Machine Learning Based Transportation Modes Recognition Using Mobile Communication Quality

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.