Budapest University of Technology and Economics, Budapest H-1111, Hungary.
Accid Anal Prev. 2019 Jul;128:1-7. doi: 10.1016/j.aap.2019.03.002. Epub 2019 Apr 4.
Indicating road safety-related aspects in the phase of planning and operating is always a challenging task for experts. The success of any method applied in identifying a high-risk location or black spot (BS) on the road should depend fundamentally on how data is organized into specific homogeneous segments. The appropriate combination of black spot identification (BSID) method and segmentation method contributes significantly to the reduction in false positive (a site involved in safety investigation while it is not needed) and false negative (not involving a site in safety investigation while it is needed) cases in identifying BS segments. The purpose of this research is to study and compare the effect of methodological diversity of road network segmentation on the performance of different BSID methods. To do this, four commonly applied BS methods (empirical Bayesian (EB), excess EB, accident frequency, and accident ratio) have been evaluated against four different segmentation methods (spatial clustering, constant length, constant traffic volume, and the standard Highway Safety Manual segmentation method). Two evaluations have been used to compare the performance of the methods. The approach first evaluates the segmentation methods based on the accuracy of the developed safety performance function (SPF). The second evaluation applies consistency tests to compare the joint performances of the BS methods and segmentation methods. In conclusion, BSID methods showed a significant change in their performance depending on the different segmentation method applied. In general, the EB method has surpassed the other BSID methods in case of all segmentation approaches.
在规划和运营阶段指出与道路安全相关的方面对于专家来说始终是一项具有挑战性的任务。任何用于识别道路上高风险地点或黑（Black Spot，BS）的方法的成功都应基本取决于数据如何组织成特定的同质段。BS 识别（Black Spot Identification，BSID）方法和分段方法的适当组合对减少假阳性（安全调查涉及一个不需要的地点）和假阴性（安全调查不涉及一个需要的地点）案例在识别 BS 段方面具有重要意义。本研究的目的是研究和比较道路网络分段的方法多样性对不同 BSID 方法性能的影响。为此，评估了四种常用的 BS 方法（经验贝叶斯（Empirical Bayesian，EB）、超额 EB、事故频率和事故比例）与四种不同的分段方法（空间聚类、恒定长度、恒定交通量和标准公路安全手册分段方法）的性能。采用两种评估方法来比较方法的性能。该方法首先根据开发的安全绩效函数（Safety Performance Function，SPF）的准确性评估分段方法。第二种评估应用一致性测试来比较 BS 方法和分段方法的联合性能。总之，BSID 方法的性能根据应用的不同分段方法有显著变化。一般来说，EB 方法在所有分段方法中都超过了其他 BSID 方法。
