The focus of this study is on evaluating transport maintenance depot systems in Senegal, with a particular emphasis on assessing their risk reduction capabilities. Bayesian hierarchical models were employed to analyse data from maintenance depots across different regions of Senegal over the period -. The models incorporate uncertainties and dependencies among depot performance metrics through a multilevel structure that accounts for spatial variability and potential heterogeneities. The analysis revealed significant variations in risk reduction effectiveness between depots located in urban versus rural areas, with an estimated proportion of 65% of depots showing lower risks when compared to the baseline level. This finding underscores the importance of considering regional context in assessing depot performance. Bayesian hierarchical models provided a nuanced understanding of maintenance depot systems' risk reduction capabilities across Senegal, highlighting the need for targeted interventions in rural areas where risk mitigation could be enhanced. Based on the findings, it is recommended that additional resources and support should be directed towards depots in rural regions to improve their effectiveness in managing operational risks. This includes tailored training programmes, technological upgrades, and possibly financial incentives. Bayesian hierarchical models, transport maintenance depots, Senegal, risk reduction, infrastructure management The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Sène et al. (Fri,) studied this question.
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