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基于能力区域的交通状态预测方法

刘擎超 陆建 陈淑燕

引用本文:
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基于能力区域的交通状态预测方法

刘擎超, 陆建, 陈淑燕

Traffic state prediction based on competence region

Liu Qing-Chao, Lu Jian, Chen Shu-Yan
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  • 交通状态预测是交通流诱导和交通信息发布系统的重要依据. 本文提出了一种基于能力区域的城市快速路交通状态预测方法,该方法通过构建神经网络分类器的能力区域,根据样本数据与交通状态类簇之间的空间距离,预测道路交通状态等级. 神经网络分类器的能力区域能够有效融合时间、空间等多种特征,并且不需要考虑各特征之间的相关性,具有很强的适应性. 实验结果表明,与经典的预测方法相比,其预测误差明显降低,均等系数增大,基于能力区域的方法预测交通状态具有较高的准确性.
    Traffic state prediction is a key basis of traffic flow guidance system and traffic information publishing system. This paper presents a new method of forecasting the traffic state of unban expressway based on competence region. This method can predict the traffic state grade of road based on the distance between the sample data and the traffic state cluster center by creating a competence region of neural network classifier. And this method can effectively integrate the temporal and spatial features together without considering the correlation between the different features, and thus it has a strong adaptability. The experimental results show that this traffic state prediction method can reduce the prediction error and improve the equality coefficients compared with the classical algorithms. The prediction method used in this paper is effective and accurate for forecasting traffic state based on the competence region.
    • 基金项目: 国家高技术研究发展计划(批准号:2011AA110302)和江苏省普通高校研究生科研创新计划(批准号:CXZZ13_0119)资助的课题.
    • Funds: Project supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110302), the Fundamental Research Funds for the Central Universities of Ministry of Education of China (Grant No. CXZZ13_0119).
    [1]

    Wang X Y 2010 Ph. D. Dissertation (Changchun: Jilin University) (in Chinese) [王新颖 2010 博士学位论文(长春: 吉林大学)]

    [2]

    Zhang H B, Sun X D, He Y L 2014 Acta Phys. Sin. 63 040505 (in Chinese) [张洪宾, 孙小端, 贺玉龙 2014 物理学报 63 040505]

    [3]

    Zhang Y M, Wu X J, Bai S L 2013 Acta Phys. Sin. 62 190509 (in Chinese) [张玉梅, 吴晓军, 白树林 2013 物理学报 62 190509]

    [4]

    Ma Q L, Liu W N, Sun D H 2012 Acta Phys. Sin. 61 169501 (in Chinese) [马庆禄, 刘卫宁, 孙棣华 2012 物理学报 61 169501]

    [5]

    Bi J, Guan W 2012 Chin. Phys. B 21 068901

    [6]

    Zhang Y, Guan W 2009 Acta Phys. Sin. 58 756 (in Chinese) [张勇, 关伟 2009 物理学报 58 756]

    [7]

    Cong R, Liu S L, Ma R 2008 Acta Phys. Sin. 57 7487 (in Chinese) [从蕊, 刘树林, 马锐 2008 物理学报 57 7487]

    [8]

    Qi C, Hou Z S 2012 Cont Theo. Applic 29 465 (in Chinese) [齐驰, 侯忠生 2012 控制理论与应用 29 465]

    [9]

    Yao Z S, Shao C F 2007 China J. High. Trans. 20 113 (in Chinese) [姚智胜, 邵春福 2007 中国公路学报 20 113]

    [10]

    Xie J, Wu W 2011 J. Tongji Univ. (Nature Science) 39 1297 (in Chinese) [谢军, 吴伟 2011 同济大学学报 (自然科学版) 39 1297]

    [11]

    Ma L C, Xu W L 2011 Cont. Deci. 26 789 (in Chinese) [马林才, 许玮珑 2011 控制与决策 26 789]

    [12]

    Zang L L, Jia L 2007 China J. High. Trans. 20 95 (in Chinese) [臧利林, 贾磊 2007 中国公路学报 20 95]

    [13]

    Shen G J, Wang X H, Kong X J 2011 Sys. Eng. Theor. Pract. 31 561 (in Chinese) [沈国江, 王啸虎, 孔祥杰 2011 系统工程理论与实践 31 561]

    [14]

    Ding H, Zhang W H, Zheng X Y 2012 China J. High Trans. 25 126 (in Chinese) [丁恒, 张卫华, 郑小燕 2012 中国公路学报 25 126]

    [15]

    Don H H, Jia L M 2010 J. Trans. Sys. Eng. Info. Tech. 10 112 (in Chinese) [董宏辉, 贾利民 2010 交通运输系统工程与信息 10 112]

    [16]

    Kuncheva L I 2000 Proceedings of 4th International Conference on Knowledge Based Intelligent Engineering Systems and Allied Technologies Brighton, UK, Aug 30-Sept 1, 2000 p185

    [17]

    Kuncheva L I 2002 IEEE Trans. Sys. M. and Cyber. Part B: Cyber. 2 146

    [18]

    Verikas A Lipnickas A, Malmqvist K 1999 Patt. Recog. Lett. 4 429

  • [1]

    Wang X Y 2010 Ph. D. Dissertation (Changchun: Jilin University) (in Chinese) [王新颖 2010 博士学位论文(长春: 吉林大学)]

    [2]

    Zhang H B, Sun X D, He Y L 2014 Acta Phys. Sin. 63 040505 (in Chinese) [张洪宾, 孙小端, 贺玉龙 2014 物理学报 63 040505]

    [3]

    Zhang Y M, Wu X J, Bai S L 2013 Acta Phys. Sin. 62 190509 (in Chinese) [张玉梅, 吴晓军, 白树林 2013 物理学报 62 190509]

    [4]

    Ma Q L, Liu W N, Sun D H 2012 Acta Phys. Sin. 61 169501 (in Chinese) [马庆禄, 刘卫宁, 孙棣华 2012 物理学报 61 169501]

    [5]

    Bi J, Guan W 2012 Chin. Phys. B 21 068901

    [6]

    Zhang Y, Guan W 2009 Acta Phys. Sin. 58 756 (in Chinese) [张勇, 关伟 2009 物理学报 58 756]

    [7]

    Cong R, Liu S L, Ma R 2008 Acta Phys. Sin. 57 7487 (in Chinese) [从蕊, 刘树林, 马锐 2008 物理学报 57 7487]

    [8]

    Qi C, Hou Z S 2012 Cont Theo. Applic 29 465 (in Chinese) [齐驰, 侯忠生 2012 控制理论与应用 29 465]

    [9]

    Yao Z S, Shao C F 2007 China J. High. Trans. 20 113 (in Chinese) [姚智胜, 邵春福 2007 中国公路学报 20 113]

    [10]

    Xie J, Wu W 2011 J. Tongji Univ. (Nature Science) 39 1297 (in Chinese) [谢军, 吴伟 2011 同济大学学报 (自然科学版) 39 1297]

    [11]

    Ma L C, Xu W L 2011 Cont. Deci. 26 789 (in Chinese) [马林才, 许玮珑 2011 控制与决策 26 789]

    [12]

    Zang L L, Jia L 2007 China J. High. Trans. 20 95 (in Chinese) [臧利林, 贾磊 2007 中国公路学报 20 95]

    [13]

    Shen G J, Wang X H, Kong X J 2011 Sys. Eng. Theor. Pract. 31 561 (in Chinese) [沈国江, 王啸虎, 孔祥杰 2011 系统工程理论与实践 31 561]

    [14]

    Ding H, Zhang W H, Zheng X Y 2012 China J. High Trans. 25 126 (in Chinese) [丁恒, 张卫华, 郑小燕 2012 中国公路学报 25 126]

    [15]

    Don H H, Jia L M 2010 J. Trans. Sys. Eng. Info. Tech. 10 112 (in Chinese) [董宏辉, 贾利民 2010 交通运输系统工程与信息 10 112]

    [16]

    Kuncheva L I 2000 Proceedings of 4th International Conference on Knowledge Based Intelligent Engineering Systems and Allied Technologies Brighton, UK, Aug 30-Sept 1, 2000 p185

    [17]

    Kuncheva L I 2002 IEEE Trans. Sys. M. and Cyber. Part B: Cyber. 2 146

    [18]

    Verikas A Lipnickas A, Malmqvist K 1999 Patt. Recog. Lett. 4 429

计量
  • 文章访问数:  1786
  • PDF下载量:  532
  • 被引次数: 0
出版历程
  • 收稿日期:  2013-11-20
  • 修回日期:  2014-03-06
  • 刊出日期:  2014-07-05

基于能力区域的交通状态预测方法

  • 1. 东南大学城市智能交通江苏省重点实验室, 南京 210096;
  • 2. 现代城市交通技术江苏高校协同创新中心, 南京 210096
    基金项目: 

    国家高技术研究发展计划(批准号:2011AA110302)和江苏省普通高校研究生科研创新计划(批准号:CXZZ13_0119)资助的课题.

摘要: 交通状态预测是交通流诱导和交通信息发布系统的重要依据. 本文提出了一种基于能力区域的城市快速路交通状态预测方法,该方法通过构建神经网络分类器的能力区域,根据样本数据与交通状态类簇之间的空间距离,预测道路交通状态等级. 神经网络分类器的能力区域能够有效融合时间、空间等多种特征,并且不需要考虑各特征之间的相关性,具有很强的适应性. 实验结果表明,与经典的预测方法相比,其预测误差明显降低,均等系数增大,基于能力区域的方法预测交通状态具有较高的准确性.

English Abstract

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