Mobile and Ubiquitous Systems: Computing, Networking, and Services. 10th International Conference, MOBIQUITOUS 2013, Tokyo, Japan, December 2-4, 2013, Revised Selected Papers

Research Article

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

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  • @INPROCEEDINGS{10.1007/978-3-319-11569-6_12,
        author={Yuanfang Chen and Antonio Ortiz and Noel Crespi and Lei Shu and Lin Lv},
        title={Reality Mining: Digging the Impact of Friendship and Location on Crowd Behavior},
        proceedings={Mobile and Ubiquitous Systems: Computing, Networking, and Services. 10th International Conference, MOBIQUITOUS 2013, Tokyo, Japan, December 2-4, 2013,  Revised Selected Papers},
        proceedings_a={MOBIQUITOUS},
        year={2014},
        month={12},
        keywords={Crowd behavior Mobile devices Wearable computing Complex social networks},
        doi={10.1007/978-3-319-11569-6_12}
    }
    
  • Yuanfang Chen
    Antonio Ortiz
    Noel Crespi
    Lei Shu
    Lin Lv
    Year: 2014
    Reality Mining: Digging the Impact of Friendship and Location on Crowd Behavior
    MOBIQUITOUS
    Springer
    DOI: 10.1007/978-3-319-11569-6_12
Yuanfang Chen1,*, Antonio Ortiz1,*, Noel Crespi1,*, Lei Shu2,*, Lin Lv3,*
  • 1: Télécom SudParis
  • 2: Guangdong University of Petrochemical Technology
  • 3: Dalian University of Technology
*Contact email: yuanfang.chen@telecom-sudparis.eu, antonio.ortiz_torres@telecom-sudparis.eu, noel.crespi@telecom-sudparis.eu, lei.shu@lab.gdupt.edu.cn, lvlin_george@mail.dlut.edu.cn

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.