Sensitivity Analysis of New Simulation-Based Conflict Metrics
Sensitivity Analysis of New Simulation-Based Conflict Metrics
复制标题
DOI：
10.1016/j.ssci.2015.09.023
复制
发表时间：
2016-02
期刊：
影响因子：
--
通讯作者：
Chen Wang;N. Stamatiadis
中科院分区：
文献类型：
--
作者：
Chen Wang;N. Stamatiadis
冲突倾向度量（CPM）和总体冲突倾向度量（ACPM）是最近提出的两种基于模拟的冲突度量作为替代安全措施。这两个指标是通过结合驾驶员反应时间 (RT) 和车辆最大制动率 (MABR) 分布的随机过程得出的。本文通过改变 RT 分布的参数（即平均值和标准差），对这两个指标进行了敏感性分析。 RT 均值和标准差都会影响此处检查的三种冲突类型（即交叉、追尾和变道）的 CPM 估计，并且影响因冲突类型而异，表明在开发基于模拟的冲突指标时需要仔细评估和考虑 RT 分布和冲突类型。基于现场数据的敏感性分析表明，不同的 RT 分布会对 ACPM 产生影响，并可能影响 ACPM 在识别相对安全性及其与实际事故的相关性方面的可靠性。这里的分析确定了不同冲突类型可能存在不同的“现实”RT 分布，并且建议的值被认为是合理的或与先前的发现一致。 ACPM 已被证明有潜力使用更合适的 RT 分布进一步提高其准确性。然而，缺乏针对特定冲突类型的专用RT分配，特别是基于多种因素的联合RT分配，阻碍了ACPM的改进。总的来说，敏感性分析通过提供合理的发现以及指出有趣且重要的未来研究方向（例如找到不同冲突类型的实际 RT 分布），显示了推导 CPM 和 ACPM 过程的优势。
The Conflict Propensity Metric (CPM) and the Aggregate Conflict Propensity Metric (ACPM) are two simulation-based conflict metrics recently proposed as surrogate safety measures. The two metrics are derived through a stochastic process incorporating distributions of driver reaction time (RT) and vehicle maximum braking rates (MABR). This paper presents sensitivity analyses on the two metrics, by altering the parameters (i.e. mean and standard deviation) of RT distributions. Both RT mean and standard deviation affect the estimates of CPM for the three conflict types examined here (i.e. crossing, rear-end and lane change), and the impacts vary by conflict types, indicating the need of carefully evaluating and considering both RT distributions and conflict types when developing simulation-based conflict metrics. A sensitivity analysis based on field data showed that different RT distributions have an impact on ACPM and could affect the reliability of ACPM in identifying relative safety and its correlation with actual crashes. The analysis here identified the potential existence of different “realistic” RT distributions for different conflict types and the suggested values are considered reasonable or consistent with prior findings. ACPM has proved to have potential to further improve its accuracy using more suitable RT distributions. However, dedicated RT distributions for specific conflict types are lacking, especially a joint RT distribution conditional various factors, impeding the improvement of ACPM. In general, sensitivity analyses have shown the strength of the process of deriving CPM and ACPM by providing reasonable findings, as well as pointing out interesting and important future research directions, such as finding actual RT distributions for different conflict types.