess possible behaviour changes of individual persons. Especially today, automated methods for this assessment are needed, which is why we propose a framework for detecting behavioural anomalies of individual users by continuously mining their movement trajectory data streams. For this, a workflow is presented which integrates data preprocessing, completeness assessment, feature extraction and pattern mining, and anomaly detection. In order to demonstrate its functionality and practical value, we apply our system to a real-world, large-scale trajectory dataset collected from 139 users over 3 months.

Keywords

  • Mobility
  • Trajectory mining
  • Anomaly detection
  • Sustainability
  • Behavior change

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Acknowledgements

This research was supported by the Swiss National Science Foundation (SNF) within NRP 71 “Managing energy consumption” and by the Commission for Technology and Innovation (CTI) within the Swiss Competence Center for Energy Research (SCCER) Mobility.

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Correspondence to David Jonietz .

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Jonietz, D., Bucher, D. (2018). Continuous Trajectory Pattern Mining for Mobility Behaviour Change Detection. In: Kiefer, P., Huang, H., Van de Weghe, N., Raubal, M. (eds) Progress in Location Based Services 2018. LBS 2018. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-71470-7_11

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