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鉴于对国家公路系统进行全面评价的方法耗费太大,一般采用抽样方法来评价路面粗糙度,这涉及到很多实际因素.尽管分层随机抽样的方法已经在干线和一般的集散系统得到利用,它仍有很大的发展空间.本文在提出多个针对路面条件的随机变量的基础上,通过改变路面粗糙度分布的基本假定来改进抽样方法,为路面粗糙度的估计建立了一个整体的框架.论文简要介绍了简单随机抽样、分层随机抽样方式,通过分析说明后者能够对路面网络的粗糙度样本提供更全面的估计,而且其偏差更小.论文进一步讨论了以精确估计为基础的良好分层带来的影响.结果表明,在交通网络上可找到一种独特的优化分层方法.根据分析结果,本文定义了该改进分层方法.
In view of the fact that the method of comprehensive evaluation of the national road system is too expensive, sampling methods are generally used to evaluate the road roughness, which involves many practical factors.Although the stratified random sampling method has been used in the trunk and general distribution system, it There is still a lot of room for development.In this paper, based on the random variables for pavement conditions, this paper improves the sampling method by changing the basic assumptions of pavement roughness distribution, and establishes an overall framework for the estimation of pavement roughness. The paper briefly introduces the simple random sampling and stratified random sampling method, which shows that the latter method can provide a more comprehensive estimation of the roughness samples of pavement network, and the deviation is smaller.This paper further discusses that the precision based on the good Stratification.The results show that a unique and optimized stratification method can be found in traffic network.According to the analysis results, this paper defines the improved stratification method.