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首页> 外文期刊>Accident Analysis & Prevention >Bayesian methodology incorporating expert judgment for ranking countermeasure effectiveness under uncertainty: Example applied to at grade railroad crossings in Korea
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Bayesian methodology incorporating expert judgment for ranking countermeasure effectiveness under uncertainty: Example applied to at grade railroad crossings in Korea

机译：贝叶斯方法结合专家判断，对不确定性下的对策有效性进行排名：以韩国平交道口为例

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摘要

Transportation professionals are sometimes required to make difficult transportation safety investment decisions in the face of uncertainty. In particular, an engineer may be expected to choose among an array of technologies and/or countermeasures to remediate perceived safety problems when: (1) little information is known about the countermeasure effects on safety; (2) information is known but from different regions, states, or countries where a direct generalization may not be appropriate; (3) where the technologies and/or countermeasures are relatively untested, or (4) where costs prohibit the full and careful testing of each of the candidate countermeasures via before-after studies. The importance of an informed and well-considered decision based on the best possible engineering knowledge and information is imperative due to the potential impact on the numbers of human injuries and deaths that may result from these investments. This paper describes the formalization and application of a methodology to evaluate the safety benefit of countermeasures in the face of uncertainty. To illustrate the methodology, 18 countermeasures for improving safety of at grade railroad crossings (AGRXs) in the Republic of Korea are considered. Akin to "stated preference" methods in travel survey research, the methodology applies random selection and laws of large numbers to derive accident modification factor (AMF) densities from expert opinions. In a full Bayesian analysis framework, the collective opinions in the form of AMF densities (data likelihood) are combined with prior knowledge (AMF density priors) for the 18 countermeasures to obtain 'best' estimates of AMFs (AMF posterior credible intervals). The countermeasures are then compared and recommended based on the largest safety returns with minimum risk (uncertainty). To the author's knowledge the complete methodology is new and has not previously been applied or reported in the literature. The results demonstrate that the methodology is able to discern anticipated safety benefit differences across candidate countermeasures. For the 18 at grade railroad crossings considered in this analysis, it was found that the top three performing countermeasures for reducing crashes are in-vehicle warning systems, obstacle detection systems, and constant warning time systems.
机译：有时，面对不确定性，有时要求运输专业人员做出艰难的运输安全投资决策。特别是，在以下情况下，可以期望工程师在一系列技术和/或对策中进行选择，以补救感知到的安全问题：（1）关于该对策对安全的影响知之甚少； （2）信息是已知的，但来自不同的地区，州或国家，在这些地区或地区可能无法直接进行概括； （3）技术和/或对策未经测试，或（4）成本禁止通过前后研究对每种候选对策进行全面且仔细的测试。必须基于最佳的工程知识和信息做出明智且经过深思熟虑的决策，因为这些投资可能对人身伤害和死亡人数造成潜在影响，因此至关重要。本文描述了在不确定性面前评估对策安全利益的方法的形式化和应用。为了说明该方法，考虑了大韩民国改善18个平交道口（AGRX）安全的18个对策。类似于旅行调查研究中的“陈述偏好”方法，该方法应用随机选择和大量定律，以从专家意见中得出事故修正因子（AMF）密度。在完整的贝叶斯分析框架中，将针对18种对策的AMF密度（数据似然性）形式的集体意见与先验知识（AMF密度先验）相结合，以获得AMF的``最佳''估计值（AMF后可信区间）。然后根据最大的安全收益和最小的风险（不确定性）对对策进行比较并提出建议。据作者所知，完整的方法是新的，以前在文献中尚未应用或报道过。结果表明，该方法能够识别候选对策之间预期的安全利益差异。对于此分析中考虑的18个平交道口，发现减少事故的前三项对策是车载警告系统，障碍物检测系统和恒定警告时间系统。

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