Department of Civil and Environmental Engineering, University of Catania, Catania, Italy.
Accid Anal Prev. 2010 Jul;42(4):1072-9. doi: 10.1016/j.aap.2009.12.015. Epub 2010 Jan 13.
In Europe, approximately 60% of road accident fatalities occur on two-lane rural roads. Thus, research to develop and enhance explanatory and predictive models for this road type continues to be of interest in mitigating these accidents. To this end, this paper describes a novel and extensive data collection and modeling effort to define accident models for two-lane road sections based on a unique combination of exposure, geometry, consistency and context variables directly related to the safety performance. The first part of the paper documents how these were identified for the segmentation of highways into homogeneous sections. Next, is a description of the extensive data collection effort that utilized differential cinematic GPS surveys to define the horizontal alignment variables, and road safety inspections (RSIs) to quantify the other road characteristics related to safety. The final part of the paper focuses on the calibration of models for estimating the expected number of accidents on homogeneous sections that can be characterized by constant values of the explanatory variables. Several candidate models were considered for calibration using the Generalized Linear Modeling (GLM) approach. After considering the statistical significance of the parameters related to exposure, geometry, consistency and context factors, and goodness of fit statistics, 19 models were ranked and three were selected as the recommended models. The first of the three is a base model, with length and traffic as the only predictor variables; since these variables are the only ones likely to be available network-wide, this base model can be used in an empirical Bayesian calculation to conduct network screening for ranking "sites with promise" of safety improvement. The other two models represent the best statistical fits with different combinations of significant variables related to exposure, geometry, consistency and context factors. These multiple variable models can be used, with caution, and in conjunction with results from other studies, to derive accident modification factors for these variables for design applications, and in safety assessment for smaller samples of sites for which these variables can be assembled with relative ease.
在欧洲，大约 60%的道路事故死亡发生在双车道农村道路上。因此，为了减少这些事故，继续研究开发和增强解释和预测这种道路类型的模型仍然是很有意义的。为此，本文描述了一种新颖而广泛的数据收集和建模工作，旨在根据与安全性能直接相关的暴露、几何形状、一致性和环境变量，为双车道道路路段定义事故模型。本文的第一部分记录了如何根据高速公路的分段识别这些变量，将其分为同质路段。接下来，描述了广泛的数据收集工作，该工作利用差分运动 GPS 调查来定义水平对准变量，并利用道路安全检查 (RSI) 来量化与安全相关的其他道路特征。本文的最后一部分重点介绍了用于校准同质路段上预计事故数量模型的方法，这些路段可以通过解释变量的恒定值来描述。使用广义线性建模 (GLM) 方法考虑了几个候选模型进行校准。在考虑了与暴露、几何形状、一致性和环境因素相关的参数以及拟合优度统计数据的统计显著性之后，对 19 个模型进行了排名，并选择了三个模型作为推荐模型。这三个模型中的第一个是基础模型，其长度和交通量是唯一的预测变量；由于这些变量是唯一可能在整个网络中可用的变量，因此该基础模型可以用于经验贝叶斯计算，以对网络进行筛选，对具有安全改进“潜力”的“地点”进行排名。另外两个模型代表了最佳的统计拟合，与暴露、几何形状、一致性和环境因素相关的重要变量有不同的组合。这些多变量模型可以谨慎使用，并结合其他研究的结果，为这些变量在设计应用中的事故修正系数和较小样本的站点的安全评估提供参考，这些变量可以相对容易地组合在一起。
