Research Article
Destination Prediction by Identifying and Clustering Prominent Features from Public Trajectory Datasets
@ARTICLE{10.4108/sis.2.5.e2, author={Li Yang and Andy Yuan Xue and Yuan Li and Rui Zhang}, title={Destination Prediction by Identifying and Clustering Prominent Features from Public Trajectory Datasets}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={2}, number={5}, publisher={ICST}, journal_a={SIS}, year={2015}, month={7}, keywords={Trajectory Mining, Destination Prediction}, doi={10.4108/sis.2.5.e2} }- Li Yang
Andy Yuan Xue
Yuan Li
Rui Zhang
Year: 2015
Destination Prediction by Identifying and Clustering Prominent Features from Public Trajectory Datasets
SIS
ICST
DOI: 10.4108/sis.2.5.e2
Abstract
Destination prediction is an essential task in many location-based services (LBS) such as providing targeted advertisements and route recommendations. Most existing solutions were generative methods that model the problem as a series of probabilistic events that are then used to compute the destination probability using Bayes’ rule. In contrast, we propose a discriminative method that chooses the most prominent features found in a public trajectory dataset, clusters the trajectories into groups based on these features, and performs destination prediction queries accordingly. Our method is more concise and simple than existing methods while achieving better runtime efficiency and prediction accuracy as verified by experimental studies.
Copyright © 2015 A. Y. Xue et al., licensed to ICST. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
