Time Series Analysis of Road Traffic Accidents in Ghana
Frank B. K. Twenefour, Emmanuel Ayitey, Justice Kangah, Lewis Brew
Asian Journal of Probability and Statistics · pp. 12–20 · Published 4 Mar 2021
10.9734/ajpas/2021/v11i230262Abstract
This study uses Time Series models to predict the annual traffic accidents in Ghana. The traffic accidents data spanning from January 1990 to December 2019 was used. The Box-Jenkins model building strategy was used. The Augmented Dickey Fuller (ADF) test showed that the accident data was stationary. Three ARMA models were suggested based on the ACF and PACF plots of the differenced series, these were ARMA (0,0), ARIMA (1,0), and ARMA (2,0). The model with the smallest corrected Akaike Information Criteria (AICs) and Bayesian Information Criteria (BIC) was chosen as the best model. The Ljung-Box statistics among others were used in assessing the quality of the model. ARMA (1,0) was the best model for the Ghana annual Traffic Accident data. The results showed that, from January to July, it would be difficult to make accurate estimates of the number of road incidents for the years leading up to 2020. This was due to the fact that the white noise process values were statistically independent at various times.
Cited by 13
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Showing 8 of 13 known citations — external sources report more than can currently be individually listed.
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