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d
t
t
y
b
a
c
e
x
x
(b)
Figure 2.5 (a) Raw locations; (b) reconstructed raw trajectories.
(a)
due to the fact that different requirements and semantics arise, some generic
trajectory reconstruction parameters can be:
Temporal gap between trajectories : The maximum allowed time interval
between two consecutive spatio-temporal positions of the same trajectory for
a single moving object (case a in Figure 2.5 a).
Spatial gap between trajectories : The maximum allowed distance in 2D plane
between two consecutive spatio-temporal positions of the same trajectory
(case b in Figure 2.5 a).
Maximum speed : The maximum allowed speed of a moving object, used to
determine noisy spatio-temporal positions (case c in Figure 2.5 a).
Maximum noise duration : The maximum duration of a noisy part of a trajec-
tory so as to consider creating a new trajectory containing this part (case d in
Figure 2.5 a).
Tolerance distance : The maximum distance between two consecutive spatio-
temporal positions of the same object in order for the object to be considered
as stationary (case e in Figure 2.5 a).
Reconstructing Semantic Trajectories
Raw trajectories contain only spatio-temporal positions x,y,t ,which
are insufficient for building meaningful trajectory applications. Therefore,
researchers have proposed to reconstruct trajectories from the low-level col-
lected data (e.g., GPS records, movement tracks) to high-level data abstractions,
thus building semantic trajectories. The idea of semantic trajectories is to encode
meaningful geo-locations/geo-objects (e.g., points of interest such as a shopping
mall, roads) into the raw spatio-temporal tracks; additional semantic annotations
(e.g., trajectory behaviors such as traveling in Paris, walking on Avenue des
Champs-Elysees, taking Metro 3, shopping in a supermarket) are attached to the
semantic trajectories.
Figure 2.6 briefly presents themain procedure of reconstructing such semantic
trajectories from the raw GPS alike mobility records. From the initial GPS
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