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