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Transportation Modes Classification Using Sensors on Smartphones.

Shih-Hau Fang1, Hao-Hsiang Liao2, Yu-Xiang Fei3

  • 1Department of Electrical Engineering, Yuan Ze University, Taoyuan 320, Taiwan. shfang@saturn.yzu.edu.tw.

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Summary

This study uses smartphone sensor data and machine learning to classify transportation modes. Improved features boosted accuracy, with Support Vector Machines showing the best performance for classifying vehicular and transport modes.

Keywords:
big dataclassificationmachine learningsensorsmart phonetransportation mode

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Area of Science:

  • * Mobile Sensing
  • * Machine Learning
  • * Transportation Engineering

Background:

  • * Smartphone sensors (accelerometer, magnetometer, gyroscope) generate big data for user activity recognition.
  • * Accurate classification of transportation and vehicular modes is crucial for intelligent transportation systems and urban planning.

Purpose of the Study:

  • * To investigate the classification of transportation and vehicular modes using smartphone sensor data.
  • * To propose improved features for enhanced classification accuracy.
  • * To compare the performance of Decision Trees, K-Nearest Neighbor, and Support Vector Machine algorithms.

Main Methods:

  • * Utilized accelerometer, magnetometer, and gyroscope data from smartphones.
  • * Developed and applied improved feature engineering techniques.
  • * Implemented and evaluated three machine learning algorithms: Decision Trees, K-Nearest Neighbor, and Support Vector Machine.

Main Results:

  • * Proposed features significantly improved classification accuracy for both transportation and vehicular modes.
  • * Support Vector Machine achieved the highest classification accuracy.
  • * Support Vector Machine exhibited the longest prediction time compared to other models.

Conclusions:

  • * Enhanced features improve the accuracy of transportation and vehicular mode classification using smartphone sensors.
  • * Machine learning algorithms, particularly Support Vector Machines, are effective for this task.
  • * There is a trade-off between classification accuracy and prediction time among the evaluated algorithms.