Hu Zhenlin, Wei Pengru, Sheng Lin, Wang Guorui, Meng Xianghai
School of Transportation Science and Engineering, Harbin Institute of Technology, 73 Huanghe Street, Nangang District, Harbin 150090, PR China.
Accid Anal Prev. 2025 Jun;215:108019. doi: 10.1016/j.aap.2025.108019. Epub 2025 Mar 30.
Freeway horizontal curves pose great challenges to vehicle driving safety due to suboptimal road alignment, poor visual conditions, and higher demands for driving maneuvers. The interaction between multiple conflicting vehicles may generate multi-type crash risks with correlations. Modeling individual types of crash risks separately will result in biased crash estimation. In this study, a bivariate Bayesian hierarchical extreme value modeling approach, which consists of a bivariate extreme value model and a Bayesian hierarchical structure, is developed. The former integrates two different conflict indicators while also accounting for their correlation. The latter combines traffic conflicts across different sites, incorporating block-level and site-level covariates and unobserved heterogeneity. Using rear-end and lane-changing conflicts collected from 14 directional curved segments of the Yinkun freeway, several univariate Bayesian hierarchical extreme value models (UBHMS) and bivariate Bayesian hierarchical extreme value models (BBHMS) were constructed to estimate expected rear-end crashes and side crashes. The crash estimation results show that the bivariate model considering correlation between multi-type conflicts has smaller standard deviations of the model parameters and outperforms the univariate models in both accuracy and precision of crash estimation. The covariate analysis suggests that a larger proportion of large vehicles and standard deviation of speed will lead to an increase in both rear-end and side crash risks; the number of car-following vehicles and the number of lane-changing vehicles have positive influences on rear-end and side crash risks, respectively, whereas the higher the overspeed and the lane space occupancy instead reduce rear-end crash risk. Finally, when vertical curves overlap with horizontal curves, the rear-end and side crash risks on sag vertical curves exceed those on crest vertical curves.
高速公路的水平曲线由于道路线形欠佳、视觉条件差以及对驾驶操作的要求较高，给车辆行驶安全带来了巨大挑战。多辆冲突车辆之间的相互作用可能会产生具有相关性的多种类型碰撞风险。单独对每种类型的碰撞风险进行建模会导致碰撞估计出现偏差。在本研究中，开发了一种双变量贝叶斯分层极值建模方法，该方法由双变量极值模型和贝叶斯分层结构组成。前者整合了两个不同的冲突指标，同时考虑了它们的相关性。后者结合了不同地点的交通冲突，纳入了街区层面和地点层面的协变量以及未观测到的异质性。利用从银昆高速公路14个定向弯道段收集的追尾和变道冲突数据，构建了几个单变量贝叶斯分层极值模型（UBHMS）和双变量贝叶斯分层极值模型（BBHMS），以估计预期的追尾碰撞和侧面碰撞。碰撞估计结果表明，考虑多种类型冲突之间相关性的双变量模型具有较小的模型参数标准差，在碰撞估计的准确性和精度方面均优于单变量模型。协变量分析表明，较大比例的大型车辆和速度标准差会导致追尾和侧面碰撞风险增加；跟车车辆数量和变道车辆数量分别对追尾和侧面碰撞风险有正向影响，而超速和车道空间占用率越高反而会降低追尾碰撞风险。最后，当竖曲线与平曲线重叠时，凹形竖曲线上的追尾和侧面碰撞风险超过凸形竖曲线上的风险。
