Public transport maintenance depots in many developing nations face chronic inefficiencies, leading to high vehicle downtime and operational costs. Systematic, evidence-based evaluations of maintenance regimes in these contexts are scarce. This study aimed to quantify the efficiency gains from implementing a structured, data-driven preventive maintenance regime compared to prevailing reactive practices in a West African urban transport setting. A randomised field trial was conducted across multiple depots. Depots were randomly assigned to either a control group (continuing existing practices) or an intervention group implementing a scheduled preventive maintenance system. Efficiency was measured via mean vehicle availability (MVA). The treatment effect was estimated using a linear mixed-effects model: MVA₈₉ = ₀ + ₁ T₈₉ + uⱼ + ₈₉, where T₈₉ is the treatment indicator for depot j, with uⱼ as depot random effects. The intervention significantly increased mean vehicle availability by 17. 3 percentage points (95% CI: 12. 1 to 22. 5; p<0. 001). The reduction in unscheduled corrective maintenance tasks was the primary driver of this improvement. A structured preventive maintenance regime substantially improves depot operational efficiency in this context, demonstrating the viability of randomised trials for infrastructure management research. Transport authorities should adopt scheduled preventive maintenance systems, supported by training and basic data-tracking protocols. Further trials should test scalable implementation models. maintenance optimisation, randomised controlled trial, transport infrastructure, depot management, preventive maintenance, field experiment This paper provides the first application of a randomised field trial methodology to evaluate engineering maintenance systems in African public transport depots, generating robust causal evidence on efficiency interventions.
Mamadou Moustapha Diagne (2010) studied this question.