There is an increased interest in the use of traffic conflicts as a surrogate safety measure and several traffic conflict indicators have been developed. One of these indicators is the deceleration rate to avoid a crash (DRAC). Generally, the greater the DRAC value, the higher the crash risk and a crash would occur when the DRAC exceeds the maximum available deceleration rate (MADR). It is noted that the MADR varies considerably for individual vehicles and depends on many factors such as the pavement conditions, vehicle weight, tire, and the braking system. Previous studies usually either set a specific value for the MADR or randomly sample values from a truncated normal distribution of MADR. However, little is known about which threshold determination approach is better. Therefore, this study aims to compare the threshold determination approaches for DRAC-based crash estimation applying Bayesian hierarchical extreme value modeling. Using traffic conflict and crash data collected from four signalized intersections in the city of Surrey, several Bayesian hierarchical models are developed for five specific values of MADR and values from two truncated normal distributions of MADR. The crash frequencies estimated from these models were compared with observed crashes. The results show that, in terms of DRAC-based crash estimation accuracy, the truncated normal distribution N(8.45, 1.4²)I(4.23, 12.68) of MADR outperforms other determination methods of MADR. Moreover, in terms of DRAC-based crash estimation accuracy and precision, the use of multisite Bayesian hierarchical models outperforms the at-site models. The truncated normal distribution N(8.45, 1.4²)I(4.23, 12.68) of MADR is therefore recommended for DRAC-based crash estimation.

人们越来越关注使用交通冲突作为替代安全措施，并且已经开发了一些交通冲突指标。这些指标之一是避免碰撞的减速率（DRAC）。通常，DRAC值越大，崩溃风险就越高，当DRAC超过最大可用减速度（MADR）时将发生崩溃。应当指出，MADR对于单个车辆而言变化很大，并且取决于许多因素，例如路面状况，车辆重量，轮胎和制动系统。以前的研究通常要么为MADR设置特定值，要么从MADR的截断正态分布中随机采样值。然而，关于哪种阈值确定方法更好的知之甚少。所以，这项研究旨在比较使用贝叶斯分层极值模型进行基于DRAC的崩溃估计的阈值确定方法。利用从萨里市的四个信号交叉口收集的交通冲突和交通事故数据，针对五个特定的MADR值和来自两个截断的正态分布的MADR，开发了几种贝叶斯层次模型。从这些模型估计的碰撞频率与观察到的碰撞进行了比较。结果表明，就基于DRAC的崩溃估计准确性而言，截断的正态分布N（8.45，1.4 针对五个特定的MADR值和来自两个截断的MADR正态分布的值，开发了几种贝叶斯层次模型。从这些模型估计的碰撞频率与观察到的碰撞进行了比较。结果表明，就基于DRAC的崩溃估计准确性而言，截断的正态分布N（8.45，1.4 针对五个特定的MADR值和来自两个截断的正态分布的MADR，开发了几种贝叶斯层次模型。从这些模型估计的碰撞频率与观察到的碰撞进行了比较。结果表明，就基于DRAC的崩溃估计准确性而言，截断的正态分布N（8.45，1.4²）MADR的I（4.23，12.68）优于其他MADR的确定方法。此外，就基于DRAC的崩溃估计的准确性和精度而言，使用多站点贝叶斯层次模型要优于现场模型。因此，建议将MADR的正态分布N（8.45，1.4 ²）I（4.23，12.68）截短，以用于基于DRAC的崩溃估计。