Open AccessA geographical location prediction method based on continuous time series Markov model
Author(s) -
Yongping Du,
Yanlei Qiao,
Dongyue Zhao,
Wenyang Guo
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0207063
Subject(s) - hidden markov model , computer science , geographic coordinate system , markov chain , time series , markov model , trajectory , series (stratigraphy) , global positioning system , mixture model , data mining , algorithm , artificial intelligence , machine learning , geography , geodesy , paleontology , telecommunications , physics , astronomy , biology
Trajectory data uploaded by mobile devices is growing quickly. It represents the movement of an individual or a device based on the longitude and latitude coordinates collected by GPS. The location based service has a broad application prospect in the real world. As the traditional location prediction models which are based on the discrete state sequence cannot predict the locations in real time, we propose a Continuous Time Series Markov Model (CTS-MM) to solve this problem. The method takes the Gaussian Mixed Model (GMM) to simulate the posterior probability of a location in the continuous time series. The probability calculation method and state transition model of the Hidden Markov Model (HMM) are improved to get the precise location prediction. The experimental results on GeoLife data show that CTS-MM performs better for location prediction in exact minute than traditional location prediction models.