Destination prediction is an essential task in a variety of mobile applications. In this paper, we optimize the matrix operation and adapt a semi-lazy framework to improve the prediction accuracy and efficiency of a state-of-the-art approach. To this end, we employ efficient dynamic-programming by devising several data constructs including Efficient Transition Probability and Transition Probabilities with Detours that are capable of pinpointing the minimum amount of computation. We prove that our method achieves one order of cut in both time and space complexity. The experimental results on real-world and synthetic datasets have shown that our solution consistently outperforms its state-of-theart counterparts in terms of both efficiency (approximately over 100 times faster) and accuracy (above 30 % increase).
In addition, REF improved the performance by using a semi-lazy framework to optimize complicated matrix calculation.
Efficient Destination Prediction Based on Route Choices with Transition Matrix Optimization
ArXiv
ArXiv
Computer Science