Citation

Fang SH, Liao HH, Fei YX, Chen KH, Huang JW, Lu YD, Tsao Y. Sensors (Basel) 2016; 16(8): e16081324.

Affiliation

Research Center for Information Technology Innovation, Academia Sinica, Taipei 115, Taiwan. yu.tsao@citi.sincia.edu.tw.

Copyright

(Copyright © 2016, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s16081324

PMID

27548182

Abstract

This paper investigates the transportation and vehicular modes classification by using big data from smartphone sensors. The three types of sensors used in this paper include the accelerometer, magnetometer, and gyroscope. This study proposes improved features and uses three machine learning algorithms including decision trees, K-nearest neighbor, and support vector machine to classify the user's transportation and vehicular modes. In the experiments, we discussed and compared the performance from different perspectives including the accuracy for both modes, the executive time, and the model size.

RESULTS show that the proposed features enhance the accuracy, in which the support vector machine provides the best performance in classification accuracy whereas it consumes the largest prediction time. This paper also investigates the vehicle classification mode and compares the results with that of the transportation modes.

Language: en