Estimating crash frequency by severity levels using traffic conflicts remains relatively unexplored in conflict-based traffic safety assessment, limiting its application scope and appeal compared to traditional methods. No studies to date have predicted the frequency of severe and non-severe crashes utilizing traffic conflicts. This study aims to address this critical gap and stimulate discussion and development in this critical area. The study estimates the frequency of severe crashes and non-severe crashes by jointly modeling the indicators of crash frequency, namely, Time to Collision (TTC) and Modified Time to Collision (MTTC), and crash severity, namely, predicted post-collision change in velocity (Delta-V or Δv), using bivariate Extreme Value. Severe crashes here are defined as crashes with a Maximum Abbreviated Injury Scale rating of greater than or equal to 3. Rear-end conflict data (TTC ≤ 3.0 s) were collected for two days (12 h each day) from two four-legged signalized intersections in Brisbane, Australia. Bivariate peak-over-threshold models for both TTC and MTTC indicators, combined with Delta-V, were estimated. Alternatively, another univariate approach was also attempted where the probability of crash occurrence was estimated using the univariate peak-over-threshold model with TTC (or MTTC) and then multiplied with the injury probability estimated from Delta-V to estimate the frequencies of severe and non-severe injury crashes. The study results demonstrate that the bivariate approach is more advantageous than the univariate approach due to a superior statistical fit to the data and more precise estimations of crash frequencies by severity levels. Both TTC and MTTC indicators, in combination with Delta-V, provide comparable results using the bivariate approach owing to the weak asymptotic dependence between the frequency and severity indicators. Comparing the combined dataset model of the two intersections with the intersection-based models shows that sharing information between similar traffic sites improves the accuracy and precision of prediction.

在基于冲突的交通安全评估中，使用交通冲突的严重程度来估计碰撞频率仍然相对未被探索，与传统方法相比，限制了其应用范围和吸引力。迄今为止，还没有研究预测利用交通冲突造成严重和非严重碰撞的频率。本研究旨在解决这一关键差距，并促进这一关键领域的讨论和发展。该研究通过联合建模碰撞频率指标，即碰撞时间 (TTC) 和修正碰撞时间 (MTTC) 和碰撞严重度指标，即预测的碰撞后变化，估计严重碰撞和非严重碰撞的频率在速度（Delta-V 或 Δv）中，使用二元极值。这里的严重碰撞定义为最大缩写伤害量表评级大于或等于 3 的碰撞。 追尾冲突数据（TTC ≤ 3.0 s）从两个四足信号灯收集两天（每天 12 小时）澳大利亚布里斯班的十字路口。估计了 TTC 和 MTTC 指标的双变量峰值超过阈值模型，并结合了 Delta-V。或者，还尝试了另一种单变量方法，其中使用具有 TTC（或 MTTC）的单变量峰值超过阈值模型估计碰撞发生的概率，然后乘以从 Delta-V 估计的伤害概率以估计严重和非重伤事故。研究结果表明，双变量方法比单变量方法更有利，因为它对数据具有出色的统计拟合，并且可以更精确地按严重程度对碰撞频率进行估计。由于频率和严重性指标之间的渐近依赖性较弱，TTC 和 MTTC 指标与 Delta-V 结合使用双变量方法提供可比较的结果。将两个交叉口的组合数据集模型与基于交叉口的模型进行比较表明，相似交通站点之间的信息共享提高了预测的准确性和精度。由于频率和严重程度指标之间的渐近依赖性较弱，因此使用双变量方法提供可比较的结果。将两个交叉口的组合数据集模型与基于交叉口的模型进行比较表明，相似交通站点之间的信息共享提高了预测的准确性和精度。由于频率和严重程度指标之间的渐近依赖性较弱，因此使用双变量方法提供可比较的结果。将两个交叉口的组合数据集模型与基于交叉口的模型进行比较表明，相似交通站点之间的信息共享提高了预测的准确性和精度。