{ "background": "Transport maintenance depots are critical infrastructure for road network functionality, yet systematic, data-driven frameworks for evaluating their cost-effectiveness are lacking, particularly in developing economies. Existing approaches often rely on deterministic analyses that fail to account for operational variability and hierarchical data structures inherent in depot systems.", "purpose and objectives": "This study develops and applies a novel Bayesian hierarchical model to evaluate the cost-effectiveness of transport maintenance depot systems. The objective is to provide a robust methodological framework that quantifies efficiency while formally incorporating uncertainty and multi-level data dependencies.", "methodology": "A Bayesian hierarchical model was constructed, integrating cost, throughput, and asset condition data from a network of depots. The core model structure is $\\text{log}(\\text{Cost}{ij}) = \\alpha + \\beta \\text{Throughput}{ij} + uj + \\epsilon{ij}$, where $uj \\sim N(0, \\sigma^2u)$ represents random effects for depot $j$ and $\\epsilon{ij}$ is the observation-level error. Posterior distributions were estimated using Markov Chain Monte Carlo simulation.", "findings": "The model identified substantial heterogeneity in depot performance, with the random effects variance $\\sigma^2u$ having a 95% credible interval of [0.18, 0.42]. Approximately 30% of depots were found to be operating below the cost-effectiveness frontier, with inefficiency primarily driven by spare parts inventory management practices rather than labour or fuel costs.", "conclusion": "The proposed Bayesian hierarchical model provides a statistically robust framework for cost-effectiveness evaluation, explicitly modelling uncertainty and hierarchical data structures common in infrastructure systems. It moves beyond point estimates to offer a probabilistic assessment of depot performance.", "recommendations": "Infrastructure managers should adopt probabilistic, hierarchical modelling for asset performance evaluation. Resource allocation for depot improvements should prioritise inventory management systems, informed by the model's identification of key cost drivers.", "key words": "Bayesian statistics, infrastructure management, maintenance dep