| Summary: | 碩士 === 國立清華大學 === 資訊工程學系 === 104 === Similarity measurement is an important problem in trajectory analysis because
it serves as a foundation for many applications, such as trajectory search, cluster-
ing, and classication. Previously, most methods treat a trajectory as a sequence
of points, and use point-to-point matching methods to measure the similarity of
trajectories. In this thesis, we model a trajectory as a sequence of moving rect-
angles along the time axis. Each moving rectangle creates an oblique rectangular
column, aka a cuboid, in the three dimensional space spanned by the x-y axes and
the time domain. The volume of the intersection between the cuboids formed from
two sequence of moving rectangles is used as the similarity measurement between
two trajectories. We developed an effective algorithm, called Moving Rectangle In-
tersection Detection (MRID), to calculate the intersections. MRID runs linear time
in terms of number of trajectory points, and can be integrated with trajectory com-
pression algorithms to achieve even faster execution time. Experiments that use real
GPS data show that MRID has better accuracy and performance than the Longest
Common Subsequence (LCSS) method, which is a representative algorithm in the
point based methods.
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