{ "background": "Transport maintenance depots are critical infrastructure for ensuring road safety and operational continuity in developing economies. Systematic evaluation of interventions to reduce operational risks within these systems is hindered by a lack of robust, context-specific methodological frameworks.", "purpose and objectives": "This Data Descriptor presents a methodological framework designed to quantify risk reduction in transport maintenance depot systems. Its primary objective is to provide a structured, quasi-experimental approach for isolating the causal effect of specific engineering and procedural interventions on key risk metrics.", "methodology": "The framework employs a difference-in-differences quasi-experimental design, comparing treated and control depots over time. The core statistical model is $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\times \\text{Post}t) + \\epsilon_{it}$, where $\\delta$ captures the causal effect. Inference relies on cluster-robust standard errors at the depot level to account for serial correlation.", "findings": "As a Data Descriptor, this paper presents the methodological framework and its associated dataset, not empirical results. The dataset includes pre- and post-intervention measurements for 24 depots on variables such as equipment failure rates, maintenance backlog, and safety incident reports. A preliminary descriptive analysis indicates a baseline mean equipment downtime of approximately 18% across control depots.", "conclusion": "The developed framework provides a rigorous, transferable method for evaluating engineering interventions in complex, real-world depot environments where randomised controlled trials are not feasible.", "recommendations": "Researchers and practitioners are encouraged to apply this framework to evaluate depot upgrades, training programmes, or new maintenance protocols. Future work should validate the framework with implemented interventions and explore extensions to other critical infrastructure systems.", "key words": "quasi-experimental design, infrastructure risk, maintenance engineering, difference-in-differences, causal inference, operational safety", "cont