Multivariate extreme value modelling techniques are widely applied to estimate crash risks from traffic conflicts, with a predominant focus on rear-end crashes. In contrast, the suitability of conflict measures within a multivariate framework for estimating opposing-through crash risks has received less attention. This study proposes a non-stationary bivariate extreme value model to identify a suitable set of traffic conflict measures for estimating opposing-through crashes (i.e., right-turn crashes for left-hand driving conditions and vice versa) by severity levels. In the proposed Generalised Extreme Value model, three crossing course conflict measures were considered, including post encroachment time (PET), gap time (GT), and supplementary time-to-collision (). Artificial intelligence-based video analytics were employed to extract these opposing-through conflict measures from a total of 144 h of video recordings of four permissible right-turn approaches for three signalised intersections in Brisbane, Australia. The models included exposure variables such as conflicting volume, right-turning volume and through volume, and evasive action-based variables like deceleration and relative velocities measured at the signal cycle level to account for non-stationarity in the extreme value models. Results suggested that a bivariate model with PET and GT as the traffic conflict measures performs better than a univariate model or other combinations of traffic conflict measures in the bivariate models. This PET-GT combination of conflict measures also showed better accuracy in estimating opposing-through crash frequencies by severity levels when combined with the (Delta-V) based severity measure. This study demonstrated the importance of accounting for various stages of opposing-through conflicts within a bivariate extreme value model to predict crash risks.

中文翻译：

---

一种双变量非平稳极值模型，用于通过应用基于人工智能的视频分析来按严重程度估计反向碰撞频率

多元极值建模技术广泛应用于估计交通冲突造成的碰撞风险，主要关注追尾碰撞。相比之下，在多变量框架内采用冲突措施来估计反对通过崩溃风险的适用性却受到较少关注。本研究提出了一种非平稳二元极值模型，以确定一组合适的交通冲突措施，用于按严重程度估计对向碰撞事故（即，左舵驾驶条件下的右转碰撞事故，反之亦然）。在提出的广义极值模型中，考虑了三种交叉路线冲突措施，包括侵入后时间（PET）、间隙时间（GT）和补充碰撞时间（）。采用基于人工智能的视频分析，从澳大利亚布里斯班三个信号交叉口的四种允许右转方法的总共 144 小时的视频记录中提取这些反对通过冲突措施。这些模型包括暴露变量，例如冲突体积、右转体积和通过体积，以及基于规避动作的变量，例如在信号周期级别测量的减速度和相对速度，以解释极值模型中的非平稳性。结果表明，以 PET 和 GT 作为交通冲突度量的双变量模型比单变量模型或双变量模型中交通冲突度量的其他组合表现得更好。当与基于 (Delta-V) 的严重性测量相结合时，冲突测量的 PET-GT 组合在按严重程度估计反对通过碰撞频率方面也表现出更好的准确性。这项研究证明了在二元极值模型中考虑对立冲突的各个阶段来预测崩溃风险的重要性。