Mobile Computing, Applications, and Services. 9th International Conference, MobiCASE 2018, Osaka, Japan, February 28 – March 2, 2018, Proceedings

Research Article

User Attribute Classification Method Based on Trajectory Patterns with Active Scanning Devices

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  • @INPROCEEDINGS{10.1007/978-3-319-90740-6_24,
        author={Kenji Takayanagi and Kazuya Murao and Masahiro Mochizuki and Nobuhiko Nishio},
        title={User Attribute Classification Method Based on Trajectory Patterns with Active Scanning Devices},
        proceedings={Mobile Computing, Applications, and Services. 9th International Conference, MobiCASE 2018,  Osaka, Japan, February 28 -- March 2, 2018, Proceedings},
        proceedings_a={MOBICASE},
        year={2018},
        month={5},
        keywords={People flow analysis Attribute estimation Spatiotemporal data Probe request frame},
        doi={10.1007/978-3-319-90740-6_24}
    }
    
  • Kenji Takayanagi
    Kazuya Murao
    Masahiro Mochizuki
    Nobuhiko Nishio
    Year: 2018
    User Attribute Classification Method Based on Trajectory Patterns with Active Scanning Devices
    MOBICASE
    Springer
    DOI: 10.1007/978-3-319-90740-6_24
Kenji Takayanagi1,*, Kazuya Murao1,*, Masahiro Mochizuki1,*, Nobuhiko Nishio1,*
  • 1: Ritsumeikan University
*Contact email: gibson@ubi.cs.ritsumei.ac.jp, murao@cs.ritsumei.ac.jp, moma@ubi.cs.ritsumei.ac.jp, nishio@cs.ritsumei.ac.jp

Abstract

Technologies for grasping the distribution and flow of people are required for urban planning, traffic planning, evacuation, rescue activities in case of disaster, and marketing. In order to grasp what kind of attribute the distribution and flow of people are formed, this paper proposes a method that estimates the attributes of users. As a method of estimating user attributes, we utilize probe request frame of Wi-Fi that smartphones are emitting. Probe request frame includes MAC address, enabling us to acquire the movement trajectory of a user by tracking the MAC address. By using the feature values obtained from the movement trajectory of the user, users are roughly classified into several types. In this paper, we focus on the user attribute estimation in underground city comprising of stations, shops, restaurants and so on. Through the practical experiment at Osaka underground city, we confirmed that the proposed method can classify the users into commuter or not by using the intervals between probe request frames.