A highly accurate transportation mode recognition using mobile communication quality

Wataru Kawakami, Kenji Kanai, Bo Wei, Jiro Katto

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)741-750
Number of pages10
JournalIEICE Transactions on Communications
VolumeE102B
Issue number4
DOIs
Publication statusPublished - 2019 Apr 1

Keywords

  • Communication quality
  • Deep learning
  • Machine learning
  • Mobile sensing
  • Quality of service
  • Transportation mode recognition

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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