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A traffic pattern detection algorithm based on multimodal sensing

Author

Listed:
  • Yanjun Qin
  • Haiyong Luo
  • Fang Zhao
  • Zhongliang Zhao
  • Mengling Jiang

Abstract

Nowadays, smartphones are widely and frequently used in people’s daily lives for their powerful functions, which generate an enormous amount of data accordingly. The large volume and various types of data make it possible to accurately identify people’s travel behaviors, that is, transportation mode detection. Using the transportation mode detection, results can increase commuting efficiency and optimize metropolitan transportation planning. Although much work has been done on transportation mode detection problem, the accuracy is not sufficient. In this article, an accurate traffic pattern detection algorithm based on multimodal sensing is proposed. This algorithm first extracts various sensory features and semantic features from four types of sensor (i.e. accelerator, gyroscope, magnetometer, and barometer). These sensors are commonly embedded in commodity smartphones. All the extracted features are then fed into a convolutional neural network to infer traffic patterns. Extensive experimental results show that the proposed scheme can identify four transportation patterns with 94.18% accuracy.

Suggested Citation

  • Yanjun Qin & Haiyong Luo & Fang Zhao & Zhongliang Zhao & Mengling Jiang, 2018. "A traffic pattern detection algorithm based on multimodal sensing," International Journal of Distributed Sensor Networks, , vol. 14(10), pages 15501477188, October.
    • Handle: RePEc:sae:intdis:v:14:y:2018:i:10:p:1550147718807832
    DOI: 10.1177/1550147718807832
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    References listed on IDEAS

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    1. Muhammad Shafique & Eiji Hato, 2015. "Use of acceleration data for transportation mode prediction," Transportation, Springer, vol. 42(1), pages 163-188, January.
    2. Tao Feng & Harry J.P. Timmermans, 2016. "Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data," Transportation Planning and Technology, Taylor & Francis Journals, vol. 39(2), pages 180-194, March.
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