A Highly Accurate Transportation Mode Recognition Using Mobile Communication Quality

• KAWAKAMI Wataru

  Waseda University

• KANAI Kenji

  Waseda University

• WEI Bo

  Waseda University

• KATTO Jiro

  Waseda University

抄録

To recognize transportation modes without any additional sensor devices, we demonstrate that the transportation modes can be recognized from communication quality factors. In the demonstration, instead of using global positioning system (GPS) and accelerometer sensors, we collect mobile TCP throughputs, received-signal strength indicators (RSSIs), and cellular base-station IDs (Cell IDs) through in-line network measurement when the user enjoys mobile services, such as video streaming. In accuracy evaluations, we conduct two different field experiments to collect the data in six typical transportation modes (static, walking, riding a bicycle, riding a bus, riding a train and riding a subway), and then construct the classifiers by applying a support-vector machine (SVM), k-nearest neighbor (k-NN), random forest (RF), and convolutional neural network (CNN). Our results show that these transportation modes can be recognized with high accuracy by using communication quality factors as well as the use of accelerometer sensors.

収録刊行物

• IEICE Transactions on Communications

  IEICE Transactions on Communications E102.B (4), 741-750, 2019-04-01

  一般社団法人 電子情報通信学会

キーワード

• mobile sensing
• transportation mode recognition
• communication quality
• machine learning
• deep learning
• quality of service

詳細情報

• CRID: 1390282763119635200
• NII論文ID: 130007621794
• DOI: 10.1587/transcom.2018sep0013
• ISSN: 17451345
• ISSN: 09168516
• Web Site: https://www.jstage.jst.go.jp/article/transcom/E102.B/4/E102.B_2018SEP0013/_pdf
• 本文言語コード: en
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• 抄録ライセンスフラグ: 使用不可