Skip to main content

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

  • 726 Accesses

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

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-90740-6_24
  • Chapter length: 7 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   54.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-90740-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   72.00
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.

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)

    Google Scholar 

  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)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kenji Takayanagi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

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

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90739-0

  • Online ISBN: 978-3-319-90740-6

  • eBook Packages: Computer ScienceComputer Science (R0)