Statistical Modelling of Road Traffic Accidents: Pattern and Trend in Kogi State, Nigeria

Abstract

Road traffic accident (RTA) is defined as unplanned car crash that causes injuries, fatalities, and property damage. In order to better understand the pattern and trend of road traffic accident in Kogi state, Nigeria, we statistically modeled RTA data collected from Federal Road Safety Corps, Lokoja from January 2017 to December 2021. The data consisted of monthly RTA types and outcomes. The RTA types considered were fatal, serious and minor while the RTA outcomes were death, injury and no injury. Time series modeling was adopted for modeling and predicting the accident rates while Pearson correlation was used to determine the degree of relationship between RTA types and outcomes. Results showed that there were steady fluctuations in the patterns of RTA types and outcomes between February and October while there were upward trend in RTA from November to January. The augmented Dickey-Fuller test showed that RTA series was stationary and out of 10 candidate models obtained using ACF and PACF plots, the best model suitable for forecasting RTA rate was found to be ARIMA(1,0,1) using mean absolute deviation (MAD) and mean square error (MSE) selection criteria. In order to estimate the parameters of the model, the Shapiro-Wilk test was conducted on the RTA values and its residuals to confirm normality. Since p < 0.05 in both cases, they were both found to be non-normal, then the least absolute deviation (LAD) estimator was used for estimation. This gives rise to Y_t = 29.0574 + 0.492151X_t-1 + 0.99994e_t-1 + ε_t as the best fitted model, which was found to be statistically significant at α=0.05. The estimated model was used to forecast RTA for 30months with 95percent confidence level and result showed that the forecast were good and there will continually be occurrence of RTA nearly every month and there will be higher RTA rates between November and January. The result of the Pearson correlation showed that fatal accident were 71percent more likely associated to death while serious accident were 61percent more likely associated to injury.