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Author(s): Thauban Omobolaji Omooseti, Sukurat Olabisi Akinjobi, Sanni-Akintunde Awaw Kehinde, Sanusi-Akintunde Aishat Taiwo.

Volume/Issue: Volume 9, Issue 2 (2026)

Page No: 136-163

Journal: African Journal of Mathematics and Statistics Studies (AJMSS)

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Abstract:

This study examines the trends, patterns, and determinants of road accidents in Southwest Nigeria. It adopted a dual-level analytical framework that integrates macro-level time series modelling and micro-level categorical analysis. Quarterly accident data from 2014 to 2024 were analysed to identify long-term trends and seasonal fluctuations, while detailed accident records were assessed to determine the factors associated with accident severity. At the macro level, both Auto-Regressive Integrated Moving Average (ARIMA) and Error–Trend–Seasonal (ETS) models were applied for comparative forecasting, with the ARIMA (2,0,0) model demonstrating superior performance based on lower AIC and error metrics (RMSE, MAE, and MAPE). The results of the forecast showed that there is a continued increase in accidental patterns until 2026. From the microlevel, chi-square tests of independence showed a significant relationship between accident severity and variables, such as "driver gender", "experience", and "collision type", highlighting human and behavioural influences as critical determinants. The combined evidence underscores that accident frequency and severity in the region are shaped more by behavioural factors than environmental conditions. The study concludes with evidence-based policy recommendations emphasising driver reorientation, seasonal safety enforcement, predictive monitoring, and data-driven road safety planning to mitigate future risks.

Keywords:

Road accidents, ARIMA, ETS, Chi-square, Time series forecasting, Accident severity.

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