标题： 捕捉双车道乡村高速公路事故严重性的道路级异质性：多层次混合效应方法 显示英文标题

标题： Capturing Road-Level Heterogeneity in Crash Severity on Two-Lane Rural Highways: A Multilevel Mixed-Effects Approach

摘要： 准确建模农村双车道道路的碰撞严重性对于有效的安全管理至关重要，但标准的单层次方法常常忽略了不同路段之间的未观测异质性。 在本研究中，我们分析了伊朗99条农村道路在过去四年中的19 956条碰撞记录，结合了碰撞层面的预测变量，如驾驶员年龄、教育程度、性别、照明和路面状况，以及道路层面的协变量，如年平均日交通量、重型车辆占比和地形坡度。 我们比较了三种二元逻辑框架：一个单层次广义线性模型，一个具有随机截距的多层次模型，用于捕捉潜在的道路层面效应（组内相关系数=21%），以及一个具有随机系数的多层次模型，允许关键预测变量的影响因道路而异。 随机系数模型在偏差、AIC和BIC方面表现出最佳拟合，并显著提高了预测性能：分类准确率从0.62提高到0.71，召回率从0.32提高到0.63，AUC从0.570提高到0.775。 200次模拟运行的结果显示，路面和照明变量的斜率存在显著差异，突显了局部环境对碰撞风险的影响。 总体而言，我们的研究结果表明，灵活的多层次建模不仅提高了预测准确性，还提供了具体情境的见解，以指导农村道路网络上的针对性安全干预措施。 显示英文摘要

摘要： Accurately modeling crash severity on rural two-lane roads is essential for effective safety management, yet standard single level approaches often overlook unobserved heterogeneity across road segments. In this study, we analyze 19 956 crash records from 99 rural roads in Iran during recent four years incorporating crash level predictors such as driver age, education, gender, lighting and pavement conditions, along with road level covariates like annual average daily traffic, heavy-vehicle share and terrain slope. We compare three binary logistic frameworks: a single level generalized linear model, a multilevel model with a random intercept capturing latent road level effects (intraclass correlation = 21 %), and a multilevel model with random coefficients that allows key predictor effects to vary by road. The random coefficient model achieves the best fit in terms of deviance, AIC and BIC, and substantially improves predictive performance: classification accuracy rises from 0.62 to 0.71, recall from 0.32 to 0.63, and AUC from 0.570 to 0.775. Results from 200 simulation runs reveal notable variability in slopes for pavement and lighting variables, underscoring how local context influences crash risk. Overall, our findings demonstrate that flexible multilevel modeling not only enhances prediction accuracy but also yields context-specific insights to guide targeted safety interventions on rural road networks.