A MULTI-CRITERIA DECISION FRAMEWORK FOR ROAD INFRASTRUCTURE MAINTENANCE PRIORITIZATION IN MUNICIPAL MANAGEMENT: A CASE STUDY OF THE ASEER REGION, SAUDI ARABIA
Keywords:
Multi-criteria decision analysis; road asset management; maintenance prioritization; municipal governance; Saudi Vision 2030; pavement condition indexAbstract
Road-maintenance prioritization in large municipal networks is often constrained by limited funding, ageing infrastructure, and the absence of transparent decision-making systems, resulting in inefficient allocation of resources. This study aimed to develop a transparent multi-criteria decision-analysis framework for prioritizing road-maintenance investment in the Aseer Region of Saudi Arabia. Specifically, the study examined the structural composition of the municipal road network, evaluated the limitations of a baseline prioritization model, and demonstrated the effectiveness of a four-criterion framework grounded in the Analytic Hierarchy Process (AHP) and weighted linear combination (WLC).
The study adopted a GIS-based multi-criteria evaluation methodology using official inventory data from the Aseer Municipality geospatial platform. The population comprised 41,744 road segments covering 20,851 km, all of which were analysed. Data were standardized, weighted through pairwise comparison, and aggregated into composite priority scores, while sensitivity analysis was conducted to assess the effects of alternative weight sets.
Findings revealed a significant mismatch between road count and maintenance value, with local roads constituting 77% of segments but accounting for only 46.4% of maintenance value, whereas regional and arterial roads concentrated disproportionately high per-asset criticality. The baseline model demonstrated weak discriminatory power, placing about 91% of segments within the two lowest priority bands. However, the proposed framework successfully re-ordered priorities by emphasizing strategic and economic consequences rather than network ubiquity.
The study concluded that the framework provides a transparent, auditable, and policy-aligned approach to municipal road-maintenance prioritization. It recommended the integration of geocoded condition data, formal expert weight elicitation, and machine-learning condition prediction to support continuous segment-level deployment.
