A highly accurate transportation mode recognition using mobile communication quality

Wataru Kawakami, Kenji Kanai, Bo Wei, Jiro Katto

Research output: Contribution to journalArticle

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

Fingerprint

Communication
Accelerometers
Sensors
Subways
Bicycles
Video streaming
Base stations
Support vector machines
Global positioning system
Classifiers
Demonstrations
Throughput
Neural networks
Experiments

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

Cite this

A highly accurate transportation mode recognition using mobile communication quality. / Kawakami, Wataru; Kanai, Kenji; Wei, Bo; Katto, Jiro.

In: IEICE Transactions on Communications, Vol. E102B, No. 4, 01.04.2019, p. 741-750.

Research output: Contribution to journalArticle

@article{d2603c043b0248d597a6497727c8fa97,
title = "A highly accurate transportation mode recognition using mobile communication quality",
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.",
keywords = "Communication quality, Deep learning, Machine learning, Mobile sensing, Quality of service, Transportation mode recognition",
author = "Wataru Kawakami and Kenji Kanai and Bo Wei and Jiro Katto",
year = "2019",
month = "4",
day = "1",
doi = "10.1587/transcom.2018SEP0013",
language = "English",
volume = "E102B",
pages = "741--750",
journal = "IEICE Transactions on Communications",
issn = "0916-8516",
publisher = "Maruzen Co., Ltd/Maruzen Kabushikikaisha",
number = "4",

}

TY - JOUR

T1 - A highly accurate transportation mode recognition using mobile communication quality

AU - Kawakami, Wataru

AU - Kanai, Kenji

AU - Wei, Bo

AU - Katto, Jiro

PY - 2019/4/1

Y1 - 2019/4/1

N2 - 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.

AB - 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.

KW - Communication quality

KW - Deep learning

KW - Machine learning

KW - Mobile sensing

KW - Quality of service

KW - Transportation mode recognition

UR - http://www.scopus.com/inward/record.url?scp=85063983137&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85063983137&partnerID=8YFLogxK

U2 - 10.1587/transcom.2018SEP0013

DO - 10.1587/transcom.2018SEP0013

M3 - Article

AN - SCOPUS:85063983137

VL - E102B

SP - 741

EP - 750

JO - IEICE Transactions on Communications

JF - IEICE Transactions on Communications

SN - 0916-8516

IS - 4

ER -