A Highly Accurate Transportation Mode Recognition Using Mobile Communication Quality

Wataru KAWAKAMI  Kenji KANAI  Bo WEI  Jiro KATTO  

IEICE TRANSACTIONS on Communications   Vol.E102-B   No.4   pp.741-750
Publication Date: 2019/04/01
Publicized: 2018/10/15
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2018SEP0013
Type of Manuscript: Special Section PAPER (Special Section on Sensing, Wireless Networking, Data Collection, Analysis and Processing Technologies for Ambient Intelligence with Internet of Things)
mobile sensing,  transportation mode recognition,  communication quality,  machine learning,  deep learning,  quality of service,  

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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.