Queensland University of Technology, School of Civil and Environmental Engineering, Brisbane, Australia.
Advanced Mobility Analytics Group (AMAG), Brisbane, Australia.
Accid Anal Prev. 2022 Oct;176:106795. doi: 10.1016/j.aap.2022.106795. Epub 2022 Aug 13.
The segmentation of highways is a fundamental step in estimating crash frequency models and conducting a before-after evaluation of engineering treatments, but the effects of segmentation approaches on the engineering treatment evaluations are not known very well. This study examined the effects of segmentation approaches on the before-after evaluation of engineering treatments. In particular, this study evaluated four segmentation approaches by applying the Empirical Bayes technique to a dataset for which the ground truth was known. Four segmentation approaches included Highway Safety Manual (HSM), Fixed (kilometre post), Fisher's, and K-means segmentation. This study utilized a 440 km stretch of rural two-lane two-way highway in Queensland, Australia, to prepare a dataset with known ground truth. The treatment under evaluation was a hypothetical treatment, which should yield a crash modification factor (CMF) of 1. For assigning hypothetical treatment, a total of fifteen datasets were prepared, including ten datasets based on the random assignment and five datasets based on the hotspot identification method. Following the before-after evaluation using the Empirical Bayes technique, the results showed that HSM and Fixed segmentation approaches predict the ground truth in both dataset types. From random assignment datasets, the estimated CMFs using HSM, Fixed, Fisher's, and K-means segmentation approaches deviated from the true CMF (i.e., 1) by 2.32 %, 5.30 %, 6.08 %, and 8.62 %, respectively. In the case of hotspots, the corresponding deviations of CMFs were 8.57 %, 9.37 %, 28.84 %, and 35.43 %, respectively. Overall, HSM segmentation best identified the actual treatment effect, followed by the Fixed segmentation. If the variables to define homogeneity for HSM segmentation are limited, then Fixed segmentation can yield reliable crash modification factors from the before-after treatment evaluations than the crash-based segmentation approaches.
高速公路分段是估计碰撞频率模型和进行工程处理前后评估的基本步骤，但分段方法对工程处理评估的影响还不是很清楚。本研究探讨了分段方法对工程处理前后评估的影响。特别是，本研究应用经验贝叶斯技术评估了四种分段方法，该技术适用于具有真实数据的数据集。四种分段方法包括公路安全手册（HSM）、固定（公里桩号）、Fisher 法和 K-均值法。本研究利用澳大利亚昆士兰州一条 440 公里长的农村双车道双向公路准备了一个具有真实数据的数据集。评估的处理方法是一种假设的处理方法，其碰撞修正系数（CMF）应为 1。为了进行假设处理分配，共准备了十五个数据集，其中十个数据集基于随机分配，五个数据集基于热点识别方法。在使用经验贝叶斯技术进行前后评估后，结果表明 HSM 和固定分段方法在两种数据集类型中都能预测真实数据。从随机分配数据集来看，使用 HSM、固定、Fisher 法和 K-均值法分段的估计 CMF 分别偏离真实 CMF（即 1）2.32%、5.30%、6.08%和 8.62%。在热点情况下，CMF 的相应偏差分别为 8.57%、9.37%、28.84%和 35.43%。总体而言，HSM 分段方法最能识别实际处理效果，其次是固定分段方法。如果 HSM 分段方法中定义同质性的变量有限，则与基于碰撞的分段方法相比，固定分段方法可以从前后处理评估中得出可靠的碰撞修正因子。
