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International Conference on Mobile Computing, Applications, and Services

MobiCASE 2018: Mobile Computing, Applications, and Services pp 282–288Cite as

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

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Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 240)

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.

Keywords

  • People flow analysis
  • Attribute estimation
  • Spatiotemporal data
  • Probe request frame

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  • DOI: 10.1007/978-3-319-90740-6_24
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Notes

  1. 1.

    https://blog.google/products/search/know-you-go-google/.

References

  1. Fukuzaki, Y., Mochizuki, M., Murao, K., Nishio, N.: Statistical analysis of actual number of pedestrians for Wi-Fi packet-based pedestrian flow sensing. In: Proceedings of the 1st International Workshop on Smart Cities, pp. 1519–1526 (2015)

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  2. Robyns, P., Bonné, B., Quax, P., Lamotte, W.: Non-cooperative 802.11 MAC layer fingerprinting and tracking of mobile devices. Secur. Commun. Netw. 2017, 1–26 (2017)

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Author information

Authors and Affiliations

  1. College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga, 525-8577, Japan

    Kenji Takayanagi, Kazuya Murao & Nobuhiko Nishio

  2. Research Organization of Science and Technology, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga, 525-8577, Japan

    Masahiro Mochizuki

Authors
  1. Kenji Takayanagi
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  2. Kazuya Murao
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  3. Masahiro Mochizuki
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  4. Nobuhiko Nishio
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Corresponding author

Correspondence to Kenji Takayanagi .

Editor information

Editors and Affiliations

  1. Ritsumeikan University, Kusatsu-shi, Shiga, Japan

    Kazuya Murao

  2. Toyohashi University of Technology, Toyohashi-shi, Aichi, Japan

    Ren Ohmura

  3. Kyushu Institute of Technology, Kitakyushu-shi, Fukuoka, Japan

    Sozo Inoue

  4. Okayama University, Okayama-shi, Okayama, Japan

    Yusuke Gotoh

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Takayanagi, K., Murao, K., Mochizuki, M., Nishio, N. (2018). User Attribute Classification Method Based on Trajectory Patterns with Active Scanning Devices. In: Murao, K., Ohmura, R., Inoue, S., Gotoh, Y. (eds) Mobile Computing, Applications, and Services. MobiCASE 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 240. Springer, Cham. https://doi.org/10.1007/978-3-319-90740-6_24

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  • DOI: https://doi.org/10.1007/978-3-319-90740-6_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90739-0

  • Online ISBN: 978-3-319-90740-6

  • eBook Packages: Computer ScienceComputer Science (R0)