Abstract
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.
Highlights
A major world public health problem is related to traffic accidents where the death toll reached 1.35 million in 2016
This study considered a database related to the victims of road accidents reported by the federal police (PF) of Brazil regarding all federal highways in the period ranging from January 1, 2018 to April 30, 2019 covering all states of the federation where the federal police reported for each victim the type of injury and some important factors such as cause of the accident, type of accident, phase of the day, weather condition, type of track, road layout, age of the victim, gender of the victim and type of vehicle
Since the significative covariates affecting the responses y2i = log(x3i/x1i) and y3i = log(x4i/x1i), where x1i = % unharmed, x2i = % minor injuries, x3i = % severe injuries and x4i = % deaths are given by poor pavement, NE region and year, Figures 5, 6, 7 and 8 show the scatter plots associated to each response and covariate from where it is possible to get important interpretations for the compositional multivariate dataset
Summary
A major world public health problem is related to traffic accidents where the death toll reached 1.35 million in 2016. In many emerging countries, including Brazil, this problem gets worse by a number of factors, including low educational attainment and severe infrastructure problems on highways and urban roads (see for example, World Health Organization, 2018; Bhalla et al, 2014; Waiselfisz, 2013; Bahadorimonfared et al, 2013; Bacchieri & Barros, 2011; Jorge et al, 2009; Marín-León et al, 2012; Andrade & Mello-Jorge, 2016; Marín & Queiroz, 2000; Lyons et al, 2008). RICARDO PUZIOL DE OLIVEIRA and JORGE ALBERTO ACHCAR and highways (the fourth largest in the world, CIA World Factbook, Brazil), where 61.1% of all cargo handled in Brazil circulates (Boletim Estatistico do CNT, 2018) This highway system, often containing old highways, with poorly drawn roads, simple and poorly signposted roads, is the main means of transporting cargo and passengers in the country’s traffic. Traffic accidents have been a highlight in external causes of mortality (ICD-10 codes WHO V01 to Y98, 1993), where in the period from 1977 to 1986 the traffic accident mortality rate in Brazil went from 16 to 22/100 thousand leading to a 38% increase (Barros et al, 2003)
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