Time-Series Forecasting Model for Adoption Rates in Transport Maintenance Depots, Uganda

The adoption rates of transport maintenance systems in Ugandan depots have been observed to vary over time, necessitating a systematic approach for forecasting future trends. A time-series forecasting model was developed based on historical data from to , incorporating autoregressive integrated moving average (ARIMA) methodology with uncertainty quantified through robust standard errors. The ARIMA model indicated a significant upward trend in adoption rates over the study period, suggesting a growing interest in adopting new maintenance systems. This study provided insights into forecasting future adoption patterns, offering valuable guidance for resource allocation and planning within Ugandan transport sectors. Transport authorities should consider implementing proactive strategies to support the adoption of innovative maintenance technologies based on our findings. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.