Transportation mode detection by using smartphone sensors and machine learning

Abstract

The aim of this study is to detect transportation modes of the users by using smartphone sensors. Therefore, GPS (Global Positioning System), accelerometer and gyroscope sensor data have been collected while walking, running, cycling and travelling by bus or by car from the smartphone of the user. Sensor data were tagged with 12 second interval and 2500 pattern were obtained. 14 features were acquired from the dataset. Machine learning methods were tested on the dataset. Best result was obtained from GPS, accelerometer and gyroscope sensor combination and Random Forest method with 99.4% accuracy rate.