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A Recognition Method of Truck Drivers’ Braking Patterns Based on FCM-LDA2vec

Author

Listed:
  • Jianfeng Xi

    (College of Transportation, Jilin University, Changchun 130022, China)

  • Yunhe Zhao

    (College of Transportation, Jilin University, Changchun 130022, China)

  • Zhiqiang Li

    (China Academy of Transportation Sciences, Beijing 100029, China)

  • Yizhou Jiang

    (China Academy of Transportation Sciences, Beijing 100029, China)

  • Wenwen Feng

    (China Academy of Transportation Sciences, Beijing 100029, China)

  • Tongqiang Ding

    (College of Transportation, Jilin University, Changchun 130022, China)

Abstract

Taking truck drivers’ braking patterns as the research objects, this study used a large amount of truck running data. A recognition method of truck drivers’ braking patterns was proposed to determine the distribution of braking patterns during the operation of trucks. First, the segmented data of braking behaviors were collected in order to extract 25 characteristic parameters. Additionally, seven main correlation factors were obtained by dimensionality reduction. The FCM clustering algorithm and CH scores were used to identify nine categories of truck drivers’ braking behaviors. Then the LDA2vec model was used to identify the distribution of different braking behavior words in braking patterns, and three categories of truck drivers’ braking patterns were identified. The test results showed that the accuracy of the truck drivers’ braking pattern recognition model based on LDA2vec was higher than 85%, and braking patterns of drivers in the daily operation process could be mined from vehicle operation data. Furthermore, through the monitoring and pre-warning of the braking patterns and targeted training of drivers, traffic accidents could be avoided. At the same time, this paper’s results can be used to protect human life and health and reduce environmental pollution caused by traffic congestion or traffic accidents.

Suggested Citation

  • Jianfeng Xi & Yunhe Zhao & Zhiqiang Li & Yizhou Jiang & Wenwen Feng & Tongqiang Ding, 2022. "A Recognition Method of Truck Drivers’ Braking Patterns Based on FCM-LDA2vec," IJERPH, MDPI, vol. 19(23), pages 1-13, November.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:15959-:d:988619
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    References listed on IDEAS

    as
    1. de Vries, Jelle & de Koster, René & Rijsdijk, Serge & Roy, Debjit, 2017. "Determinants of safe and productive truck driving: Empirical evidence from long-haul cargo transport," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 97(C), pages 113-131.
    2. Shengdi Chen & Qingwen Xue & Xiaochen Zhao & Yingying Xing & Jian John Lu, 2021. "Risky Driving Behavior Recognition Based on Vehicle Trajectory," IJERPH, MDPI, vol. 18(23), pages 1-14, November.
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