Next Article in Journal
A Method to Simultaneously Detect the Current Sensor Fault and Estimate the State of Energy for Batteries in Electric Vehicles
Next Article in Special Issue
The Front-End Readout as an Encoder IC for Magneto-Resistive Linear Scale Sensors
Previous Article in Journal
The Availability of Space Service for Inter-Satellite Links in Navigation Constellations
Previous Article in Special Issue
Generation of Localized Surface Plasmon Resonance Using Hybrid Au–Ag Nanoparticle Arrays as a Sensor of Polychlorinated Biphenyls Detection
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(8), 1324; doi:10.3390/s16081324

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
Received: 26 May 2016 / Revised: 5 August 2016 / Accepted: 16 August 2016 / Published: 19 August 2016
View Full-Text   |   Download PDF [6577 KB, uploaded 23 August 2016]   |  

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. View Full-Text
Keywords: transportation mode; big data; machine learning; sensor; smart phone; classification transportation mode; big data; machine learning; sensor; smart phone; classification
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top