Application of Generalized Poisson Regression for the development of modeling motorcycle accidents based on rider characteristics

Abstract

Abstract Motorcycles are a mode of transportation that has experienced many traffic accidents in developing countries. This incident can occur intentionally or not and cause great loss of life and property. Therefore, this work aims to develop a model of the number of motorcycle accidents based on driver characteristics using Generalized Poisson Regression in East Java Province, Indonesia. The modeling can identify the relationship between the number of motorcycle accidents with variables that contribute to accident based on driver characteristics. This characteristic is a combination of socioeconomic factors and movements. Research analysis includes descriptive statistics and accident modeling using Generalized Poisson Regression Models. Model parameter values are estimated using the Maximum Likelihood Estimation (MLE) approach. The optimal solution for parameter estimation uses the Newton-Raphson algorithm. The results found two models and then selected the best model. Based on the parameter significance test, the population density, the percentage of low education, the gender ratio, and the percentage of accident perpetrators without a driver’s license significantly impact the number of motorcycle accidents in East Java Province.