Articles

Motor Vehicle Growth in Guyana (2000–2025): Statistical Trends, Forecasting, And Infrastructure Implications

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Motor vehicle registrations are a key indicator of transport demand, economic development, and infrastructure pressure. This study examines long-term trends in motor vehicle registrations in Guyana from 2000 to 2025 and generates forecasts for 2026–2030 using time-series modelling, including an ARIMA (0,1,0) model with drift.

The results show a strong and sustained increase in total vehicle registrations over the study period, rising from relatively low levels in the early 2000s to 38,346 vehicles in 2025. Growth is characterised by marked year-to-year volatility but a clear upward structural trend, particularly after the mid-2010s and the post-2020 period. Private cars remain the dominant category throughout, followed by motorcycles, both of which drive the overall expansion of motorisation. Commercial and specialised vehicle categories such as lorries, vans, buses, and hire cars show more moderate and stable growth patterns, reflecting their close link to economic activity.

Correlation analysis reveals consistently strong positive relationships across vehicle categories, indicating broad-based expansion of motorisation rather than isolated growth. Forecast results suggest that total vehicle registrations will continue to rise steadily, increasing from approximately 39,666 in 2026 to 44,948 in 2030. Diagnostic tests confirm the adequacy of the ARIMA model, with residuals behaving as white noise and acceptable forecast accuracy.

Overall, the findings indicate structurally persistent motorisation in Guyana, with significant implications for road infrastructure capacity, transport planning, and sustainable mobility policy.

Hybrid Bootstrap–LSTM Model for Probabilistic Sea Level Rise Prediction

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Sea level rise poses increasing risks to coastal regions, highlighting the need for accurate and reliable forecasting methods. This study proposes a probabilistic sea level forecasting framework by integrating a Long Short-Term Memory (LSTM) model with the Moving Block Bootstrap (MBB) technique. The LSTM model is used to capture nonlinear temporal dependencies in sea level time-series data, while the bootstrap approach is employed to quantify prediction uncertainty through probabilistic forecasting. The LSTM model achieved high deterministic prediction accuracy with an MSE of 2.11 × 10!”, RMSE of 0.00459, MAE of 0.00356, and MAPE of 0.34%. The proposed hybrid MBB–LSTM model generates probabilistic forecasts with a 95% confidence interval, resulting in an MSE of 0.01155, RMSE of 0.10749, MAE of 0.08370, and MAPE of 8.99%. Forecast results indicate relatively stable sea level variability until 2026 with an estimated rising trend of approximately 7.44 mm per year. The proposed hybrid framework provides a more informative prediction approach by combining deep learning with bootstrap-based uncertainty estimation, which is valuable for coastal risk assessment and climate adaptation planning.