Comparison of the Effectiveness of Regression Models for the Number of Road Accident Injuries - Phaphan, Wikanda; Sangnuch, Nutnaree; Piladaeng, Janjira (Science & Technology Asia, 2023)
This article aimed to compare the effectiveness of regression models using data on the number of road traffic injuries from the Injury Surveillance System of the Thai Department of Disease Control between 2018 and 2022. The regression models used in this study for the count data include the Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial, and Conway-Maxwell Poisson models. These were compared to find a suitable regression model to predict the number of road traffic injuries. The results show that the negative binomial regression model provides an appropriate regression equation for predicting the number of road traffic injuries because it gives the lowest Akaike information criterion (AIC). Moreover, this model can still be used as a preliminary tool for predicting the number of road accident injuries since it does not rely on many independent variables.