Abstract:
With widespread availability of low cost GPS
devices, it is becoming possible to record data about the
moving objects on a large scale. The analysis of moving
objects trajectory data is a critical component in a wide
range of research and decision-making fields. To
analyze the object movement regularities and anomalies,
trajectory clustering plays an important role in
trajectory mining. Trajectory clustering provides new
and helpful information such as Jam detection and
significant location recognition. In this paper, we focus
on vehicle movement trajectory data and analyze to
discover the various significant locations. We propose a
K-means based clustering algorithm to mine the
trajectory data for extracting the important information.
The truck trajectory dataset of Athens is used in the
proposed approach as an illustrative example. The result
of clustering is visualized with the help of Google Maps.
