交通流异常数据检测研究及实证  

Study of recognizing discrepant traffic data and its validation

作　　者：李成兵[1] 姚琛 

机构地区：[1]内蒙古大学交通学院,呼和浩特010070 [2]烟台市交通运输管理局,山东烟台264000

出　　处：《计算机工程与应用》2013年第20期244-246,共3页Computer Engineering and Applications

基　　金：内蒙古自治区高等学校科学研究项目(No.NJZC13030);内蒙古交通科技项目(No.NJ2012-2)

摘　　要：针对道路交通系统实时交通流数据普遍存在的异常现象,提出一种基于最小二乘支持向量机的交通异常数据检测方法。运用相空间重构技术,将时间序列数据转换为矢量数据。运用训练数据构建基于最小二乘支持向量机的回归估计模型,通过计算实际值与预测值之间的残差来判别异常数据。以成都市一环路北二段进行实证研究,并与传统检测方法比较分析,结果证实该检测方法的有效性。Focusing on the phenomenon that the real traffic databases are highly susceptible to noisy, missing, and inconsistent data, a model is built based on least square Support Vector Machine （SVM） to detect the traffic abnormal data. It transforms the time series data into vector data with the method of phase-space reconstruction. Based on the SVM, it establishes the regression analyse model with the training data before distinguishing abnormal data by calculating the residual error between actual value and predictive value. With the empirical research in Bei Er Duan of the First Circle Road of Chengdu and the comparative analysis with the traditional detection method, the results Drove the effectiveness of the oronosed detection method

关 键 词：交通流 异常数据 支持向量机 最小二乘法 

分 类 号：U491.14[交通运输工程—交通运输规划与管理]