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Traffic crashes have become the eighth leading cause of mortality for all ages and the leading cause of death among children and young adults aged 5–29 years, resulting in significant social and economic effects. Consequently, integrating road safety into all aspects of the road management process is crucial. This study introduces a robust analytical framework for road maintenance prioritization that integrates road safety metrics with conventional criteria such as pavement condition and traffic volume, to support more strategic and efficient resource allocation. The framework is built upon multicriteria analysis (MA), a widely used practical method for decision-making. However, MA is often criticized for its sensitivity to methodological choices, which can introduce uncertainty into the results. To address this challenge, the proposed framework integrates uncertainty quantification, sensitivity analysis (SA), and uncertainty management. Variance-based SA is utilized in factor prioritization to identify influential and non-influential factors, and factor fixing is applied to the non-influential factors to reduce the complexity of the analysis. A probabilistic exceedance-based ranking method is introduced to ensure robust prioritization under uncertainty. In addition, the framework incorporates a safety impact analysis based on estimated fatalities and serious injuries (FSIs) to guide cutoff selection and objectively assess safety outcomes. The framework is demonstrated using 472.1 km of road sections in Addis Ababa City. The results show that it significantly outperforms conventional approaches, prioritizing more unsafe road sections and potentially preventing up to 175 additional FSIs. The proposed framework can help road agencies make data-driven, safety-conscious decisions that effectively reduce road safety risks while maintaining mobility.
Angelo et al. (Mon,) studied this question.