A Spatial-Contextual Indoor Trajectory Prediction Approach via Hidden Markov Models
Open Access
- 11 January 2022
- journal article
- research article
- Published by Hindawi Limited in Wireless Communications and Mobile Computing
- Vol. 2022, 1-13
- https://doi.org/10.1155/2022/6719514
Abstract
Unlike outdoor trajectory prediction that has been studied many years, predicting the movement of a large number of users in indoor space like shopping mall has just been a hot and challenging issue due to the ubiquitous emerging of mobile devices and free Wi-Fi services in shopping centers in recent years. Aimed at solving the indoor trajectory prediction problem, in this paper, a hybrid method based on Hidden Markov approach is proposed. The proposed approach clusters Wi-Fi access points according to their similarities first; then, a frequent subtrajectory based HMM which captures the moving patterns of users has been investigated. In addition, we assume that a customers visiting history has certain patterns; thus, we integrate trajectory prediction with shop category prediction into a unified framework which further improves the predicting ability. Comprehensive performance evaluation using a large-scale real dataset collected between September 2012 and October 2013 from over 120,000 anonymized, opt-in consumers in a large shopping center in Sydney was conducted; the experimental results show that the proposed method outperforms the traditional HMM and perform well enough to be usable in practice.Keywords
Funding Information
- Heilongjiang Postdoctoral Science Foundation (LBH-Z14071, HEUCFM180601, QC2016083, F2015030, 61402126, 61370083, 61672179)
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