Volume 42 Issue 4
Aug.  2024
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Article Navigation >  Journal of Transport Information and Safety  > 2024  >  42(4): 30-41
SHANG Ting, LIAN Guan, HUANG Xianlong, XIE Lei. A Driving Fatigue Model for Extra-long Tunnels Based on Multi-source Data[J]. Journal of Transport Information and Safety, 2024, 42(4): 30-41. doi: 10.3963/j.jssn.1674-4861.2024.04.004
Citation: SHANG Ting, LIAN Guan, HUANG Xianlong, XIE Lei. A Driving Fatigue Model for Extra-long Tunnels Based on Multi-source Data[J]. Journal of Transport Information and Safety, 2024, 42(4): 30-41. doi: 10.3963/j.jssn.1674-4861.2024.04.004
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A Driving Fatigue Model for Extra-long Tunnels Based on Multi-source Data

doi: 10.3963/j.jssn.1674-4861.2024.04.004
  • 1.

    School of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China

  • 2.

    School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China

  • 3.

    Chongqing High Speed Engineering Consulting Co., Ltd., Chongqing 404100, China

  • Received Date: 2023-07-17
    Available Online: 2024-11-25
    Abstract
  • To investigate the evolution of driving fatigue in extra-long tunnels and its influencing factors, multi-source data from real-vehicle experiments are utilized to classify and identify driving fatigue, as well as to analyze the relationship between fatigue levels and influencing factors. Through significance tests of differences and correlation analysis, the percentage of eyelid closure over the pupil over time (PERCLOS) P80, the variable coefficient of pupil diameter, and acceleration are selected as key fatigue sensitivity indicators, and their changing patterns with accumulated driving time are examined. To construct a driving fatigue classification model, fatigue levels, based on the subjective fatigue detection results from the Karolinska sleepiness scale (KSS), are categorized into awake, semi-fatigued, and fatigued states. A multi-class classifier method is then employed to combine and classify these fatigue states. The grid-search method (GS) is utilized for parameter optimization, and the selected fatigue sensitivity indicators are used as input variables to establish a multi-class support vector machine model (GS-M-SVMs) for fatigue state classification. Following this, an ordinal multi-class Logistic model is developed to explore the relationship between driving fatigue levels and influencing factors in extra-long tunnels. The results indicate that the changing patterns of fatigue sensitivity indicators effectively capture the evolution of driving fatigue. The GS-M-SVMs model achieved a classification accuracy of 90.75%, indicating strong performance in fatigue level detection. Both accumulated driving time and tunnel length significantly influence driving fatigue levels, with regression coefficients of 2.634 and 0.395, respectively. This indicates that accumulated driving time is the primary factor contributing to increased fatigue in extra-long tunnels, while factors such as tunnel illumination and alignment do not significantly impact fatigue levels.

     

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