Transport maintenance depots (TMDs) play a critical role in ensuring vehicles are operational for public and commercial transport systems in Kenya. A Bayesian hierarchical model was developed to analyse data from multiple depots, accounting for variations in operational costs and vehicle types. Uncertainty quantification was performed through credible intervals. The analysis revealed that the proportion of vehicles requiring repair within a depot can vary significantly by type, with some categories showing up to 40% higher maintenance needs compared to others. Bayesian hierarchical modelling provided insights into cost-effectiveness across different depots and vehicle types in Kenya's transport system. The findings suggest that targeted interventions focusing on high-maintenance vehicle categories could enhance overall efficiency of TMDs. The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Ochieng et al. (Tue,) studied this question.
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