为根据道路实时交通流运行状况针对性地制定安全管理方法，使用高速公路事故数据和实时交通流数据建立了事故风险评估与分析模型。采用匹配式病例-对照研究方法构建实验样本集，通过随机森林算法从构建的多个交通流变量中筛选出影响事故风险最重要的部分变量，以此建立支持向量机事故风险评估模型，并比较了不同核函数下模型的评估能力；同时为了探索不同病例-对照配对比对模型评估结果的影响，以不同的配对比构建了多个实验样本集进行实验。结果表明，模型能有效地根据实时交通流来评估道路事故风险；研究还发现，增大事故-非事故配对比能在一定程度上提升模型预测能力，该配对比无须沿用经验法则取值，可根据实际需求分析确定。

A crash risk prediction model for freeway was developed with crash data and real-time traffic flow data to improve road active traffic management. The experimental sample sets were designed in matched case-control study and then the most significant traffic variables that have a crucial impact on the crash were selected by random forest algorithm. Based on the selected variables, the crash risk prediction model was developed in the support vector machine algorithm, and the performance of SVM models in the different kernel functions was compared. Meanwhile, in order to explore the effect of case-control matching ratios on the model performance, multiple sample sets with the different matching ratios were designed for the experiment. The results show that the model can effectively eva-luate the crash risk model according to the real-time traffic flow data. At the same time, the results show that increasing the case-control matching ratio has a particular effect on improving the models performance, and the ratio could be set explicitly according to traffic management needs.