LIN Sheng, LIU Jian-bei, YAN Ying, LUO Jing, YUAN Zhen-zhou. Safety evaluation model of long-steep downgrade section for montane highway based on driving workload[J]. Journal of Traffic and Transportation Engineering, 2013, 13(6): 99-106.
Citation: LIN Sheng, LIU Jian-bei, YAN Ying, LUO Jing, YUAN Zhen-zhou. Safety evaluation model of long-steep downgrade section for montane highway based on driving workload[J]. Journal of Traffic and Transportation Engineering, 2013, 13(6): 99-106.

Safety evaluation model of long-steep downgrade section for montane highway based on driving workload

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  • Author Bio:

    LIN Sheng(1972-), male, doctoral student, +86-10-62667895, linsheng@cchmet.com

    YUAN Zhen-zhou(1966-), male, professor, PhD, +86-10-51688502, zzyuan@bjtu.edu.cn

  • Received Date: 2013-07-18
  • Publish Date: 2013-12-25
  • The existing psychological and physiological driving indexes and safety evaluation methods were analyzed, the practical vehicle test on the continuous long-steep downgrade sections of typical montane highway was put out, and the 23 549 heart rate variability (HRV) indexes of 27 truck drivers on 22 test sections were collected. Heart rate variability was taken as driving workload, the driving workload model related with longitudinal slope degree, slope length and vehicle speed was set up, and the interval range of driving comfort level on long-steep downgrade section was determined. Based on the threshold division range of driving workload, the relationship between driving workload and longitudinal slope index was analyzed, and example verification was carried out. Verification result indicates that when vehicle speed is not more than 75 km·h-1 and slope degree is from-6% to-3%, the determined risk sections contain all the accident frequent sections by using the proposed model. There are 38 traffic accidents on the determined highest-risk section and 11 traffic accidents on the determined higher-risk section, and the ratio of the death toll on the two risk sections to the total death toll is 84%. The evaluation result is consistent with the actual traffic accident distribution, so the proposed model is effective.

     

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