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    高速公路非常规交通事故研究综述

    Review of the Research Toward Freeway Unconventional Traffic Accidents

    • 摘要: 高速公路事故频发造成了严重的生命财产安全问题.诸多学者在高速公路交通事故致因分析、事故严重程度分析及事故预测等方面获得了丰硕的研究成果,然而多数研究和文献聚焦于对常规事故研究成果的梳理,缺少对高速公路二次事故、多车事故等非常规事故足够的关注.本文对目前高速公路交通事故方面的研究成果以及新技术手段在高速公路交通安全的应用进行了总结,并着重对二次事故及多车事故等非常规交通事故研究进展进行梳理,指出目前研究的问题、需求和挑战,并探讨未来的研究方向.分析表明,高速公路常规交通事故与非常规事故的发生机理存在一定差异;在针对影响因素挖掘、严重程度分析和事故预测的研究中,两类事故的模型适用性也有所不同.模型特性方面,基于数理统计分析的传统模型在处理事故与多因素之间的非线性关系中并不占优势,机器学习手段在处理输入和输出数据之间的非线性关系方面优势显著,但模型可解释性不强;各类方法的侧重点不同导致其均存在一定的局限性.随着交通事故信息采集技术手段的丰富以及计算机性能的提升,如何扩展研究角度并提高模型性能值得进一步思考;车路协同与智能网联等新兴技术的发展将与高速公路交通安全深度融合,这将为交通安全数字治理与动态服务提供全新的应用场景.

       

      Abstract: 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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