Machine Learning Based Transportation Modes Recognition Using Mobile Communication Quality

Wataru Kawakami, Kenji Kanai, Bo Wei, Jiro Katto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In order to recognize the transportation modes without any additional sensor devices, we propose a recognition method by using communication quality factors. In the proposed method, instead of 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 service. In accuracy evaluations, we conduct two different field experiments to collect the data in five typical transportation modes (static, walking, riding a bicycle, a bus and a train,) and then construct the classifiers by applying Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Random Forest (RF). Results conclude that these transportation modes can be recognized by using communication quality factors with high accuracy as well as the use of accelerometer sensors.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Multimedia and Expo, ICME 2018
PublisherIEEE Computer Society
ISBN (Electronic)9781538617373
DOIs
Publication statusPublished - 2018 Oct 8
Event2018 IEEE International Conference on Multimedia and Expo, ICME 2018 - San Diego, United States
Duration: 2018 Jul 232018 Jul 27

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
Volume2018-July
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2018 IEEE International Conference on Multimedia and Expo, ICME 2018
CountryUnited States
CitySan Diego
Period18/7/2318/7/27

Keywords

  • communication quality
  • machine learning
  • mobile sensing
  • Quality of Service
  • Transportation modes recognition

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Machine Learning Based Transportation Modes Recognition Using Mobile Communication Quality'. Together they form a unique fingerprint.

  • Cite this

    Kawakami, W., Kanai, K., Wei, B., & Katto, J. (2018). Machine Learning Based Transportation Modes Recognition Using Mobile Communication Quality. In 2018 IEEE International Conference on Multimedia and Expo, ICME 2018 [8486560] (Proceedings - IEEE International Conference on Multimedia and Expo; Vol. 2018-July). IEEE Computer Society. https://doi.org/10.1109/ICME.2018.8486560