Improvements in estimating a fatal accidents model formed by an Artificial Neural Network

摘要

The Smeed Equation (SE) is the first model being used to improve the estimation of the number of dead in accidents that consist of the independent variables of population and number of vehicles and the dependent variable of the number of dead. In this study, the population variable in the SE is replaced with the number of drivers. At first, the SE is made suitable for USA data and the Revised SE is obtained. Then the coefficients are calculated again by the replacement of the number of drivers with population and the Improved SE is obtained. Afterwards, Artificial Neural Network (ANN) models are formed in both variable groups of population and number of drivers. The best ANN model, whose inputs are the number of vehicles and drivers, has 19 neurons, a tan-sig transfer function and a Levenberg-Marquardt training algorithm. In the comparison of ANN models and SE models, the value of R2 increases from 0.8906 to 0.9695 and the value of mean square errors (MSEs) decreases from 87,503 to 39,310. As a result the replacement of the number of drivers variable with population has a contribution in the estimation of the number of dead in vehicle accidents. This study showed that use of the number of drivers instead of the population in the number of dead prediction can be improved with the accuracy of the proposed models. Moreover, ANN models can be used to predict the number of dead in traffic accidents with a high correlation coefficient and a low MSE according to the SE and loglinear regression methods.