A study of freeway crash risk prediction and interpretation based on risky driving behavior and traffic flow data.

The prediction of traffic crashes is an essential topic in traffic safety research. Most of the previous studies conducted experiments on real-time crash prediction of expressways or freeways, based on traffic flow data. However, the influence of risky driving behavior on traffic crash risk prediction has rarely been considered. Thus, a traffic crash risk prediction model based on risky driving behavior and traffic flow has been developed. The data employed in this research were captured using the in-vehicle AutoNavigator software. A random forest to select variables with strong impacts on crashes and the synthetic minority oversampling technique (SMOTE) to adjust the imbalanced dataset were included in the research. A logistic regression model was developed to predict the risk of traffic crash and to interpret its relationship with traffic flow and risky driving behavior characteristics. This model accurately predicted 84.48% of the crashes, while its false alarm rate remained as low as 9.75%, which indicated that this traffic crash risk prediction model had high accuracy. By analyzing the relationship between traffic flow, risky driving behavior, and crashes through partial dependency plots (PDPs), the impact of traffic flow and risky driving behavior variables on certain traffic crashes in the prediction model were determined. Through this study, the data of traffic flow and risky driving behavior could be used to assess the traffic crash risk on freeways and lay a foundation for traffic safety management.