Analyzing freeway diverging risks using high-resolution trajectory data based on conflict prediction models

This study aims to develop a reliable safety evaluation model for diverging vehicles and investigates the impact of the surrounding traffic environment on freeway diverging risks. High-resolution trajectory data from three sites in the Netherlands (Delft, Ter-Heide, and Zonzeel) were employed for the risk analysis. Linear regression (LR), Support vector machine (SVM), Random Forest (RF), Extreme randomize trees (ET), Adaptive boosting (Adaboost), Extreme gradient boosting (XGboost), and Multilayer perceptron (MLP), were developed for safety evaluation. The result showed that MLP outperforms the other models for diverging risk prediction over all the indicators, conflict thresholds, and locations. Pairwise matrix, Shapely addictive explanation (SHAP), and Linear regression algorithms were further adopted to interpret the influence of the surrounding environment. It indicates that an increase in traffic density, subject vehicle lateral speed, the distance of subject vehicle from ramp nose, and subject vehicle length would increase the diverging risk. At the same time, an increase in leading vehicle speed and space headway would decrease diverging risk. Finally, spatial analysis was also conducted to explore the stability of identified traffic features regarding the impact on the diverging risk across the sites.