Abstract
Purpose:The increasing number of deaths due to road traffic accidents(RTAs)has attracted global attention.However,the influence of road types is rarely considered in the study of RTAs.This study evaluates the influence of different road types in RTAs in northern Guizhou to provide a basis for the formulation of evidence-based policies and measures.Methods:We obtained the data from the Zunyi Traffic Management Data Platform for the years 2009-2018.The mortality rates of RTAs were calculated.Descriptive methods and Chi-square tests were used to analyze the characteristics of road traffic collisions on different road types.We also examined the associations be-tween the mortality rate per 10,000 vehicles and the growth of per capital gross domestic product(GDP)with Spearman's rank correlation analysis.According to the passing volume and the infrastructure,we defined different types of roads,like administrative road,functional road,general urban road and urban expressway.Results:In 2012,the traffic mortality rate of administrative roads was 8.9 per 100,000 people,and the mortality rate of functional roads was 7.4 per 100,000 people,which decreased in 2018 to 6.1 deaths per 100,000 people and 5.2 deaths per 100,000 people,respectively.The mortality rate per 10,000 vehicles reached the highest level in 2011(28.8 per 10,000 vehicles and 22.5 per 10,000 vehicles on adminis-trative and functional roads,respectively).The death rate of county roads was the highest among administrative roads(x2 = 17.389,p ＜ 0.05)and that of fourth-class roads was the highest among functional roads(x2 = 21.785,p ＜ 0.05).The mortality rate per 10,000 vehicles was negatively correlated with per capital GDP.Conclusion:Although our research shows that RTAs in northern Guizhou have steadily declined in recent years,the range of decline is relatively small.Many measures and sustainable efforts are needed to control road traffic death and accelerate the progress in road traffic safety in northern Guizhou.
基金项目
study was supported by the National Natural Science Foundation of China(NSFC(81760233)
Science and Tech-nology Project of Guizhou Province([2020]4Y149)
Science and Tech-nology Project of Guizhou Province([2019]5661)
NETL
NSTL
维普
万方数据