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Geometry of interest (GOI): spatio-temporal destination extraction and partitioning in GPS trajectory data

  • Seyed Morteza Mousavi
  • Aaron Harwood
  • Shanika Karunasekera
  • Mojtaba Maghrebi
Original Research

Abstract

Nowadays large amounts of GPS trajectory data is continuously collected by GPS-enabled devices such as vehicles navigation systems and mobile phones. GPS trajectory data is useful for applications such as traffic management, location forecasting, and itinerary planning. Such applications often need to extract the time-stamped sequence of visited locations (SVLs) of the mobile objects. The nearest neighbor query (NNQ) is the most applied method for labeling the visited locations based on the IDs of the points of interests (POIs) in the process of SVL generation. NNQ in some scenarios is not accurate enough. To improve the quality of the extracted SVLs, instead of using NNQ, we label the visited locations as the IDs of the POIs which geometrically intersect with the GPS observations. Intersection operator requires the accurate geometries of the POIs which we refer to them as the geometries of interest (GOIs). In some application domains (e.g. movement trajectories of animals), adequate information about the POIs and their geometries may not be available a priori, or they may not be publicly accessible and, therefore, they need to be derived from GPS trajectory data. In this paper we propose a novel method for estimating the GOIs, which consists of three phases: (1) extracting the geometries of the stay regions; (2) constructing the geometry of destination regions based on the extracted stay regions; and (3) constructing the GOIs based on the geometries of the destination regions. Using the geometric similarity to known GOIs as the major evaluation criterion, the experiments we performed using long-term GPS trajectory data show that our method outperforms the state-of-the-art.

Keywords

Trajectory data Spatio-temporal partitioning Geometry of interest Time-value Time-weighted centroid Destination extraction GOI POI 

Notes

Acknowledgment

We would like to acknowledge the financial support that we received from Data61 during this research project.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Seyed Morteza Mousavi
    • 1
  • Aaron Harwood
    • 2
  • Shanika Karunasekera
    • 2
  • Mojtaba Maghrebi
    • 3
  1. 1.Data61, Department of Computing and Information SystemsThe University of MelbourneMelbourneAustralia
  2. 2.Department of Computing and Information SystemsThe University of MelbourneThe University of MelbourneAustralia
  3. 3.Department of Civil EngineeringFerdowsi University of MashhadMashhadIran

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