基于强空间集成的交通状态判别方法

为研究服务于交通诱导的路网宏观交通状态判别模型,依据集成学习理论,基于强空间集成,提出一种交通状态判别方法。采用K-近邻规则寻找与待判别交通流数据相似的一组训练样本,构成待判别数据的邻域,挖掘交通状态学习器的强空间,进而输出交通运行状态等级标签。采用交通状态混淆矩阵,查全率、查准率等进行实验,结果表明该方法能够较准确地判断路网交通状态,满足交通状态判别的实际应用。...

Full description

Saved in:
Bibliographic Details
Published in计算机工程 Vol. 43; no. 11; pp. 297 - 302
Main Author 刘擎超 蔡英凤 江浩斌 何友国 陈龙
Format Journal Article
LanguageChinese
Published 江苏大学汽车工程研究院,江苏镇江,212013 2017
Subjects
交通状态判别
学习器
强空间
智能交通
混淆矩阵
强空间
confusion matrix
交通状态判别
learner
automatic condition identification
strong space
智能交通
intelligent transportation
混淆矩阵
学习器
Online AccessGet full text
ISSN1000-3428
DOI10.3969/j.issn.1000-3428.2017.11.048

Cover

More Information
Summary:为研究服务于交通诱导的路网宏观交通状态判别模型,依据集成学习理论,基于强空间集成,提出一种交通状态判别方法。采用K-近邻规则寻找与待判别交通流数据相似的一组训练样本,构成待判别数据的邻域,挖掘交通状态学习器的强空间,进而输出交通运行状态等级标签。采用交通状态混淆矩阵,查全率、查准率等进行实验,结果表明该方法能够较准确地判断路网交通状态,满足交通状态判别的实际应用。
Bibliography:In order to research the road network macroscopic automatic condition identification model which is used in traffic guidance,according to the ensemble learning theory,this paper proposes an automatic condition identification method based on strong space integration. It uses K-nearest neighbor rule to find a set of training samples similar to the traffic data to be discriminated,the neighborhood of the data to be discriminated,and excavates the strong space of the automatic condition learner,then outputs the automatic condition level label. Automatic condition confusion matrix, recall rate and precision rate are used in the experiment. Experimental results show that this model can accurately identify the automatic condition of the road network and meet the practical application of the automatic condition identification.
31-1289/TP
LIU Qingchao, CAI Yingfeng, JIANG Haobin, HE Youguo, CHEN Long （ Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, Jiangsu 212013, China）
intelligent transportat
ISSN:1000-3428
DOI:10.3969/j.issn.1000-3428.2017.11.048