An Efficient Destination Prediction Approach Based on Future Trajectory Prediction and Transition Matrix Optimization

摘要

Destination prediction is an essential task in various mobile applications and up to now many methods have been proposed. However, existing methods usually suffer from the problems of heavy computational burden, data sparsity, and low coverage. Therefore, a novel approach named DestPD is proposed to tackle the aforementioned problems. Differing from an earlier approach that only considers the starting and current location of a partial trip, DestPD first determines the most likely future location and then predicts the destination. It comprises two phases, the offline training and the online prediction. During the offline training, transition probabilities between two locations are obtained via Markov transition matrix multiplication. In order to improve the efficiency of matrix multiplication, we propose two data constructs, Efficient Transition Probability (ETP) and Transition Probabilities with Detours (TPD). They are capable of pinpointing the minimum amount of needed computation. During the online prediction, we design Obligatory Update Point (OUP) and Transition Affected Area (TAA) to accelerate the frequent update of ETP and TPD for recomputing the transition probabilities. Moreover, a new future trajectory prediction approach is devised. It captures the most recent movement based on a query trajectory. It consists of two components: similarity finding through Best Path Notation (BPN) and best node selection. Our novel BPN similarity finding scheme keeps track of the nodes that induces inefficiency and then finds similarity fast based on these nodes. It is particularly suitable for trajectories with overlapping segments. Finally, the destination is predicted by combining transition probabilities and the most probable future location through Bayesian reasoning. The DestPD method is proved to achieve one order of cut in both time and space complexity. Furthermore, the experimental results on real-world and synthetic datasets have shown that DestPD consistently surpasses the state-of-the-art methods in terms of both efficiency (approximately over 100 times faster) and accuracy.