| 基于改进的SVM的交通信息融合方法研究 |
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| 引用本文： | 郭晓妮.基于改进的SVM的交通信息融合方法研究[J].物流技术,2012,31(1):95-98,150. |
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| 作者姓名： | 郭晓妮 |
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| 作者单位： | 中冶(北京)交通科技发展有限公司,北京,100176 |
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| 基金项目： | 国家高技术研究发展计划(863计划)“分布式交通系统信息互操作技术”(2007AA11E226) |
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| 摘 要： | 在对交通数据特性分析的基础上,提出基于改进的SVM多源交通信息融合算法。首先,在分析了传统SVM不足的基础上,提出了决策树-支持向量机算法(Decision TreeMethod-SupportVector Machines,DTM-SVM)。其次,对DTM-SVM多源信息融合算法的模型及其流程进行了研究,建立了DTM-SVM的分层结构模型。最后,经过实际数据测试,证明了该方法的高效性和实用性。
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| 关 键 词： | 信息融合 交通监测信息 决策树-支持向量机 |
Study on Traffic Information Fusion Based on Improved SVM |
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| GUO Xiao-ni.Study on Traffic Information Fusion Based on Improved SVM[J].Logistics Technology,2012,31(1):95-98,150. |
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| Authors: | GUO Xiao-ni |
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| Institution: | GUO Xiao-ni(MCC(Beijing) Communication Technology Development Co.,Ltd.,Beijing 100176,China) |
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| Abstract: | On the basis of an analysis of the characteristics of traffic data,the paper proposes a multi-source traffic information fusion algorithm based on improved support vector machine(SVM).First,it analyzes the inadequacy of traditional SVM and proposes a decision tree method-support vector machine(DTM-SVM) algorithm.Secondly it studies the model and procedure of the DTM-SVM multi-source information fusion algorithm and establishes its layered structure model.Finally in a test with actual statistical data,it demonstrates the efficiency and practicality of the method. |
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| Keywords: | information fusion traffic monitoring information decision tree method-support vector machine |
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