A User-Centered Location Model
Personal and Ubiquitous Computing
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Location prediction algorithms for mobile wireless systems
Wireless internet handbook
Learning and inferring transportation routines
Artificial Intelligence
Graph abstraction in real-time heuristic search
Journal of Artificial Intelligence Research
Inferring complex agent motions from partial trajectory observations
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Hi-index | 0.00 |
Companies and organizations that track moving objects are interested in predicting the intended destination of these moving objects. We develop a formal model for destination prediction problems where the agent (Predictor) predicting a destination may not know anything about the route planning mechanism used by another agent (Target) nor does the agent have historical information about the target's past movements nor do the observations about the agent have to be complete (there may be gaps when the target was not seen). We develop axioms that any destination probability function should satisfy and then provide a broad family of such functions guaranteed to satisfy the axioms. We experimentally compare our work with an existing method for destination prediction using Hidden Semi-Markov Models (HSMMs). We found our algorithms to be faster than the existing method. Considering prediction accuracy we found that, when the Predictor knows the route planning algorithm the target is using, the HSMM method is better, but without this assumption our algorithm is better.