Quantification of Rear-End Crash Risk and Analysis of Its Influencing Factors Based on a New Surrogate Safety Measure

Abstract

Traditional surrogate measures of safety (SMoS) cannot fully consider the crash mechanism or fail to reflect the crash probability and crash severity at the same time. In addition, driving risks are constantly changing with driver’s personal driving characteristics and environmental factors. Considering the heterogeneity of drivers, to study the impact of behavioral characteristics and environmental characteristics on the rear-end crash risk is essential to ensure driving safety. In this study, 16,905 car-following events were identified and extracted from Shanghai Naturalistic Driving Study (SH-NDS). A new SMoS, named rear-end crash risk index (RCRI), was then proposed to quantify rear-end crash risk. Based on this measure, a risk comparative analysis was conducted to investigate the impact of factors from different facets in terms of weather, temporal variables, and traffic conditions. Then, a mixed-effects linear regression model was applied to clarify the relationship between rear-end crash risk and its influencing factors. Results show that RCRI can reflect the dynamic changes of rear-end crash risk and can be applied to any car-following scenarios. The comparative analysis indicates that high traffic density, workdays, and morning peaks lead to higher risks. Moreover, results from the mixed-effects linear regression model suggest that driving characteristics, traffic density, day-of-week (workday vs. holiday), and time-of-day (peak hours vs. off-peak hours) had significant effects on driving risks. This study provides a new surrogate safety measure that can better identify rear-end crash risks in a more reliable way and can be applied to real-time crash risk prediction in driver assistance systems. In addition, the results of this study can be used to provide a theoretical basis for the formulation of traffic management strategies to improve driving safety.