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Erschienen in:
OPSitu: A Semantic-Web Based Situation Inference Tool Under Opportunistic Sensing Paradigm Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

Reality Mining: Digging the Impact of Friendship and Location on Crowd Behavior

verfasst von : Yuanfang Chen, Antonio M. Ortiz, Noel Crespi, Lei Shu, Lin Lv

Erschienen in: Mobile and Ubiquitous Systems: Computing, Networking, and Services

Verlag: Springer International Publishing

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Abstract

Crowd behavior of human deserves to be studied since it is common that people are influenced and change their behavior when being in a group. In pervasive computing research, an amount of work has been directed towards discovering human movement patterns based on wireless networks, mainly focusing on movements of individuals. It is surprising that social interaction among individuals in a crowd is largely neglected. Mobile phones offer on-body tracking and they are already deployed on a large scale, allowing the characterization of user behavior through large amounts of wireless information collected by mobile phones. In this paper, we observe and analyze the impact of friendship and location attributes on crowd behavior, using location-based wireless mobility information. This is a cornerstone for predicting crowd behavior, which can be used in a large number of applications such as crowdsourcing-based technology, traffic management, crowd safety, and infrastructure deployment.

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Vorheriges Kapitel Fast and Accurate Wi-Fi Localization in Large-Scale Indoor Venues
Nächstes Kapitel Robust Overlay Routing in Structured, Location Aware Mobile Peer-to-Peer Systems
Fußnoten
1
Crowd behavior is a branch of human dynamics. A large number of individuals are gathered or consider to gather together as some particular groups with some special purposes such as crowdsourcing-based knowledge learning, social contact and event-based gathering.
 
2
For a location-aware online social network, if \(B\) is in the friend list of \(A\), we consider that there is friendship between \(A\) and \(B\), and the relationship is directed.
 
3
An Expectation-Maximization (EM) clustering algorithm is used in this paper. The EM assigns a probability distribution for each track record (instance), which indicates the probability of each instance belonging to each of the clusters. The EM can automatically decide how many clusters to create.
 
4
Even if the dataset is special, it still can be used to show that “the impact of Friendship is existent on crowd behavior”.
 
5
The number of clusters is manually set to \(6\) for clustering; only \(5\) clusters are outputted at last.
 
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Metadaten
Titel
Reality Mining: Digging the Impact of Friendship and Location on Crowd Behavior
verfasst von
Yuanfang Chen
Antonio M. Ortiz
Noel Crespi
Lei Shu
Lin Lv
Copyright-Jahr
2014
Buch
Mobile and Ubiquitous Systems: Computing, Networking, and Services

Print ISBN: 978-3-319-11568-9

Electronic ISBN: 978-3-319-11569-6

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-319-11569-6_12