A study of freeway crash risk prediction and interpretation based on risky driving behavior and traffic flow data. (September 2021)

• Record Type: Journal Article
• Authors: Guo, Miao; Zhao, Xiaohua; Yao, Ying; Yan, Pengwei; Su, Yuelong; Bi, Chaofan; Wu, Dayong
• Is Part Of: Accident analysis and prevention. Volume 160(2022)
• Publication Date: 2021-09
• Subjects: Traffic crash risk prediction -- Traffic flow -- Risky driving behavior -- Logistic regression model; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2021.106328
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: We have developed a traffic crash risk prediction model based on the data of risky driving behavior and traffic flow. Six indicators were selected to predict traffic crash risk, including volume, average speed, congestion index, coefficient of variation of speed, sharp acceleration and sharp deceleration. The influence relationship between traffic flow, risky driving behavior and traffic crash risk is analyzed and interpreted by Partial Dependency Plots. This study helps to directly quantify the relationship between traffic flow variables, risky driving behavior variables, and crashes in real-time crash prediction. We provide a useful reference for the transfer from post-crash passive disposal to pre-crash active prevention. Abstract: 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.