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.