| 用于交通运营管理的实时交通流状态分类高斯混合模型 |
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| 引用本文： | 孙璐,张惠民,高荣,顾文钧,徐冰,陈鲤梁.用于交通运营管理的实时交通流状态分类高斯混合模型[J].东南大学学报,2011(2):174-179. |
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| 作者姓名： | 孙璐 张惠民 高荣 顾文钧 徐冰 陈鲤梁 |
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| 作者单位： | 东南大学交通学院;Department of Civil Engineering;The Catholic University of America;山西省公路局晋中分局;山西省公路局忻州分局; |
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| 基金项目： | The US National Science Foundation (No. CMMI-0408390,CMMI-0644552); the American Chemical Society Petroleum Research Foundation (No.PRF-44468-G9); the Research Fellowship for International Young Scientists (No.51050110143); the Fok Ying-Tong Education Foundation (No.114024); the Natural Science Foundation of Jiangsu Province (No.BK2009015); the Postdoctoral Science Foundation of Jiangsu Province (No.0901005C) |
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| 摘 要： | 采用高斯混合模型GMM,同时以交通流量、平均速度和密度3种交通流宏观特征为指标,对交通流状态进行聚类和分类.和其他聚类分类方法比较,高斯混合模型是结构化的模型,适合于各种情形交通流参数.高斯混合模型中子类的个数通过Gap统计量结合交通流的领域知识加以确定,而模型的其他参数则由E-M算法进行估计.所建立的GMM模型可以作...
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| 关 键 词： | 交通流型 高斯混合模型 服务水平 数据挖掘 聚类分析 分类 |
Gaussian mixture models for clustering and classifying traffic flow in real-time for traffic operation and management |
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| Sun Lu , Zhang Huimin Gao Rong Gu Wenjun Xu Bing Chen Liliang.Gaussian mixture models for clustering and classifying traffic flow in real-time for traffic operation and management[J].Journal of Southeast University(English Edition),2011(2):174-179. |
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| Authors: | Sun Lu Zhang Huimin Gao Rong Gu Wenjun Xu Bing Chen Liliang |
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| Institution: | Sun Lu 1,2 Zhang Huimin 3 Gao Rong 4 Gu Wenjun 1 Xu Bing 1 Chen Liliang 1 ( 1School of Transportation,Southeast University,Nanjing 210096,China) ( 2Department of Civil Engineering,The Catholic University of America,Washington DC 20064,USA) ( 3Jinzhong Bureau of Highway Administration of Shanxi Province,Jinzhong 030600,China) ( 4Xinzhou Bureau of Highway Administration of Shanxi Province,Xinzhou 034000,China) |
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| Abstract: | Based on Gaussian mixture models(GMM), speed, flow and occupancy are used together in the cluster analysis of traffic flow data. Compared with other clustering and sorting techniques, as a structural model, the GMM is suitable for various kinds of traffic flow parameters. Gap statistics and domain knowledge of traffic flow are used to determine a proper number of clusters. The expectation-maximization (E-M) algorithm is used to estimate parameters of the GMM model. The clustered traffic flow patterns are th... |
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| Keywords: | traffic flow patterns Gaussian mixture model level of service data mining cluster analysis classifier |
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