A geographic information system-based assessment of asphalt road network inventory, condition, and maintenance needs in Jimma City, Ethiopia

Urban road infrastructure is critical for economic development and mobility, yet municipalities in developing nations like Ethiopia face substantial challenges in infrastructure management and maintenance. In Jimma City, the absence of a systematic and complete inventory of its asphalt road network has resulted in inefficient maintenance, accelerated pavement deterioration, and subsequent economic losses. This study addresses this critical gap by developing a comprehensive GIS-based framework to assess the asphalt road network’s inventory, condition, and maintenance needs. A cross-sectional study was conducted within the administrative boundaries of Jimma City, with primary data on road geometry and attributes collected through GPS surveys and direct visual inspection, focusing on distress types, severity, and extent. This information was integrated into a centralized GIS database using ArcGIS Desktop 10.8 version. Pavement conditions were evaluated and categorized, and spatial analysis was employed to identify the distribution of road conditions and distress hotspots. Subsequently, a maintenance needs forecast was developed, prioritizing interventions based on road condition and hierarchy. The assessment revealed that 69.3% of Jimma City’s asphalt road network is in poor (62.9%) or very poor (6.4%) condition, while only 12.1% are in good or very good condition. Potholes were identified as the most dominant distress, affecting 48.6% of the surveyed network. Other significant distress types included wash-away damage (16.4%), edge drop-off (12.6%), and alligator cracking (9.3%). Notably, statistical analysis revealed a severe decoupling between road assets and expenditures, with a near-zero correlation ( r ≤ 0.07) between maintenance costs and all asset metrics, despite strong internal asset valuations ( r ≥ 0.91). This structural disconnect highlights an inefficient, reactive maintenance regime where funding is allocated independently of deterioration rates or asset values. The GIS-based approach proved effective for creating a detailed inventory, evaluating conditions, and forecasting maintenance needs. The findings highlight an urgent need for a shift from reactive to proactive, data-driven maintenance strategies.