基于粗集理论的交通流丢失数据补齐方法

为了解决交通检测器检测到的数据存在丢失的问题,提出了一种基于粗集理论的丢失数据补齐方法。利用检测到的交通流数据构造信息系统,通过计算扩充可辨识矩阵,并对其进行多次完整化分析,实施丢失数据的补齐,并采用英国南安普敦市的实际检测数据对算法进行了验证。研究结果表明:同一时间段,当仅有一个属性数据丢失时,粗集理论的补齐精度较高,绝对相对误差较小,基本保持在0～5%之间;当不同属性的数据同时丢失时,补齐精度较低,绝对相对误差甚至高达20%;当所有属性数据全部丢失时,补齐精度非常低,可视为无法实现补齐。可见,粗集理论是一种补齐少量丢失数据的有效方法。

Filling method of missing data for traffic flow based on rough set theory

In order to solve the problem of missing data detected from traffic detectors,the algorithm of filling missing data was proposed based on rough set theory,information system was constructed by the detected traffic flow data,distinct matrix was extended and analyzed repeatedly,the missing data of information system were filled,the algorithm was validated with the data of Southampton.Analysis result shows that,at the same period,when only one attribute datum is missed,the filling precision based on rough set theory is higher,the absolute relative error is lower,and basically keeps between 0 and 5%;when different attribute data are missed simultaneously,the filling precision is lower,and the absolute relative error is up to 20%;when all attribute data are missed,the filling is unable to realize,so the algorithm is very effective to fill a spot of missing data.4 tabs,9 figs,12 refs.