REPORTED CASES OF ROAD TRAFFIC ACCIDENT IN GOMBE STATE USING BOX-JENKINS MODEL
Abstract
bstract
Road traffic accidents (RTAs) remain a leading cause of fatalities and economic losses in Nigeria, yet predictive modeling for targeted interventions remains underutilized, particularly at the state level. This study focuses on analyzing and forecasting the patterns of reported road traffic accidents in Gombe State, Nigeria, using the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) model.
The research utilizes secondary time-series data on monthly reported accident cases in Gombe State, obtained from the Federal Road Safety Corps (FRSC) archives, spanning a period of [e.g., 10 years, e.g., 2014–2024]. The methodology follows the iterative Box-Jenkins approach, which includes three key stages: model identification (achieving stationarity and analyzing Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots), parameter estimation, and diagnostic checking to ensure model adequacy.
The results indicate that the [e.g., ARIMA (1,1,1) or ARIMA (0,1,1)] model provided the best fit for the historical accident data based on the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values. The model successfully captured the underlying trends and seasonal fluctuations in accident occurrences across major highways and urban centers in Gombe. Forecasts for the subsequent [e.g., 12 months] suggest a [e.g., slight upward trend / seasonal spike during harmattan periods], highlighting critical periods of vulnerability.
The study concludes that the Box-Jenkins model is an effective tool for predicting road traffic accidents in Gombe State. It recommends that the Gombe State Traffic Management Agency and the FRSC adopt this predictive approach for proactive resource allocation, strategic deployment of personnel during high-risk periods, and the design of data-driven public enlightenment campaigns to enhance road safety.
Keywords: Road Traffic Accident, Box-Jenkins Model, ARIMA, Forecasting, Gombe State, Time Series Analysis.
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