Prioritising highway safety improvement projects: a robust data envelopment analysis approach

摘要

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.