Analyzing Motorcycle Accident Frequency Using Generalized Poisson Distributions

Abstract

Motorcycle accidents in East Java are more common than accidents in other modes of transportation. In addition to the many motorcycle users today, human, environmental, and road factors are considered the highest causes of these accidents. The study's goal is to find the best model of the Generalized Poisson Family Distribution (GPR), namely Lagrangian Poisson Regression (LPR) and to construct a model that will quantify the frequency of motorcycle accidents in East Java. Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC) criteria are the model comparison methods used in this research. The selection was also made based on the model's exponential coefficient with a 95% CI to further deepen the selection results obtained. In addition, the paired samples test was performed to determine the degree of dissimilarity between the outcomes produced by the developed model and the actual data. The best performance model is applied to identify the characteristics or factors highly involved in motorcycle accidents. The research uses secondary data from related agencies, namely the East Java Regional Police, especially the traffic accident unit, and East Java BPS, for 38 cities and districts in 2021. The numerical optimization method used is the iteratively reweighted least squares (IRLS) algorithm, assisted by R Studio software. The study findings show that LPR is the most efficient and exact approach for modeling the frequency of motorcycle accidents. Meanwhile, the percentage of teenagers (X1), the frequency of motorized vehicles (X3), and the average annual rainfall (X5) have a considerable impact on accident occurrence. This research has an important contribution, especially in the field of transportation modeling and designing appropriate strategies to reduce the frequency of motorcycle accidents.