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A Problem of Detecting Stops While Tracking Moving Objects Under the Stream Processing Regime

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The tracking of moving objects with the use of GPS/GNSS or other techniques is relied upon in numerous applications, from health monitoring and physical activity support, to social investigations to detection of fraud in transportation. While monitoring movement, a common subtask consists in determining the object’s moving periods, and its immobility periods. In this paper, we isolate the mathematical problem of automatic detection of a stop of tracking objects under the stream processing regime (ideal data processing algorithm regime) in which one is allowed to use only a constant amount of memory, while the stream of GNSS positions of the tracked object increases in size. We propose an approximation scheme of the stop detection problem based on the fuzziness in the approximation of noise level related to the position reported by GNSS. We provide a solving algorithm that determines some upper bounds for the problem’s complexity. We also provide an experimental illustration of the problem at hand.
Rocznik
Tom
Strony
123—132
Opis fizyczny
Bibliogr. 19 poz., rys., tab., wykr.
Twórcy
  • Advanced Information Technologies Department National Institute of Telecommunications, Warsaw, Poland
Bibliografia
  • [1] A. Domin, D. Spruijt-Metz, D. Theisen, Y. Ouzzahra, and C. Vogele, “Smartphone-based Interventions for Physical Activity Promotion: Scoping Review of the Evidence over the Last 10 Years”, JMIR mHealth uHealth, vol. 9, no. 7, pp. 638– 648, 2021 (https://doi.org/10.2196/24308).
  • [2] Y. Ye, Y. Zheng, Y. Chen, J. Feng, and X. Xie, “Mining Individual Life Pattern Based on Location History”, in: Tenth International Conference on Mobile Data Management: Systems, Services and Middleware, Taipei, Taiwan, 2009 (https://doi.org/ 10.1109/MDM.2009.11).
  • [3] S.E. Wiehe et al., “Using GPS-enabled Cell Phones to Track the Travel Patterns of Adolescents”, International Journal of Health Geographics, vol. 7, art. no. 22, 2008 (https://doi.org/10.1186/1476-072X-7-22).
  • [4] S.S. Dukare, D.A. Patil, and K. Rane, “Vehicle Tracking, Monitoring and Alerting Systems: A Review”, International Journal of Computer Applications, vol. 119, no. 10, pp. 39– 43, 2015 (https://doi.org/10.5120/21107-3835).
  • [5] Traccar: GPS Tracking Software - Free and Open Source System [Online]. Available: https://www.traccar.org.
  • [6] P. Deshmukh, A. Bhajibhakre, S. Gambhire, A. Channe, and N. Deshpande, “Survey of Geofencing Algorithms”, Internation-al Journal of Computer Science Engineering Techniques, vol. 3 no. 2, 2018 (http://www.ijcsejournal.org/volume3/issue2/IJCSE-V3I2P1.pdf).
  • [7] Y. Ge, H. Xiong, C. Liu, and Z. Zhou, “A Taxi Driving Fraud Detection System”, in: 2011 IEEE 11th International Conference on Data Mining, Vancouver, Canada, pp. 181– 190, 2011 (https://doi.org/10.1109/ICDM.2011.18).
  • [8] J.B. Oliva, “Anomaly Detection and Modeling of Trajectories”, M.Sc. Thesis, Carnegie Mellon University, Pittsburgh, USA, 2012 (https: //apps.dtic.mil/sti/citations/ADA566110).
  • [9] Y. Bu, L. Chen, A.W. Fu, and D. Liu, “Efficient Anomaly Monitoring over Moving Object Trajectory Streams” in: Proc. of 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, 2009 (https://doi.org/ 10.1145/1557019.1557043).
  • [10] L.H. Tran, Q.V.H. Nguyen, N.H. Do, and Y. Zhixian, “Robust and Hierarchical Stop Discovery in Sparse and Diverse Trajectories”, Infoscience. EPFL. Technical Reports, 2011, https://infoscience.epfl.ch/record/175473.
  • [11] L. Killars, B. Schouten, and O. Mussmann, “Stop and Go Detection in GPS-position Data – Discussion Paper”, CBS. Discussion Paper, Netherlands, 2020 https://www.researchgate.net/publicati on/338402776_Stop_and_Go_detection_in_GPS-position_data_-_Discussion_Paper.
  • [12] G. Cich, L. Knapen, T. Bellemans, D. Janssens, and G. Wets,“TRIP/STOP Detection in GPS Traces to Feed Prompted RecallSurvey”, Procedia Computer Science, vol. 52, pp. 262 –269, 2015 (https://doi.org/10.1016/j.procs.2015.05.074).
  • [13] H. Safi, B. Asemi, M. Mesbah, and L. Ferreira, “A Trip Detection Method for Smartphone-assisted Travel Data Collection”, in: Proceedings of the Transportation Research Board (TRB) 95th Annual Meeting, Washington, USA, pp. 1– 18, 2016 https://eprints.qut.edu.au/125568/.
  • [14] L. Gong, T. Yamamoto, and T. Morikawa, “Identification of Activity Stop Locations in GPS Trajectories by DBSCAN-TE Method Combined with Support Vector Machines”, Transportation Research Procedia, vol. 32, no. 3, pp. 146– 154, 2018 (https://doi.org/ 10.1016/j.trpro.2018.10.028).
  • [15] R. Montoliu and D. Gatica-Perez, “Discovering Human Places of Interest from Multimodal Mobile Phone Data”, in: Proc. of the 9th International Conference on Mobile and Ubiquitous Multimedia, Limassol, Cyprus, 2010 (https://doi.org/ 10. 1145/ 1899475.1899487).
  • [16] J.H. Kang, H. Jong, W. Welbourne, B. Stewart, and G. Borriello, “Extracting Places from Traces of Locations”, in: Proc. of 2nd ACM Intl Workshop on Wireless Mobile Applications and Services on WLAN Hotspots, Philadelphia, USA, pp. 110– 118, 2004 (https://doi.org/10.1145/1024733.1024748).
  • [17] C.E. Leiserson, R.L. Rivest, and C. Stein, Introduction to Algorithms, 2nd Edition, MIT Press, London, UK, 2001 (ISBN: 9780262032933 ).
  • [18] T.M.T. Do and D. Gatica-Perez, “The Places of Our Lives: Visiting Patterns and Automatic Labeling from Longitudinal Smartphone Data”, IEEE Transactions on Mobile Computing, vol. 13, no. 3, pp. 638–648, 2014 (https://doi.org/10.1109/TMC.2013.19).
  • [19] The Open Street Map service, [Online]. Available: https://www.op enstreetmap.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0eb9d733-a5cc-4491-8b65-f3d7e727fedd
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