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Open AccessArticle

Transportation Modes Classification Using Sensors on Smartphones

1
Department of Electrical Engineering, Yuan Ze University, Taoyuan 320, Taiwan
2
Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan
3
Research Center for Information Technology Innovation, Academia Sinica, Taipei 115, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Teen-Hang Meen, Shoou-Jinn Chang, Stephen D. Prior and Artde Donald KinTak Lam
Sensors 2016, 16(8), 1324; https://doi.org/10.3390/s16081324
Received: 26 May 2016 / Revised: 5 August 2016 / Accepted: 16 August 2016 / Published: 19 August 2016
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. View Full-Text
Keywords: transportation mode; big data; machine learning; sensor; smart phone; classification transportation mode; big data; machine learning; sensor; smart phone; classification
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Fang, S.-H.; Liao, H.-H.; Fei, Y.-X.; Chen, K.-H.; Huang, J.-W.; Lu, Y.-D.; Tsao, Y. Transportation Modes Classification Using Sensors on Smartphones. Sensors 2016, 16, 1324.

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