Application of Statistical Models: Parameters Estimation of Road Accident in Bangladesh

Abstract

Road traffic accident is the most unwanted situation and one of the significant reasons for death and injuries of people of all ages worldwide. Statistical analysis of highway-related accidents is of utmost importance to evaluate the severity of the problem and speed up taking decision toward its attenuation. In this research, three statistical models, namely negative binomial, gamma regression and Poisson regression model, were developed by using the statistical software IBM SPSS 25.0 to determine the various contributing factors which were significantly responsible for road accidents occurring in Bangladesh. The parameters selected to develop each of the models are collision type, junction type, vehicle type, weather conditions, and driver behaviors. The goodness of fit test of the Poisson regression model indicates that there was an overdispersion problem in the accident data. The value of deviance and Pearson Chi-square of negative binomial regression analysis were found to be approximately 1.00. This determination declines that the negative binomial regression model was the best fit for the data. The gamma regression analysis was selected due to the handle under dispersion data. The significant contributing factors for road traffic accident occurring in this city based on the appropriate model were head-on and sideswipe as a collision type; T junction and cross junction as a junction type; bus and truck as a vehicle type; high speed and loss of control as a driver behavior. The weather condition is the only factor that has no significant contribution to road traffic accident occurrence.