A Spatial-Contextual Indoor Trajectory Prediction Approach via Hidden Markov Models

Peng Wang,Jianpei Zhang,Jing Yang,Chakchai So-In

doi:10.1155/2022/6719514

Peng Wang, Jianpei Zhang + Show 2 more

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https://doi.org/10.1155/2022/6719514

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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 customer’s 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.

Highlights

During the past decade, there have been a large amount of researches focusing on trajectory prediction [1, 2]
While most of the researches to date concentrated on outdoor scenario with GPS or GPS-like positioning only, researches show that human beings spend around 87% of times in indoor environments such as shopping malls, office buildings, airports, conference facilities, and private homes [3, 4]; the research of trajectory prediction falls short in another important setting, namely, indoor scenario
We first investigate the effect of similarity parameter δ on the prediction accuracy; we will check the trend of accuracy when varying the number of frequent subtrajectory patterns; we will compare the fusion method with the aforementioned approaches and discuss some important findings about trajectory prediction in indoor scenarios

Summary

Introduction

There have been a large amount of researches focusing on trajectory prediction [1, 2]. While most of the researches to date concentrated on outdoor scenario with GPS or GPS-like positioning only, researches show that human beings spend around 87% of times in indoor environments such as shopping malls, office buildings, airports, conference facilities, and private homes [3, 4]; the research of trajectory prediction falls short in another important setting, namely, indoor scenario. With the prevalent of mobile devices which support Wi-Fienabled connectivity and the increasing number of indoor WiFi-enabled venues, breakthrough in indoor trajectory prediction has been made in recent years. Shopping malls are providing free Wi-Fi connections to attract and retain users. Wi-Fi is almost a must have for shopping malls, and because of the ubiquitous Wi-Fi services, it is becoming easier to track shoppers’ foot-path and physical movements by capturing the WiFi signals emitted by mobile devices and collecting the MAC address while the shoppers move around in the shopping mall

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