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Abstract
The alarming rate of road traffic accident in the country (Nigeria) is among the most worrisome problems currently facing the nation. Sadly, Nigeria has earned the unenviable distinction of consistently leading all the nations of the world in high road traffic accident and high fatality rate. One of the best ways to understand the occurrence of road accident is to develop accident prediction models which are also standard practices in assessing and improving the safety of our roads. The aim of this study is to conduct a comprehensive evaluation of selected expert systems such as multiple linear regression and artificial neural
network for the modelling and prediction of road accident. The study area is Ugbowo-Lagos Road. A reconnaissance survey was done first to ascertain the geometric characteristic of the road which include; the chainage, the vertical and horizontal curve and the super elevation. Thereafter, secondary data which include road accident data was collected from Federal Road Safety Office at lucky way Benin City. To investigate the qualities of the secondary data, basic preliminary analysis techniques, namely;
outlier detection, homogeneity test, test of normality and autocorrelation test were done. While modelling and prediction of road accident was done with the aid of multiple linear regression and artificial neural networks. From the geometric characteristic of the road under study, it was observed that for a chainage of 11.5 to 13km, the vertical curve was 12.4% while the super elevation was 4.3%. Calculated Cronbach alpha value of 0.900 as observed in the reliability test revealed that the data are reliable and the computed goodness of fit statistics of reliability gave a maximum Guttman coefficient of 88.10% which further confirm the reliability of the data used. With a computed p-value greater than 0.05 for all the independent variables, the null hypothesis of the Dixon test was accepted and it was concluded that the accident data obtained from FRSC is devoid of outliers. In addition, with a centered VIF(Vehicle influence factor) < 10, it was concluded that there is the absence of multicollinearity between the dependent (NAC) and independent variables (NPIV, NPIJ, NPK, NVI). With a computed coefficient of determination (R 2) value of 0.9265, artificial neural network (ANN) was acclaimed better road accident prediction model compare to multiple linear regression model (MLRM) with a computed R2 value of 0.0617. The implication of this findings states that if the R2 value is
lesser than 0.0617 it is will not to work .
network for the modelling and prediction of road accident. The study area is Ugbowo-Lagos Road. A reconnaissance survey was done first to ascertain the geometric characteristic of the road which include; the chainage, the vertical and horizontal curve and the super elevation. Thereafter, secondary data which include road accident data was collected from Federal Road Safety Office at lucky way Benin City. To investigate the qualities of the secondary data, basic preliminary analysis techniques, namely;
outlier detection, homogeneity test, test of normality and autocorrelation test were done. While modelling and prediction of road accident was done with the aid of multiple linear regression and artificial neural networks. From the geometric characteristic of the road under study, it was observed that for a chainage of 11.5 to 13km, the vertical curve was 12.4% while the super elevation was 4.3%. Calculated Cronbach alpha value of 0.900 as observed in the reliability test revealed that the data are reliable and the computed goodness of fit statistics of reliability gave a maximum Guttman coefficient of 88.10% which further confirm the reliability of the data used. With a computed p-value greater than 0.05 for all the independent variables, the null hypothesis of the Dixon test was accepted and it was concluded that the accident data obtained from FRSC is devoid of outliers. In addition, with a centered VIF(Vehicle influence factor) < 10, it was concluded that there is the absence of multicollinearity between the dependent (NAC) and independent variables (NPIV, NPIJ, NPK, NVI). With a computed coefficient of determination (R 2) value of 0.9265, artificial neural network (ANN) was acclaimed better road accident prediction model compare to multiple linear regression model (MLRM) with a computed R2 value of 0.0617. The implication of this findings states that if the R2 value is
lesser than 0.0617 it is will not to work .
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