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    YANG Yang, WANG Wenhui, WU Xianyu, WANG Yunpeng. Review of the Research Toward Freeway Unconventional Traffic Accidents[J]. Journal of Basic Science and Engineering, 2024, 32(3): 601-626. DOI: 10.16058/j.issn.1005-0930.2024.03.001
    Citation: YANG Yang, WANG Wenhui, WU Xianyu, WANG Yunpeng. Review of the Research Toward Freeway Unconventional Traffic Accidents[J]. Journal of Basic Science and Engineering, 2024, 32(3): 601-626. DOI: 10.16058/j.issn.1005-0930.2024.03.001

    Review of the Research Toward Freeway Unconventional Traffic Accidents

    • Frequent traffic accidents on freeways have caused serious safety problems of human life and property.Previous researches have yielded valuable insights into accidents cause analysis and accident severity prediction methods,but often focus on conventional traffic accidents,ignoring unconventional accidents such as secondary and multi vehicle crashes.This review aims to summarize the current research literature review of freeway traffic accidents and the application of innovative technologies in freeway traffic safety.Especially,it focuses on the research progress towards unconventional traffic accidents such as secondary traffic accidents and multi vehicle traffic accidents,figures out the problems,demands and challenges of the current research,and discusses the future research and application direction.The analysis indicates that there are some differences between the occurrence mechanism of conventional traffic accidents and unconventional accidents.For the research towards factors’ exploration,severity analysis and accident prediction,the model applicability between conventional traffic accidents and unconventional accidents is typically different.In terms of model characteristics,the traditional models based on mathematical statistics analysis are not dominant in dealing with the nonlinear relationship between accidents and multiple factors,while the machine learning approaches have significant advantages in dealing with the nonlinear relationship between input and output data.However,the machine learning approaches are not strong in interpretation.Consequently,due to the different mechanism of these models,there are some limitations in various methods.With the abundance of the traffic accident information collection means and accuracy,as well as the improvement of computer performance,extending the research ideas and improving the model performance are worthy of further consideration.Moreover,emerging technologies such as Vehicle-to-Everything (V2X) communication systems and intelligent networking will be deeply integrated with freeway traffic safety,providing new application scenarios for traffic safety digital governance and dynamic services.
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