Transportation modes classification using sensors on smartphones

Shih Hau Fang, Hao Hsiang Liao, Yu Xiang Fei, Kai Hsiang Chen, Jen Wei Huang, Yu Ding Lu, Yu Tsao

Research output: Contribution to journalArticle

22 Citations (Scopus)

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.

Original languageEnglish
Article number1324
JournalSensors (Switzerland)
Volume16
Issue number8
DOIs
Publication statusPublished - 2016 Aug 19

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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  • Cite this

    Fang, S. H., Liao, H. H., Fei, Y. X., Chen, K. H., Huang, J. W., Lu, Y. D., & Tsao, Y. (2016). Transportation modes classification using sensors on smartphones. Sensors (Switzerland), 16(8), [1324]. https://doi.org/10.3390/s16081324