COMPOSITIONAL STATISTICAL MODELS UNDER A BAYESIAN APPROACH: AN APPLICATION TO TRAFFIC ACCIDENT DATA IN FEDERAL HIGHWAYS IN BRAZIL

ABSTRACT

This study considers the use of a composicional statistical model under a Bayesian approach using Markov Chain Monte Carlo simulation methods applied for road traffic victims ocurring in federal roads of Brazil in a specified period of time. The main motivation of the present study is based on a database with information on the injury severity of each person involved in an accident occurred in federal highways in Brazil during a time period ranging from January, 2018 to April, 2019 reported by the federal highway police office of Brazil. Four types of events associated with each injured person (uninjured, minor injury, serious injury and death) are grouped for each state of Brazil in each month characterizing compositional multivariate data. Such kind of data requires specific modeling and inference approaches that differ from the traditional use of multivariate models assuming multivariate normal distributions.The proportion events associated to the accidents (uninjured, minor injuries, serious injuries and deaths) are considered as a sample of vectors of proportions adding to a value one together with some covariates such as pavement conditions in each province, regions of Brazil, months and years that may affect the severity of the injury of each person involved in an accident. From the obtained results, it is observed that the proportions of serious accidents and deaths are affected by some covariates as the different regions of Brazil and years.

Keywords:
accident victims; types of injuries; deaths; federal highways; compositional data; Bayesian approach