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.
Highlights
Statistics from the World Health Organization (WHO) show that the number of deaths caused by road traffic crashes is about 1.35 million each year, ranking eighth among all causes of death [1]. e serious consequences of traffic crashes have driven researchers to investigate the causes of the crashes
A study conducted by National Highway Traffic Safety Administration (NHTSA) found that driver-related factors account for 94% of the critical reasons of these crashes, and most studies indicated that traffic crashes can largely result from risky driving behaviors [2,3,4]
To better illustrate the rear-end crash risk identification measure proposed in this study, TTC, deceleration rate to avoid crash (DRAC), stop distance index (SDI), and rear-end crash risk index (RCRI) were all employed to quantify the risk for one car-following event
Summary
Statistics from the World Health Organization (WHO) show that the number of deaths caused by road traffic crashes is about 1.35 million each year, ranking eighth among all causes of death [1]. e serious consequences of traffic crashes have driven researchers to investigate the causes of the crashes. Among the many causes, driving behavior has been found to be a crucial one. A study conducted by National Highway Traffic Safety Administration (NHTSA) found that driver-related factors account for 94% of the critical reasons of these crashes, and most studies indicated that traffic crashes can largely result from risky driving behaviors [2,3,4]. To reduce casualties and mitigate injuries from traffic crashes, understanding and identifying the crash risk is essential. Statistics from the NHTSA show that the rear-end crashes accounting for 32.4% of all accident types that cause personal injury [7]. Since most rear-end crashes occurred in car-following situations, it has become crucial to identify the rear-end crash risk during car-following process and explore its influencing factors [8,9,10]
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