Quantification of cut-in risk and analysis of its influencing factors: a study using random parameters ordered probit model

Abstract

Abstract In the cut-in scenario, drivers are forced to experience a smaller headway distance, which may easily lead to rear-end crashes and reduced road traffic efficiency. Quantitatively evaluating cut-in risks and considering the heterogeneity of driving maneuvers to explore the influencing factors of cut-in risks using microscopic driving behavior data are still limited. In this study, a cut-in risk index (CIRI) was proposed to evaluate the cut-in risk based on fault tree analysis (FTA). To consider the heterogeneity of driving maneuvers, a random parameter ordered probit (RPOP) model was employed to recognize the key determinants of risky cut-in maneuvers. The results obtained in this study show that during the cut-in process, the cut-in vehicle has the highest crash risk with the preceding vehicle in the current lane compared to other surrounding vehicles. The proposed surrogate measure can objectively quantify cut-in risk. The present study suggests that the driver not only needs to pay attention to the following vehicle in the target lane, but also pay more attention to the preceding vehicle in the current lane during cut-in. Quantifying cut-in risks and exploring its influencing factors are essential for road traffic control, thereby improving driving safety and traffic efficiency.