一种组合核相关向量机的短时交通流局域预测方法
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摘要
为有效提高短时交通流预测的精度,提出一种基于组合核相关向量机模型的短时交通流局域预测方法.首先利用C-C方法实现相空间重构,然后根据Hannan-Quinn准则确定邻近点个数,进而构建基于粒子群优化的组合核相关向量机模型,最后采用上海市南北高架快速路的感应线圈实测数据进行实验验证和对比分析.实验结果表明:基于组合核相关向量机模型的短时交通流局域预测方法的预测误差和均等系数均优于对比方法,其中,平均绝对百分比误差比GKF-RVM模型、GKF-SVM模型和加权一阶局域预测模型分别降低了29.2%、47.5%和59.5%,能够进一步提高短时交通流预测的精度.
In order to improve the prediction accuracy of short-term traffic flow effectively,a short-term traffic flow local prediction method based on a combined kernel function relevance vector machine( CKF-RVM) model was proposed. Firstly,the C-C method was used to realize phase space reconstruction. Secondly,the number of neighboring points was determined by use of Hannan-Quinn criteria. Then,the CKF-RVM model was constructed based on particle swarm optimization algorithm. Finally,validation and comparative analysis was carried out using inductive loop data measured from the north-south viaduct in Shanghai. The experimental results demonstrate that the prediction error and the equal coefficient of the proposed method are both superior to the contrastive method.The MAPEs of the proposed method are 29. 2%,47. 5% and 59. 5% lower than GKF-RVM model,GKF-SVM model and weighted first-order local prediction model,which can further improve the prediction accuracy of shortterm traffic flow.
In order to improve the prediction accuracy of short-term traffic flow effectively,a short-term traffic flow local prediction method based on a combined kernel function relevance vector machine( CKF-RVM) model was proposed. Firstly,the C-C method was used to realize phase space reconstruction. Secondly,the number of neighboring points was determined by use of Hannan-Quinn criteria. Then,the CKF-RVM model was constructed based on particle swarm optimization algorithm. Finally,validation and comparative analysis was carried out using inductive loop data measured from the north-south viaduct in Shanghai. The experimental results demonstrate that the prediction error and the equal coefficient of the proposed method are both superior to the contrastive method.The MAPEs of the proposed method are 29. 2%,47. 5% and 59. 5% lower than GKF-RVM model,GKF-SVM model and weighted first-order local prediction model,which can further improve the prediction accuracy of shortterm traffic flow.
引文
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[9]杨兆升,王媛,管青.基于支持向量机方法的短时交通流量预测方法[J].吉林大学学报(工学版),2006,36(6):881-884.YANG Zhaosheng,WANG Yuan,GUAN Qing.Short-term traffic flow prediction method based on SVM[J].Journal of Jilin University(Engineering and Technology Edition),2006,36(6):881-884.
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[12]董春娇,邵春福,李娟,等.基于混沌分析的道路网交通流短时预测[J].系统工程学报,2011,26(3):340-345.DONG Chunjiao,SHAO Chunfu,LI Juan,et al.Short-term traffic flow prediction of road network based on chaos theory[J].Journal of System Engineering,2011,26(3):340-345.
[13]张洪宾,孙小端,贺玉龙.短时交通流复杂动力学特性分析及预测[J].物理学报,2014,63(4):1-8.DOI:10.7498/aps.63.040505.ZHANG Hongbin,SUN Xiaoduan,HE Yulong.Analysis and prediction of complex dynamical characteristics of short-term traffic flow[J].Acta Physica Sinica,2014,63(4):1-8.
[14]FARMER J D,SIDOROWICH J J.Prediction chaotic time series[J].Physical Review Letters,1987,59(8):845-848.
[15]PACKARD N H,CRUTCHFIELD J P,FARMER J D.Geometry from a time series[J].Physical Review Letters,1980,45(9):712-716.
[16]KIM H S,EYKHOLT R,SALAS J D.Nonlinear dynamics,delay times,and embedding windows[J].Physical D,1999,127:48-60.
[17]TIPPING M E.Sparse Bayesian learning and the relevance vector machine[J].Journal of Machine Learning Research,2001,1(3):211-244.
[18]瞿娜娜.基于组合核函数支持向量机研究及应用[D].广州:华南理工大学,2011.QU Nana.Research and application of support vector machine based on mixed-kenel function[D].Guangzhou:South China University of Technology,2011.
[19]孟庆芳,彭玉华,曲怀敬,等.基于信息准则的局域预测法邻近点的选取方法[J].物理学报,2008,57(3):1423-1430.MENG Qingfang,PENG Yuhua,QU Huaijing,et al.The neighbor point selection method for local prediction based on information criterion[J].Acta Physica Sinica,2008,57(3):1423-1430.
[2]MIN W,WYNER L.Real-time road traffic prediction with spatiotemporal correlation[J].Transportation Research Part C:Emerging Technologies,2011,19(4):606-616.DOI:10.1016/j.trc.2010.10.002.
[3]邴其春,杨兆升,周熙阳,等.基于向量误差修正模型的短时交通参数预测[J].吉林大学学报(工学版),2015,45(4):1076-1081.DOI:10.13229/j.cnki.jdxbgxb,201504008.BING Qichun,YANG Zhaosheng,ZHOU Xiyang,et al.Short-term traffic parameters prediction method based on vector error correction model[J].Journal of Jilin University(Engineering and Technology Edition),2015,45(4):1076-1081.DOI:10.13229/j.cnki.jdxbgxb,201504008.
[4]CLARK S.Traffic prediction using multivariate nonparametric regression[J].Journal of Transportation Engineering,2003,129(2):161-168.DOI:10.1061/(ASCE)0733-947x(2003)129:2(161).
[5]杨兆升,朱中.基于卡尔曼滤波理论的交通流量实时预测模型[J].中国公路学报,1999,12(3):63-67.YANG Zhaosheng,ZHU Zhong.A real-time traffic volume prediction model based on the kalman filtering theory[J].China Journal of Highway and Transport,1999,12(3):63-67.
[6]WANG Y,PAPAGEORGOION M.Real-time freeway traffic state estimation based on extended kalman filter:a general approach[J].Transportation Research Part B:Methodological,2005,39(2):141-167.DOI:10.1016/j.trb.2004.03.003.
[7]龚勃文,林赐云,李静,等.基于核自组织映射-前馈神经网络的交通流短时预测[J].吉林大学学报(工学版),2011,41(4):938-943.GONG Bowen,LIN Ciyun,LI Jing,et al.Short-term traffic flow prediction based on KSOM-BP neural network[J].Journal of Jilin University(Engineering and Technology Edition),2011,41(4):938-943.
[8]ZHU J Z,CAO J X,ZHU Y.Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections[J].Transportation Research Part C:Emerging Technology,2014,47(2):139-154.
[9]杨兆升,王媛,管青.基于支持向量机方法的短时交通流量预测方法[J].吉林大学学报(工学版),2006,36(6):881-884.YANG Zhaosheng,WANG Yuan,GUAN Qing.Short-term traffic flow prediction method based on SVM[J].Journal of Jilin University(Engineering and Technology Edition),2006,36(6):881-884.
[10]傅贵,韩国强,逯峰,等.基于支持向量机回归的短时交通流预测模型[J].华南理工大学学报(自然科学版),2013,41(9):71-76.DOI:10.3969/j.issn.1000-565X.2013.09.012.FU Gui,HAN Guoqiang,LU Feng,et al.Short-term traffic flow forecasting model based on support vector machine regression[J].Journal of South China University of Technology(Natural Science Edition),2013,41(9):71-76.DOI:10.3969/j.issn.1000-565X.2013.09.012.
[11]王进,史其信.基于非线性理论的短期交通流预测研究[J].西安建筑科技大学学报(自然科学版),2006,38(2):184-188.WANG Jin,SHI Qixin.Study of the short-term traffic flow forecasting based on nonlinear theory[J].Journal of Xian University of Architecture and Technology(Natural Science Edition),2006,38(2):184-188.
[12]董春娇,邵春福,李娟,等.基于混沌分析的道路网交通流短时预测[J].系统工程学报,2011,26(3):340-345.DONG Chunjiao,SHAO Chunfu,LI Juan,et al.Short-term traffic flow prediction of road network based on chaos theory[J].Journal of System Engineering,2011,26(3):340-345.
[13]张洪宾,孙小端,贺玉龙.短时交通流复杂动力学特性分析及预测[J].物理学报,2014,63(4):1-8.DOI:10.7498/aps.63.040505.ZHANG Hongbin,SUN Xiaoduan,HE Yulong.Analysis and prediction of complex dynamical characteristics of short-term traffic flow[J].Acta Physica Sinica,2014,63(4):1-8.
[14]FARMER J D,SIDOROWICH J J.Prediction chaotic time series[J].Physical Review Letters,1987,59(8):845-848.
[15]PACKARD N H,CRUTCHFIELD J P,FARMER J D.Geometry from a time series[J].Physical Review Letters,1980,45(9):712-716.
[16]KIM H S,EYKHOLT R,SALAS J D.Nonlinear dynamics,delay times,and embedding windows[J].Physical D,1999,127:48-60.
[17]TIPPING M E.Sparse Bayesian learning and the relevance vector machine[J].Journal of Machine Learning Research,2001,1(3):211-244.
[18]瞿娜娜.基于组合核函数支持向量机研究及应用[D].广州:华南理工大学,2011.QU Nana.Research and application of support vector machine based on mixed-kenel function[D].Guangzhou:South China University of Technology,2011.
[19]孟庆芳,彭玉华,曲怀敬,等.基于信息准则的局域预测法邻近点的选取方法[J].物理学报,2008,57(3):1423-1430.MENG Qingfang,PENG Yuhua,QU Huaijing,et al.The neighbor point selection method for local prediction based on information criterion[J].Acta Physica Sinica,2008,57(3):1423-1430.
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