PDF(Pubmed)
Abstract：
UNASSIGNED: Road traffic accidents are still serious, especially in developing countries. This paper takes China as a typical example of a developing country with rich characteristics related to road traffic safety for analysis.
UNASSIGNED: Temporal, spatial, road traffic accidents and economic information were gathered from the China Statistical Yearbook. Principal components analysis (PCA) was employed to establish a comprehensive indicator to represent road traffic safety based on different types of road traffic accidents information. Pearson correlation analysis and Eta coefficient test were performed to analyze whether time and space characteristics would affect the established indicator. Then the established indicator was introduced as dependent variable while year and regions as independent variables in the mixed linear model (MLM). At last, single-element regression model was built to study the impact of GDP per capita on road traffic safety.
UNASSIGNED: In PCA, the variance explained by the established indicator was 93.993%. The results of Pearson correlation analysis and Eta coefficient test suggested that time and region were both related to the established indicator. MLM showed that the year, the regions and the interaction between them influenced road traffic safety in China significantly. The single-variable regression analysis indicated that, with the increase in GDP per capita, road traffic safety initially decreased and then increased.
UNASSIGNED: Road traffic safety in China was grim and changed greatly between different regions and years. This might be attributed to the yearly economic development and disparities among regions. .
UNASSIGNED: Temporal, spatial, road traffic accidents and economic information were gathered from the China Statistical Yearbook. Principal components analysis (PCA) was employed to establish a comprehensive indicator to represent road traffic safety based on different types of road traffic accidents information. Pearson correlation analysis and Eta coefficient test were performed to analyze whether time and space characteristics would affect the established indicator. Then the established indicator was introduced as dependent variable while year and regions as independent variables in the mixed linear model (MLM). At last, single-element regression model was built to study the impact of GDP per capita on road traffic safety.
UNASSIGNED: In PCA, the variance explained by the established indicator was 93.993%. The results of Pearson correlation analysis and Eta coefficient test suggested that time and region were both related to the established indicator. MLM showed that the year, the regions and the interaction between them influenced road traffic safety in China significantly. The single-variable regression analysis indicated that, with the increase in GDP per capita, road traffic safety initially decreased and then increased.
UNASSIGNED: Road traffic safety in China was grim and changed greatly between different regions and years. This might be attributed to the yearly economic development and disparities among regions. .
摘要：
■道路交通事故仍然严重，尤其是在发展中国家。本文以中国作为典型的具有丰富特征的发展中国家为例,对道路交通安全进行分析。
■时间，空间,道路交通事故和经济信息来自《中国统计年鉴》。基于不同类型的道路交通事故信息,采用主成分分析法(PCA)建立道路交通安全综合指标。通过Pearson相关性分析和Eta系数检验分析时间和空间特征是否会影响所建立的指标。然后在混合线性模型（MLM）中引入已建立的指标作为因变量，而年份和地区作为自变量。最后,建立一元回归模型,研究人均GDP对道路交通安全的影响。
■在PCA中，既定指标解释的方差为93.993%。Pearson相关性分析和Eta系数检验结果表明,时间和地域均与所建立的指标相关。传销表明，这一年，区域之间的相互作用对我国道路交通安全有显著影响。单变量回归分析表明,随着人均GDP的增加，道路交通安全最初下降，然后上升。
■中国的道路交通安全严峻，在不同地区和年份之间发生了很大变化。这可能归因于每年的经济发展和地区之间的差距。.
■时间，空间,道路交通事故和经济信息来自《中国统计年鉴》。基于不同类型的道路交通事故信息,采用主成分分析法(PCA)建立道路交通安全综合指标。通过Pearson相关性分析和Eta系数检验分析时间和空间特征是否会影响所建立的指标。然后在混合线性模型（MLM）中引入已建立的指标作为因变量，而年份和地区作为自变量。最后,建立一元回归模型,研究人均GDP对道路交通安全的影响。
■在PCA中，既定指标解释的方差为93.993%。Pearson相关性分析和Eta系数检验结果表明,时间和地域均与所建立的指标相关。传销表明，这一年，区域之间的相互作用对我国道路交通安全有显著影响。单变量回归分析表明,随着人均GDP的增加，道路交通安全最初下降，然后上升。
■中国的道路交通安全严峻，在不同地区和年份之间发生了很大变化。这可能归因于每年的经济发展和地区之间的差距。.
