Trajectory Pattern Mining
published: Aug. 14, 2007, recorded: August 2007, views: 8941
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The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) is leading to the collection of large spatio-temporal datasets and to the opportunity of discovering usable knowledge about movement behaviour, which fosters novel applications and services. In this paper, we move towards this direction and develop an extension of the sequential pattern mining paradigm that analyzes the trajectories of moving objects. We introduce trajectory patterns as concise descriptions of frequent behaviours, in terms of both space (i.e., the regions of space visited during movements) and time (i.e., the duration of movements). In this setting, we provide a general formal statement of the novel mining problem and then study several different instantiations of different complexity. The various approaches are then empirically evaluated over real data and synthetic benchmarks, comparing their strengths and weaknesses.
Download slides: kdd07_nanni_tpm_01.pdf (1.7 MB)
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Reviews and comments:
FYI: by 'continuity issues' he does not mean discontinuities, but rather approximate matching of continuous quantities rather than discrete locations. Their solution? Discretize into (clustered) areas & do frequent set mining.
Looking for a dataset? The "Metro" bus system, Seattle, USA, provides GPS coordinates on all vehicles in real time. And they are labeled by their current route number, for ground truth to test your algorithms. Plus the bus schedules provide ground truth.
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