Modeling the Number of Road Accidents in Kenya Using Time Series Analysis: A SARIMA Approach

Road transport is a vital mode of mobility in Kenya. Despite existing research and numerous interventions, road accidents continue to pose a sig- nificant challenge. This study investigates the trend and future projection of road accidents using a Seasonal Autoregressive Integrated Moving Average (S...

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Bibliographic Details
Published inAsian Journal of Probability and Statistics Vol. 27; no. 8; pp. 123 - 133
Main Authors Wamani, Oliver Siminyu, Musyoki, Michael N.
Format Journal Article
LanguageEnglish
Published 23.08.2025
Subjects
Mathematics
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Summary:Road transport is a vital mode of mobility in Kenya. Despite existing research and numerous interventions, road accidents continue to pose a sig- nificant challenge. This study investigates the trend and future projection of road accidents using a Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Secondary data from the National Transport and Safety Authority (NTSA) from 2019 to 2023 was analyzed using R (version 4.3.1). The optimal model, SARIMA (0,1,1)(1,0,0)[12], demonstrated high forecasting accuracy. The study provides forecasts for the next 24 months and offers insights for policymakers and safety planners to mitigate accident risks.
ISSN:2582-0230
2582-0230
DOI:10.9734/ajpas/2025/v27i8797