HarmonyMoves: A Unified Prediction Approach for Moving Object Future Path
Research Abstract
Trajectory prediction plays a critical role on
many location-based services such as proximity-based marketing,
routing services, and traffic management. The vast majority
of existing trajectory prediction techniques utilize the object’s
motion history to predict the future path(s). In addition to, their
assumptions that the objects’ moving with recognized patterns
or know their routes. However, these techniques fail when the
history is unavailable. Also, these techniques fail to predict the
path when the query moving objects lost their ways or moving
with abnormal patterns. This paper introduces a system named
HarmonyMoves to predict the future paths of moving objects on
road networks without relying on their past trajectories. The
system checks the harmony between the query object and other
moving objects, after that if the harmony exists, this means that
there are other objects in space moving like the query object.
Then, a Markov Model is adopted to analyze this set of similar
motion patterns and generate the next potential road segments
of the object with their probabilities. If the harmony does not
exist, HarmonyMoves considers this query object as abnormal
object (object lost the way and needs support to return back known
routes), for this purpose HarmonyMoves employed a new module
to handle this case. A fundamental aspect of HarmonyMoves lies in
achieving a high accurate prediction while performing efficiently
to return query answers.
Research Keywords
Deep Learning, moving objects, predection