Volume 40 Issue 6
Dec.  2022
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Article Navigation >  Journal of Transport Information and Safety  > 2022  >  40(6): 22-32
KOU Min, ZHANG Mengmeng, ZHAO Junxue, XIE Qingmin, LI Xin, ZHANG Ronglin. A Review of Identification and Analysis Methods for Road Safety Risk[J]. Journal of Transport Information and Safety, 2022, 40(6): 22-32. doi: 10.3963/j.jssn.1674-4861.2022.06.003
Citation: KOU Min, ZHANG Mengmeng, ZHAO Junxue, XIE Qingmin, LI Xin, ZHANG Ronglin. A Review of Identification and Analysis Methods for Road Safety Risk[J]. Journal of Transport Information and Safety, 2022, 40(6): 22-32. doi: 10.3963/j.jssn.1674-4861.2022.06.003
shu

A Review of Identification and Analysis Methods for Road Safety Risk

doi: 10.3963/j.jssn.1674-4861.2022.06.003
  • 1.

    School of Transportation and Logistics Engineering, Shandong Jiaotong University, Jinan 250357, China

  • 2.

    Traffic Management Bureau of Shandong Public Security Department, Jinan 250000, China

  • 3.

    Shandong High-speed Transportation Construction Group Co., LTD., Jinan 250101, China

  • 4.

    SHUIFA Technology Group Co.Ltd., Jinan 250100, China

  • Received Date: 2022-04-07
    Available Online: 2023-03-27
    Abstract
  • The accuracy and comprehensiveness of road traffic safety risk identification and analysis is the basis and key link to achieve active risk prevention and control, and directly affects the refinement level of road traffic safety management. This paper summarizes and comments on the studies related to road traffic safety risk from two aspects of influencing factors and analysis methods. In a view of the single factor risk such as unsafe behavior of drivers, unsafe state of vehicles, unsafe conditions of roads, and external environmental stimulation, as well as the correlation and coupling risk identification among multiple factors, the road traffic safety risk analysis methods such as safety risk theoretical analysis method, system safety analysis method, big data and artificial intelligence analysis method are sorted out. The study shows that the qualitative analysis methods such as the safety risk theoretical analysis method and the system safety analysis method focus on the comprehensive and systematic analysis of the road traffic safety risk factors, and have the advantages of simplicity, directness, and ease of operation, but there are many limitations in the quantitative analysis of road traffic accidents and the deep excavation of accident causes under the influence of multiple factors. Big data and artificial intelligence analysis methods based on multi-source data mining technology have obvious advantages in massive information perception, efficient computing, and processing, and can comprehensively analyze and accurately mine traffic safety risks based on multiple data, depict accident risk characteristics under the coupling of multiple factors, and explore the rules of accident occurrence, which is the current mainstream research direction. It also points out the shortcomings in the field of road traffic safety risk research and the direction of future research and development, mainly including the dynamic collection and fusion of multi-source heterogeneous data, road traffic safety risk identification under the intelligent network environment, and the research of transplantable road traffic safety risk identification model considering space-time heterogeneity.

     

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