基于支持向量聚类和模糊粗糙集的交通流数据修复方法  

Missing Traffic Flow Data Imputation Based on Support Vector Clustering and Fuzzy Rough Set

作　　者：朱世超 王骋程 王超 刘隆 张润芝 王浩 ZHU Shichao;WANG Chengcheng;WANG Chao;LIU Long;ZHANG Runzhi;WANG Hao(Shandong Hi-speed Infrastructure Construction Co.LTD.,Jinan 250000,China;Shandong Provincial Communications Planning and Design Institute Group Co.LTD.,Jinan 250000,China;Shandong Hi-speed Jiwei Expressway Co.LTD.,Jinan 250000,China)

机构地区：[1]山东高速基础设施建设有限公司,济南250000 [2]山东省交通规划设计院集团有限公司,济南250000 [3]山东高速济潍高速公路有限公司,济南250000

出　　处：《森林工程》2023年第1期157-165,共9页Forest Engineering

基　　金：山东省交通运输科技计划项目(2021B68);山东省自然科学基金青年基金(ZR202103040494)。

摘　　要：为解决受天气影响、探测器故障和人为错误等多种原因造成的交通流数据丢失问题,提出一种基于模糊粗糙集理论的交通流数据补缺方法,将支持向量聚类与模糊粗糙集结合进行交通流数据的分类,并结合模糊神经网络和遗传算法进行数据补齐。该方法对支持向量聚类参数,聚类大小和加权因子进行优化,并估计缺失值。研究结果表明所提出的混合方法具有足够且合理的数据修复性能,与模糊神经网络等估算模型的结果对比表明,该模型的数据修复效果优于其他对比模型。In order to solve the problems of missing traffic flow data caused by various reasons such as weather effect, detector faults and artificial error etc., this paper proposed a method based on the fuzzy rough set theory to impute missing traffic flow data. We combined the support vector clustering and fuzzy rough set to classify traffic flow data, and then combined the fuzzy neural network and genetic algorithm to impute missing data. The method optimized the support vector clustering parameters, cluster size and weighting factor, and estimated the missing values. The results of the study showed that the proposed novel hybrid method produced sufficient and reasonable data imputation performance results. Compared with the results of fuzzy neural network and other estimation models, the data imputation effect of this model was better than other comparison models.

关 键 词：模糊粗糙集 模糊神经网络 支持向量聚类 交通流 数据修复 

分 类 号：S[农业科学]