Research Article
User Attribute Classification Method Based on Trajectory Patterns with Active Scanning Devices
@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
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.