In this paper, we solve the problem of predicting the next locations of the moving objects with a historical dataset of trajectories. We present a Next Location Predictor with Markov Modeling (NLPMM) which has the following advantages: (1) it considers both individual and collective movement patterns in making prediction, (2) it is effective even when the trajectory data is sparse, (3) it considers the time factor and builds models that are suited to different time periods. We have conducted extensive experiments in a real dataset, and the results demonstrate the superiority of NLPMM over existing methods. © 2014 Springer International Publishing.
CITATION STYLE
Chen, M., Liu, Y., & Yu, X. (2014). NLPMM: A next location predictor with Markov modeling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8444 LNAI, pp. 186–197). Springer Verlag. https://doi.org/10.1007/978-3-319-06605-9_16
Mendeley helps you to discover research relevant for your work.