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.

中文翻译：

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基于一种新的替代安全措施量化后方碰撞风险并分析其影响因素

传统的替代安全措施（SMoS）无法充分考虑碰撞机制，或者无法同时反映碰撞概率和碰撞严重性。另外，驾驶风险随着驾驶员的个人驾驶特性和环境因素而不断变化。考虑到驾驶员的异质性，研究行为特征和环境特征对追尾事故风险的影响对于确保驾驶安全至关重要。在这项研究中，从上海自然驾驶研究（SH-NDS）中识别并提取了16905个跟车事件。然后提出了一种新的SMoS，称为后端碰撞风险指数（RCRI），以量化后端碰撞风险。基于此措施，进行了风险比较分析，以调查来自不同方面的因素对天气的影响，时间变数和交通状况。然后，采用混合效应线性回归模型来阐明追尾事故风险及其影响因素之间的关系。结果表明，RCRI可以反映出后端碰撞风险的动态变化，并且可以应用于任何跟车情况。对比分析表明，高交通密度，工作日和高峰时段导致更高的风险。此外，混合效应线性回归模型的结果表明，驾驶特性，交通密度，星期几（工作日与节假日）和一天中的时间（高峰时段与非高峰时段）具有显着影响。驾驶风险。这项研究提供了一种新的替代安全措施，可以以更可靠的方式更好地识别后端碰撞风险，并可以将其应用于驾驶员辅助系统中的实时碰撞风险预测。此外，本研究结果可为制定交通管理策略以提高驾驶安全性提供理论依据。