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week, or day. This allows the user to uncover and study movement pat-
terns related to temporal cycles, for example, find typical routes taken in the
morning and see their differences from the routes taken in the evening.
2. Transformations with respect to the individual lifelines of trajectories: Tra-
jectories can be shifted in time to a common start time or a common end
time. This facilitates the comparison of dynamic properties of the trajectories
(particularly, spatially similar trajectories), for example, the dynamics of the
speed. Aligning both the start and end times supports comparison of internal
dynamics in trajectories irrespective of the average movement speed.
An example of time-transformed trajectories is shown in Figure
8.2
e. The
STC shows the route-based clusters of car trajectories ending in the northwest.
The times in the trajectories have been transformed so that all trajectories have
a common end time. This allows us to see that, although the routes within
each cluster are similar, the dynamics of the movement may differ greatly. The
speeds can be judged from the slopes of the lines. Fast movement is manifested
by slightly inclined lines (which means more distance traveled in less time);
steep lines signify slow movement. Vertical line segments mean staying in the
same place. In the STC in Figure
8.2
we can very clearly observe the movement
dynamics in the red cluster: the cars moved slowly while being in the city center
but couldmove quickly after reaching the diagonal motorway. The orange cluster
is divided in two parts. One part consists of nearly straight, slightly tilted lines
indicating uniformly high speed along the whole route. The other part consists
of trajectories with steep segments at the beginning. This means that there were
times when the movement in the eastern part of the northern motorway was
obstructed and the cars could not reach high speed. We can interactively select
the trajectories with the steep segments and find out the times of the obstructed
traffic: from about 06:00 A.M. till 01:00 P.M.; the most difficult situation was
after 10:30 A.M. Making such observations could hardly be possible with the
trajectories positioned in the STC according to their original times.
8.3 Looking inside Trajectories: Attributes, Events, and Patterns
The methods described in the previous section deal with trajectories as wholes,
that is, treat them as atomic objects. Here we consider methods operating on
the level of points and segments of trajectories. They visualize and analyze the
variation of movement characteristics (speed, direction, etc.) and other dynamic
attributes associated with trajectory positions or segments. The most obvious
way to visualize position-related attributes is by dividing the lines or bands
representing trajectories on a map or in a 3D display into segments and varying
the appearance of these segments. Attribute values are usually represented by
colouring or shading of the segments.
