Chen Zhankun, Johnsson Carl, D'Agostino Carmelo
Department of Technology & Society, Lund University, Lund 221 00, Sweden.
Accid Anal Prev. 2025 Oct;221:108186. doi: 10.1016/j.aap.2025.108186. Epub 2025 Aug 12.
Extreme Value Theory (EVT) is the state-of-the-art method for proactive prediction of accident frequency from traffic interactions on a microscopic scale. The main advantage of using EVT is to predict unobserved critical events based on one or more Surrogate Measures of Safety (SMoS) (single- or multivariate EVT) through a mathematical extrapolation of extreme interactions. Such interactions are quantitatively described by SMoS, which commonly measure the proximity of two road users, increasing the probability of a collision as the proximity decreases. Those events with a higher likelihood of turning into an accident are defined as severe interactions, and they are considered extremes and are used in the EVT model. Since EVT analysis focuses on the upper tail of the distribution, decreasing transformations are a prerequisite, without which it is impossible to model the extremes. However, prediction results depend on the shape of the indicators' distributions. Some studies use simple transformations, such as negation, while others employ nonlinear methods that adjust the relationship between proximity and severity. In the present study, the theory of tail analysis has been used to rigorously formulate the effect of a set of conventional linear and nonlinear transformations of SMoS. The approach was tested on a Swedish dataset, and the effects of the transformations on the prediction of extreme events were evaluated based on an accident model built on local data and Empirical Byes correction. The novelty of this study is that one of the most fundamental concepts in traffic conflict theory, such as conflict-crash relationships, has been examined with mathematical interpretation. The results of this study can be further extended to become a standard procedure in modelling traffic conflicts using EVT.
极值理论（EVT）是一种用于在微观尺度上主动预测交通交互事故频率的先进方法。使用EVT的主要优点是通过对极端交互进行数学外推，基于一个或多个安全替代指标（SMoS）（单变量或多变量EVT）来预测未观察到的关键事件。此类交互由SMoS进行定量描述，SMoS通常测量两个道路使用者的接近程度，随着接近程度降低，碰撞概率增加。那些更有可能演变为事故的事件被定义为严重交互，它们被视为极端情况，并用于EVT模型。由于EVT分析关注分布的上尾，因此降序变换是一个先决条件，没有它就无法对极端情况进行建模。然而，预测结果取决于指标分布的形状。一些研究使用简单变换，如取反，而其他研究则采用调整接近程度与严重程度之间关系的非线性方法。在本研究中，尾部分析理论已被用于严格阐述一组传统线性和非线性SMoS变换的效果。该方法在瑞典数据集上进行了测试，并基于基于本地数据构建的事故模型和经验贝叶斯校正评估了变换对极端事件预测的影响。本研究的新颖之处在于，交通冲突理论中最基本的概念之一，如冲突与碰撞的关系，已通过数学解释进行了研究。本研究结果可进一步扩展，成为使用EVT对交通冲突进行建模的标准程序。
