基于抽样技术的ITS数据存档策略

ITS Data Archiving Strategy Based-on Sampling Approach

介绍了ITS数据存储的最佳抽样方法,即对原始数据进行质量控制之后,运用误差平方和法(SSE)或互验法(CV)确定出原始ITS数据中最具有代表性的样本,从而降低所需的数据存储空间。通过开发相应的数据处理软件,针对美国得克萨斯州圣安东尼奥TransGuide交通管理中心的数据进行了测试,结果表明:对数据进行处理以后,仅需存储十分之一的原始数据,所得到的最佳样本则包含了最多的原始数据信息,且该样本数据能够满足潜在的不同交通需求。

This paper presents an optimization—ased sampling approach for data archiving. This approach intends to identify the best representative samples of the raw ITS data based on either sum squire error (SSE) or cross validation (CV) while minimize the required storage size. The proposed approach is tested in the case study of TransGuide of San Antonio, Texas. After the proposed sampling approach is applied in the case study, only one tenth of the original data are needed to be stored, while the resulting optimal samples contain the maximum information of the raw data, which are able to meet the potential uses of various transportation purposes.