Volume 43 Issue 2
Apr.  2025
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Article Navigation >  Journal of Transport Information and Safety  > 2025  >  43(2): 177-186
LI Chengxin, LIU Benmin, LIAO Chenfei, WANG Pengfei, HU Jiaxin, LIU Pengqian, TU Huizhao. A Method for Traffic Risk Identification Under Complex Weather Conditions Based on the BKPE Security Field Model[J]. Journal of Transport Information and Safety, 2025, 43(2): 177-186. doi: 10.3963/j.jssn.1674-4861.2025.02.018
Citation: LI Chengxin, LIU Benmin, LIAO Chenfei, WANG Pengfei, HU Jiaxin, LIU Pengqian, TU Huizhao. A Method for Traffic Risk Identification Under Complex Weather Conditions Based on the BKPE Security Field Model[J]. Journal of Transport Information and Safety, 2025, 43(2): 177-186. doi: 10.3963/j.jssn.1674-4861.2025.02.018
shu

A Method for Traffic Risk Identification Under Complex Weather Conditions Based on the BKPE Security Field Model

doi: 10.3963/j.jssn.1674-4861.2025.02.018
  • 1.

    The Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China

  • 2.

    AI thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511466, China

  • 3.

    School of Emergency Management and Safety Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China

  • Received Date: 2024-07-09
    Abstract
  • The current driving safety field (DSF) theory is based on a three-dimensional framework of "driver-vehicle-road" to construct the potential energy function. However, it overlooks the complex impact of weather conditions on driving risk, simplistically categorizing the influence of road conditions ("road") and weather conditions ("environment") into one category. This approach underestimates the extent of the impact of weather conditions ondriving risk and exhibits insufficient sensitivity to the risk calculation associated with extreme weather conditions, thereby significantly limiting the practical application of the method. Therefore, based on the DSF theory, a new environmental field function is introduced to achieve comprehensive coverage of risk factors in a"driver-vehicle-road-environment"framework. Specifically, the Behavior field, Kinetic energy field, Potential energy field, andEnvironmental field are constructed separately, and the BKPE model for driving safety field under adverse weatherconditions is proposed. In this study, the relevant parameters of the original driving safety field are re-calibratedbased on the Chinese road traffic safety dataset. Meanwhile, the exponential change characteristics of weather factors on driving safety are analyzed, and an environmental impact factor is constructed, leading to the proposal of theenvironmental field function. On the basis of the driving safety field model incorporating the environmental field, the artificial potential energy function is calculated for specific cases using the Car-100 data set for micro-analysis.Two typical events are analyzed to quantify multiple types of risks, and a comparative analysis with the originaldriving safety field model is conducted, demonstrating that the original model underestimates the risks associatedwith weather conditions. Subsequently, based on Bootstrap sampling, the average accuracy rate of the artificial potential energy function in describing actual traffic events, calculated from six samples, reaches 91.7%. Finally, corresponding driving risk control strategies are proposed based on the BKPE model.

     

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