This paper presents and evaluates an application of a robust data envelopment analysis (RDEA) multi-criteria model for prioritising safety improvement projects with limited budget and data uncertainty. RDEA can be seen as an extension of classic data envelopment analysis (CDEA) that supports a more flexible and robust project selection by enabling decision-makers to adjust the level of conservation (or robustness) of decision-making against data uncertainty. Data from an existing case study were used to evaluate the performance of the model. The results indicated that the proposed methodology provides a useful tool for adjusting the level of robustness and that the efficiency of the candidate project decreases with the decision-maker's conservativeness and their ranking does not change as the decision-maker becomes more risk-averse. As a comparative study, the proposed approach was compared with incremental benefit–cost analysis and CDEA methods. The results indicated some changes in the list of selected projects considering the uncertainty impacts of data observed according to allocated budgets.

中文翻译：

---

优先考虑公路安全改进项目：一种强大的数据包络分析方法

本文介绍并评估了稳健数据包络分析 (RDEA) 多标准模型在预算有限和数据不确定的情况下对安全改进项目进行优先排序的应用。RDEA 可以看作是经典数据包络分析 (CDEA) 的扩展，它通过使决策者能够针对数据不确定性调整决策的保护（或稳健性）水平来支持更灵活和稳健的项目选择。来自现有案例研究的数据用于评估模型的性能。结果表明，所提出的方法为调整稳健性水平提供了有用的工具，候选项目的效率随着决策者的保守性而降低，并且他们的排名不会随着决策者变得更加规避风险而改变。作为一项比较研究，将所提出的方法与增量收益成本分析和 CDEA 方法进行了比较。结果表明，考虑到根据分配的预算观察到的数据的不确定性影响，选定项目的清单发生了一些变化。