NDLI: Trajectory pattern change analysis in campus WiFi networks

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Trajectory pattern change analysis in campus WiFi networks

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Trajectory pattern change analysis in campus WiFi networks

Content Provider	ACM Digital Library
Author	Qiang, Siwei Jin, Yaohui Chen, Xiaming Gong, Wenjuan
Abstract	Mobile devices equipped with geo-location modules facilitate researches on the nature of human mobility. While valuable contributions have been made to discover trajectory patterns from movement history, there are still not a comparative amount of works specialized in analyzing how trajectory patterns change over time. In this paper, we facilitate this problem in a systematic and quantified manner as identifying potential change points of user trajectory patterns extracted from successive time intervals. Specifically, we present a unified, information-based measure to quantify pattern changes between two intervals, and perform a Bayesian analysis on a sequence of aggregate measures for each individual interval to detect the actual change points. Experimenting on a three-month long dataset in real campus WiFi networks, we show that our approach is effective to identify trajectory pattern changes in practice with a discussion on the impact of internal parameters of proposed model on detection performance. Furthermore, we also inspect external factors influencing user mobility in reality by associating trajectory pattern changes with public events, and there shows an interesting connection between group pattern changes and real-world principles such as the weekday calendar or public events such as the May Day holiday.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781450325318
DOI	10.1145/2534190.2534191
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-11-05
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Trajectory pattern Group phenomena Mobility mining Pattern analysis Change mining
Content Type	Text
Resource Type	Article

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