基于支持向量机的交通流预测方法研究
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摘要
实时准确的交通流量预测是实现智能交通控制和诱导的前提与关键,也是智能化交通管理的客观需要。由于城市交通系统本身的强非线性、随机性、时变性等特点,以精确数学模型为基础的传统预测方法效果并不理想,因此人工智能的方法愈来愈受到人们的重视。支持向量机(Support Vector Machine, SVM)作为一种基于统计学习理论的机器学习方法,较好地解决了非线性、高维数、局部极小点等实际问题,是复杂非线性科学和人工智能科学的研究前沿。本文总结了交通流预测模型的研究现状,比较了预测模型的优缺点,分析了交通流数据的相关性和影响交通流变化的主要因素,以此为基础,主要从支持向量机回归方法进行研究和其在交通流预测中的应用相结合的角度出发,对支持向量机的参数选择、核函数构造、增量学习、并行计算等方法以及在交通流预测中的应用进行了系统的研究,主要工作如下：
(1)对支持向量机参数选择进行了研究。不敏感损失系数、惩罚系数、核函数及其参数的优化选择对回归模型的学习精度和推广能力的好坏起着重要的作用。结合交通流数据特性,本文提出自适应参数选择的交通流预测方法,利用训练集对支持向量回归参数进行求解。与BP神经网络方法、经验选择方法和粒子群优化方法进行比较,自适应参数选择方法能够依据训练样本集自适应进行参数选取,实现预测模型自适应能力,有效地改善了交通流量的预测精度。
(2)对支持向量机核函数的构造方法进行研究。目前较为常用的核函数为高斯径向基核函数,它在模式识别与回归分析中都表现出了良好的映射性能,但是径向基不是一个完备基,不能较好地对任意信号逼近,特别是对边界处逼近和多尺度信号逼近。本文利用子波良好的时频多分辨特性及逼近特性,构造基于Marr子波核函数和多尺度核函数,通过仿真试验分析,在泛化性能和训练时间方面优于传统径向基核函数,同时弥补了传统核函数在逼近性能方面的不足,较好地解决了交通流量实时预测中存在的随机干扰因素影响大、不确定性强的问题。
(3)对支持向量机并行计算技术进行研究。对于大规模路网的动态交通流预测,并行计算是解决短时交通流预测中对大样本集进行快速训练的一条重要途径。本文在研究并行SVM技术的基础上,引入并行SMO算法用于交通流预测,同时对2000条多路段并行实验研究,通过在深腾1800高性能机器上进行实验,验证了基于SVR大规模交通流预测的实用性和可用性。
Accurate real time traffic flow prediction is a prerequisite and key to realize intelligent traffic control and guidance, and it is also the objective requirement to intelligent traffic management. Due to the strong non-linear, stochastic, time-varying characteristics of urban transport system, traditional forecasting methods based on accurate mathematical models is not ideal, therefore, artificial intelligence methods obtain more and more attention. Support Vector Machine (SVM) is a machine learning method based on statistical learning theory (SLT), it can efficiently solve small samples, nonlinear, high dimensions and local minima problems, and it is a research front of complex non-linear science and artificial intelligence scientific research. In this paper, the research situation of traffic flow prediction models are summarized, the advantages and disadvantages of different models is compared. Based on the analysis of the correlation of traffic flow data and important factors which affect the traffic flow's change, the support vector regression method and its application in traffic flow prediction are systematically discussed, such as the support vector machine's parameter selection method, kernel function construction method, incremental learning technique, parallel computing technology and its application in traffic flow prediction, main tasks are as follows:
(1) Research on the SVM parameter selection. The selection of insensitive loss coefficient, penalty coefficient C, kernel function and its parameters is important to the learning accuracy and generalization ability of regression model. In this paper, according to the traffic data properties, a traffic flow prediction method with adaptive parameter selection is proposed, using the training set to solve support vector regression parameters. Compared with traditional experience selection SVR method, adaptive parameter selection SVR method can adaptively select parameters based on training set, realize the model's adaptive capacity and efficaciously improve traffic flow prediction accuracy.
(2) Research on support vector machine kernel function's construction method. Since the widely used Gaussian (Radial Basis Function) kernel function can not does arbitrary signals approximation well, especially the boundary approximation and multi-scale signals approximation, the wavelet is used which has good multi-resolution time-frequency characteristics and approximation properties, and SVR with Marr wavelet kernel function and multi-scale kernel function are constructed. With simulation test analysis, this method has better generalization capability and training speed, make up the approximation performance deficiencies of traditional kernel functions and solve the stochastic interference factors' enormous influence and strong uncertainty problems in real time traffic flow prediction.
(3) Research on support vector machine's parallel computing technology. In the dynamic traffic flow prediction in large-scale network, parallel computing is an important solution to solve the large sample set's fast training problems in short time traffic flow prediction. With the research on parallel SVM, parallel SMO is employed in traffic flow prediction. Simultaneously,2000 sections experiments are carried on the DeepComp 1800 high-performance machine. The experimental results demonstrate parallel SVR is practicable and effective to predict traffic flow in large-scale traffic network.
(1)对支持向量机参数选择进行了研究。不敏感损失系数、惩罚系数、核函数及其参数的优化选择对回归模型的学习精度和推广能力的好坏起着重要的作用。结合交通流数据特性,本文提出自适应参数选择的交通流预测方法,利用训练集对支持向量回归参数进行求解。与BP神经网络方法、经验选择方法和粒子群优化方法进行比较,自适应参数选择方法能够依据训练样本集自适应进行参数选取,实现预测模型自适应能力,有效地改善了交通流量的预测精度。
(2)对支持向量机核函数的构造方法进行研究。目前较为常用的核函数为高斯径向基核函数,它在模式识别与回归分析中都表现出了良好的映射性能,但是径向基不是一个完备基,不能较好地对任意信号逼近,特别是对边界处逼近和多尺度信号逼近。本文利用子波良好的时频多分辨特性及逼近特性,构造基于Marr子波核函数和多尺度核函数,通过仿真试验分析,在泛化性能和训练时间方面优于传统径向基核函数,同时弥补了传统核函数在逼近性能方面的不足,较好地解决了交通流量实时预测中存在的随机干扰因素影响大、不确定性强的问题。
(3)对支持向量机并行计算技术进行研究。对于大规模路网的动态交通流预测,并行计算是解决短时交通流预测中对大样本集进行快速训练的一条重要途径。本文在研究并行SVM技术的基础上,引入并行SMO算法用于交通流预测,同时对2000条多路段并行实验研究,通过在深腾1800高性能机器上进行实验,验证了基于SVR大规模交通流预测的实用性和可用性。
Accurate real time traffic flow prediction is a prerequisite and key to realize intelligent traffic control and guidance, and it is also the objective requirement to intelligent traffic management. Due to the strong non-linear, stochastic, time-varying characteristics of urban transport system, traditional forecasting methods based on accurate mathematical models is not ideal, therefore, artificial intelligence methods obtain more and more attention. Support Vector Machine (SVM) is a machine learning method based on statistical learning theory (SLT), it can efficiently solve small samples, nonlinear, high dimensions and local minima problems, and it is a research front of complex non-linear science and artificial intelligence scientific research. In this paper, the research situation of traffic flow prediction models are summarized, the advantages and disadvantages of different models is compared. Based on the analysis of the correlation of traffic flow data and important factors which affect the traffic flow's change, the support vector regression method and its application in traffic flow prediction are systematically discussed, such as the support vector machine's parameter selection method, kernel function construction method, incremental learning technique, parallel computing technology and its application in traffic flow prediction, main tasks are as follows:
(1) Research on the SVM parameter selection. The selection of insensitive loss coefficient, penalty coefficient C, kernel function and its parameters is important to the learning accuracy and generalization ability of regression model. In this paper, according to the traffic data properties, a traffic flow prediction method with adaptive parameter selection is proposed, using the training set to solve support vector regression parameters. Compared with traditional experience selection SVR method, adaptive parameter selection SVR method can adaptively select parameters based on training set, realize the model's adaptive capacity and efficaciously improve traffic flow prediction accuracy.
(2) Research on support vector machine kernel function's construction method. Since the widely used Gaussian (Radial Basis Function) kernel function can not does arbitrary signals approximation well, especially the boundary approximation and multi-scale signals approximation, the wavelet is used which has good multi-resolution time-frequency characteristics and approximation properties, and SVR with Marr wavelet kernel function and multi-scale kernel function are constructed. With simulation test analysis, this method has better generalization capability and training speed, make up the approximation performance deficiencies of traditional kernel functions and solve the stochastic interference factors' enormous influence and strong uncertainty problems in real time traffic flow prediction.
(3) Research on support vector machine's parallel computing technology. In the dynamic traffic flow prediction in large-scale network, parallel computing is an important solution to solve the large sample set's fast training problems in short time traffic flow prediction. With the research on parallel SVM, parallel SMO is employed in traffic flow prediction. Simultaneously,2000 sections experiments are carried on the DeepComp 1800 high-performance machine. The experimental results demonstrate parallel SVR is practicable and effective to predict traffic flow in large-scale traffic network.
引文
[1]赵亦林(美)著.谭国真译.车辆定位与导航系统[M].北京：电子工业出版社,1999.
[2]杨兆升.新一代智能交通控制系统关键技术及其应用[M].北京：中国铁道出版社,2008
[3]《中国智能运输系统体系框架》专题组著.中国智能运输系统体系框架[M].北京：人民交通出版社,2003.
[4]朱茵,王军利,周彤梅.智能交通系统导论[M].北京：中国人民公安大学出版社,2007.
[5]陆化普.智能交通系统概论[M].北京：人民交通出版社,2004.
[6]翁小雄,谭国贤,姚树申.城市交叉口交通流特征与短时预测模型[J].交通运输工程学报,2006,6(2)：103-107.
[7]Smith-BL, Demetsky M J. Traffic flow forecasting:comparison of modeling approaches[J]. Journal of Transportation Engineering,1997,123(4):261-266.
[8]贺国光,李宇,马寿峰.基于数学模型的短时交通流预测方法探讨[J].系统工程理论与实践,2000,(12)：51-56.
[9]刘静,关伟.交通流预测方法综述[J].公路交通科技,2004,(3)：82-85.
[10]姚智胜,邵春福.道路交通状态预测研究[J].哈尔滨工业大学学报,2009,41(4)：247-249.
[11]王进,史其信.短时交通流预测模型综述[J].中国公共安全,2005,1(1)：92-98.
[12]Ahmaed M S, Cook A R. Analysis of freeway traffic time-series data by using Box-Jenkins technique[J].Transportation Research Record,1979,722:1-9.
[13]Davis G A, Nihan N L. Using time-series designs to estimate changes in freeway level of service, despite missing data[J]. Transportation Research Part A:General,1984, 18(5):431-438.
[14]Kim Changkyun, Hobeika. Antonie G. Short-term demand forecasting model from real-time traffic data[C]. Proceedings of the Infrastructure Planning and Management, USA:ASCE, 1993:540-550.
[15]Lee S, Fambro D B. Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting[C]. Transportation Research Record, Washington DC,1999,1678:179-188.
[16]Willams B M.Multivariate vehicular traffic flow prediction:an evaluation of ARIMAX modeling [C]. Transportation Research Record, Washington DC,2001,1776:194-200.
[17]Kamarianakis Y, Prastacos P. Forecasting traffic flow conditions in an urban network: comparison of multivariate and univariate approaches[C]. Transportation Research Record, Washington DC,2003,1857:74-84.
[18]Willams B M, HOEL L A.Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process:a theoretical basis and empirical results [J]. Journal of Transportation Engineering,2003,129(6):583-588.
[19]韩超,宋苏,王成红.基于ARIMA模型的短时交通流实时自适应预测[J].系统仿真学报,2004,16(7)：1530-1535.
[20]张辉,刘嘉,柳湘月等.交通流的季节ARIMA模型与预报[J].天津大学学报,2008,25(1)：129-133.
[21]孙湘海,刘潭秋.基于SARIMA模型的城市道路短期交通流预测研究[J].公路交通科技,2008,25(1)：129-133.
[22]陈岳明,萧德云.基于跳转模型的路网交通流预测[J].控制与决策,2009,24(8)：1177-1180.
[23]李振龙,张利国,钱海峰.基于非参数回归的短时交通流预测研究综述[J].交通运输工程与信息学报,2008,6(4)：34-37.
[24]Oswald R K, Scherer W T, Smith B. L. Traffic flow forecasting using approximate nearest neighbor nonparametric regression[R]. Research Report No. UVACTS-15-13-7, Center for Transportation Studies, University of Virginia,2001.
[25]达庆东,段里仁.交通流非参数回归模型[J].数理统计与管理,2003,22(4)：41-46.
[26]王晓原,张敬磊.基于非参数样条拟合的交通流预测方法研究[J].计算机工程与应用,2006,42(26)：218-221.
[27]Davis G A, Nihan N L. Nonparametric regression and short-term freeway traffic forecasting[J]. Journal of Transportation Engineering,1991,117(2):178-188.
[28]Smith B L, Demetsky M J. Traffic flow forecasting:comparison of modeling approaches[J]. Journal of Transportation Engineering,1997,123(4):261-266.
[29]Smith BL, Williams BM, Oswald RK. Comparison of parametric and nonparametric modelsfor traffic flow forecasting[J]. Transportation Research Part C,2002,10(4):303-321.
[30]Smith B L, Oswald R K. Meeting real time traffic flow forecasting requirements with imprecise computations [J]. Computer-aided Civil and Infrastructure Engineering,2003, 18(3):201-213.
[31]Clark S. Traffic prediction using multivariate nonparametric regression[J]. Journal of Transportation Engineering,2003,129(2):161-167.
[32]宫晓燕,汤淑明.基于非参数回归的短时交通流量预测与事件检测综合算法[J].中国公路学报,2003,16(1)：82-86.
[33]周小鹏,冯奇,孙立军.基于最近邻法的短时交通流预测[J].同济大学学报(自然科学版),2006,34(10)：1494-1498.
[34]张晓利,贺国光.考虑交通吸纳点的非参数回归组合型短时交通流预测方法[J].系统工程,2006,24(12)：21-25.
[35]张晓利,贺国光,陆化普.基于K-邻域非参数回归短时交通流预测方法[J].系统工程学报,2009,24(2)：178-183.
[36]Iwao O, Stephanedes Y J. Dynamic prediction of traffic volume through kalman filtering theory[J]. Transportation Research Part B,1984,18(1):1-11.
[37]Vythoulkas P C. Alternative approaches to short-term traffic forecasting for use in
driver information systems[J]. Transportation and Traffic Theory,1993,12:485-506.
[38]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,(3)：63-67.
[39]YangF, Yin Z, Liu HX. Online recursive algorithm for short-term traffic predict ion [J]. Transportation Research Record,2004,1879:1-8.
[40]李嘉,刘小兰.基于卡尔曼滤波理论的交通信息预测模型及其应用[J].中南公路工程,2005,30(4)：108-123.
[41]Ye Zhirui, Zhang Yunlong, Middleton D R. Unscented Kalman filter method for speed estimation using single loop detector data[J]. Transportation Research Record,2006, 1968:117-125.
[42]王均,关伟.基于Kalman滤波的城市环路交通流短时预测研究[J].交通与计算机,2006,24(5)：16-19.
[43]Wang Y, Papageorgiou M, Messmer A. A real-time freeway network traffic surveillance tool[J]. IEEE Trans. Control Systems Technology,2006,14(1):18-32.
[44]Yibing W, Papageorgiou M, Messmer A. Real-time freeway traffic stateestimation based on extended kalman filter:a case study [J]. Transportation Science,2007,41(2):167-181.
[45]聂佩林,余志,何兆成.基于约束卡尔曼滤波的短时交通流量组合预测模型[J].交通运输工程学报,2008,8(5)：86-90.
[46]Dougherty M. A review of neural networks applied to transport[J]. Transportation Research Part C,1995,3(4):247-260.
[47]Zhang H J, Ritchie S G, Lo Z P. Macroscopic modeling of freeway traffic using an artificial neural network[J]. Transportation Research Record,1997,1588:110-119.
[48]Faghri A, Hua J. Evaluation of artificial neural network applications in transportation engineering[J]. Transportation Research Record,1992,1358:71-80.
[49]Dougherty M S, Kirby H C. The use of neural networks to recognize and predict traffic congestion[J]. Traffic Engineering and Control,1993,34(6):311-314.
[50]Park B, Messer C J, Urbanik T. Short-term freeway traffic volume forecasting using radial basis function neural network[J]. Transportation Research Record,1998, 1651:39-47.
[51]Abdulhai B, Porwal H, Recker W. Short-term freeway traffic flow prediction using genetically optimized time delay based neural networks[R]. Institute of Transportation Studies, University of California,1998.
[52]Dia H. An object-oriented neural network approach to shortterm traffic forecasting [J]. European Journal of Operational Research,2001,131(2):253-261.
[53]Ishak S, ASCE M, Alecsandru C. Optimizing traffic prediction performance of neural networks under various topological input and traffic condition settings[J]. Journal of Transportation Engineering,2004,130(7):452-465.
[54]Xiao H, Sun H, Ran B. Special factor adjustment model using fuzzy-neural network in traffic prediction[J]. Transportation Research Record,2004,1879:17-23.
[55]Dia H. An object-oriented neural network approach to short-term traffic forecasting[J]. European Journal of Operational Research,2001,131(2):253-261.
[56]Vanajakshi L, Rilett L R. A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed[C]. In IEEE intelligent vehicles symposium,2004:194-199.
[57]Zhang H M. Recursive prediction of traffic conditions with neural network models [J]. Journal of Transportation Engineering,2000,126(6):472-481.
[58]Yun S Y, Namkoong S. A performance evaluation of neural network models in traffic volume forecasting [J]. Mathematical and Computer Modeling,1998,27(6):293-310.
[59]Guozhen Tan, Qingqing Deng, Zhu Tian, et al. A dish parallel BP for traffic flow forecasting [C]. International Conference on Computational Intelligence and Security, Harbin,2007:546-549.
[60]Jiang X, Adeli H. Dynamic wavelet neural network model for traffic flow forecasting [J]. Journal of Transportation Engineering,2005,131(10):771-779.
[61]Lingras P, Sharma S. C, Osborne P, et al. Traffic volume time-series analysis according to type of road use[J]. Computer-Aided Civil and InfrastructureEngineering,2000, 15(5):365-373.
[62]Park B. Hybrid neuro-fuzzy application in short-term freeway traffic volume forecasting[J]. Transportation Research Record,2002,1802:190-196.
[63]Zheng W, Lee D. H, Shi Q. Short-term freeway traffic flow prediction:Bayesian combined neural network approach[J]. Journal of Transportation Engineering,2006, 132(2):114-121.
[64]Smith B L, Demetsky M J. Short-term traffic flow prediction:Neural network approach [J]. Transportation Research Record,1995,1453:98-104.
[65]Chang E C. Traffic estimation for proactive traffic control[J]. Transportation Research Record,1999,1679:81-86.
[66]Chen H, Grant-Muller S. Use of sequential learning for short-term traffic flow forecasting [J]. Transportation Research Part C,2001,9(5):319-336.
[67]Dougherty M S., Cobbett M R. Short-term inter-urban traffic forecast using neural networks [J]. International Journal of Forecasting,1997,13(1):21-31.
[68]Kirby H R., Watson S M, Dougherty M S. Should we use neural networks or statistical models for short-term traffic forecasting[J]. International Journal of Forecasting, 1997,13(1):43-50.
[69]Vlahogianni E I, Karlaftis M G, Golias J C. Optimized and metaoptimized neural networks for short-term traffic flow prediction:A genetic approach [J]. Transportation Research
Part C,2005,13(3):211-234.
[70]Zhong M, Sharma S, Lingras P. Short-term traffic prediction on different types of roads with genetically designed regression and time delay neural network models[J]. Journal of Computing in CivilEngineering,2005,19(1):94-103.
[71]Manoel Castro-Neto, Young-Seon Jeong, Myong-Kee Jeong, et al. Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions [J]. Expert Systems with Applications,2009,36(3):6164-6173.
[72]V. N. Vapnik. The Nature of Statistical Learning Theory[M]. New York:Springer,1995..
[73]邓乃扬,田英杰.数据挖掘中的新方法—支持向量机[M].北京：科学出版,2004.
[74]Ren J T, Ou X L, Zhang Y. Research on network level traffic pattern recognition[C]. IEEE 5th International Conference on Intelligent Transportation Systems, Singapore, 2002:500-504.
[75]Wu C H, Wei C C, Chang M. Travel time prediction with support vector regression [J]. IEEE Transaction on Intelligent Transportation Systems.2004,5(12):276-281.
[76]Scholkopf B, Alex J. New Support Vector Algorithms [J]. Neural Computation,2000,12(5): 1207-1245.
[77]丁爱玲.基于统计学习理论的交通流量时间序列预测[J].交通与计算机,2002,20(2)：27-30.
[78]Ding A, Zhao X, Jiao L. Traffic flow time series prediction based on statistics learning theory[C]. In Proceedings IEEE 5th Int. Conference on Intelligent Transportation Systems,2002:727-730.
[79]徐启华,杨瑞.支持向量机在交通流量实时预测中的应用[J].公路交通科技,2005,22(12)：131-134.
[80]蒋刚,肖建.基于核机器方法的城市交通流量实时预测[J].计算机工程,2006,32(17)：48-51.
[81]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.
[82]姚智胜,邵春福,高永亮.基于支持向量回归机的交通状态短时预测方法研究[J].北京交通大学学报,2006,30(3)：19-22.
[83]姚智胜,邵春福,熊志华.基于主成分分析和支持向量机的道路网短时交通流量预测[J].吉林大学学报(工学版),2008,38(1)：48-52.
[84]张朝元,陈丽.改进的LS-SVM算法及在交通流量预测上的应用[J].昆明理工大学学报(理工版),2008,33(6)：72-75.
[85]杨建华,于小宁.高速公路动态交通流支持向量机预测模型[J].西安工业大学学报,2009,29(3)：280-284.
[86]贺国光,马寿峰,李宇.基于小波分解与重构的交通流短时预测法[J].系统工程理论与实践,2002,(9)：101-106.
[87]Chang S C, Kim R S, Kim S. J. Traffic flow forecasting using a 3-stage model [C]. IEEE
Intelligent Vehicles Symposium, Proceedings,2000:451-456.
[88]Kaye BH. A random walk through fractal dimensions [M]. New York:VCH Publishers,1989.
[89]王正武,黄中祥.短时交通流预测模型的分析与评价[J].系统工程,2003,21(6)：97-100.
[90]Frankhouser P. Aspects Fractals des Structures Urbaines[J]. L Espace Geographic, 1990,19(1):45-49.
[91]刘继生,陈彦光.交通网络空间结构的分形维数及其测算方法探讨[J].地理学报,1999,54(5)：471-478.
[92]李江,郭庆胜.基于GIS的城市交通网络复杂性定量描述[J].华中师范大学学报(自然科学版),2002,36(4)：534-537.
[93]刘妙龙,黄佩蓓.分形理论在城市交通网络时空演变特征研究中的应用[J].武汉大学学报(信息科学版),2003,28(6)：749-753.
[94]刘妙龙,黄佩蓓.上海大都市交通网络分形的时空特征演变研究[J].地理科学,2004,24(2)：144-149.
[95]姜世国,周一星.北京城市形态的分形集聚特征及其实践意义[J].地理研究,2006,25(2)：204-211.
[96]张鹏,韩增林.辽宁省公路交通网络的分形研究[J].交通运输系统工程与信息,2006,6(1)：123-127.
[97]郭建科,韩增林,许妍.基于集聚分形的大连城市交通网络演变研究[J].交通运输系统工程与信息,2007,7(5)：121-126.
[98]王秋平,张琦.城市中心区交通量时间序列的分形特征[J].西安建筑科技大学学报(自然科学版),2008,40(1)：101-107.
[99]凌复华.突变理论及其应用[M].上海：上海交通大学出版社,1987.
[100]Dendrinos D S. Operating speeds and volume to capacity ratios[J]. Transportation Research.1978,12:191-194.
[101]Navin F P D. Traffic congestion catastrophes[J]. Transportation Planning and Technology,1986,11(1):19-25.
[102]Persaud B N, Hall F L. Catastrophe theory and patterns in 30-second freeway traffic data implications for incident detection[J]. Transportation Research, Part A (General),1989,23,103-113.
[103]Acha-Daza J A. The application of catastrophe theory to traffic flow variables[J]. Transportation Research Part B:Methodological,1994,28(3):235-250.
[104]王晓原,隽志才,朴基男.局部比较的变点统计理论及其在交通流突变研究中的应用[J].公路交通科技,2002,19(6)：112-115.
[105]王英平,王殿海,杨少辉.突变理论在交通流分析理论中应用综述[J].交通运输系统工程与信息,2005,5(6)：68-71.
[106]陈涛,陈森发.基于突变理论的拥挤控制模型研究[J].系统工程学报.2006,211(6)：598-605.
[107]郭健,陈兴林,金鸿章.基于尖点突变对交通流模型的研究[J].控制与决
策,2008,23(2)：237-240.
[108]Disbro J E, Frame M. Traffic flow theory and chaotic behaviour[J]. Transportation research record,1989,1225:109-115.
[109]Dendrinos D S. Traffic-flow dynamics:A search for chaos[J]. Chaos, Solitons and Fractals,1994,4(4):605-617.
[110]李英,刘豹,马寿峰.交通流时间序列中混沌特性判定的替代数据方法[J].系统工程,2000,18(6)：54-58.
[111]宗春光,宋靖雁,任江涛.基于相空间重构的短时交通流预测研究[J].公路交通科技,2003,20(4)：71-75.
[112]Huang Kun, Chen Senfa, Zhou Zhenguo. Research on a non-linear chaotic prediction model for urban traffic flow[J]. Journal of Southeast University (English Edition),2003, 19(4):411-413.
[113]王东山,贺国光.交通混沌研究综述与展望[J].土木工程学报,2003,36(1)：68-74.
[114]王进,史其信.基于延迟坐标状态空间重构的短期交通流预测研究[J].ITS通讯,2005,7(1)：14-18.
[115]田晶,杨玉珍,陈阳舟.短时交通流量两种预测方法的研究[J].公路交通科技,2006,23(4)：103-106.
[116]Xue Jieni, Shi Zhongke. Short-time traffic flow prediction based on chaos time series theory [J]. Journal of Transportation Systems Engineering and Information Technology, 2008,8(5):68-72.
[117]Chrobo K R, Wahl E J, Schreckenbergm. Traffic forecast using simulations of large scale networks[C].4th IEEE Int. Conf. Intelligent Transportaion Systems, Oakland, CA,2001: 434-439.
[118]李奎,王坚.基于微观仿真的快速路短时交通流预测[J].安徽理工大学学报(自然科学版).2008,28(4)：47-52.
[119]马云龙,王坚,任子晖.基于微观仿真的快速路短时交通流预测研究[J].系统仿真学报,2009,21(14)：4501-4503.
[120]李一龙.基于动态规划的城市交通流预测与分配[J].交通运输系统工程与信息,2009,9(3)：135-139.
[121]Park D, Rilett L. Forecasting multiple-period freeway link travel times usingmodular neural networks[J]. Transportation Research Record,1998,1617:163-170.
[122]Abdulhai B, Porwal H, Recker W. Short-term traffic flow prediction using neural genetic algorithms[J]. ITS Journal,2002,7(1):35-41.
[123]Lingras P, Sharma S, Zhong M. Prediction of recreational travel using genetically designed regression and time delay neural networks [J]. Transportation Research Record, 2002,1805:16-24.
[124]刘洁,魏连雨,杨春风.基于遗传一神经网络的交通量预测[J].长安大学学报(自然科学 版),2003,23(1)：68-70.
[125]Liu M Z, Wang R L, Wu J S, et al. A genetic-algorithm-based neural network approach for short-term traffic flow forecasting[C]. Second International Symposium on Neural Networks:Advances in Neural Networks. Chongqing China,2005:965-970.
[126]Tao Ji, Qingle Pang, Xinyun Liu. Study of traffic flow forecasting based on genetic neural network[C]. Intelligent Systems Design and Applications,2006. ISDA'06. Sixth International Conference on,2006,1:960-965.
[127]Zhanquan Sun, Yinglong Wang, Jingshan Pan. Feature selection to traffic flow forecasting[C]. Proceedings of the 7th World Congress on Intelligent Control and Automation,2008:2864-2869.
[128]Matsui H, Fujita M. Travel time prediction for freeway traffic information by neural network driven fuzzy reasoning[C]. Neural Networks in Transport Application. Brookfield: Ashgate Publishing Company,1998:355-364.
[129]Yuan Zhenzhon, Li Weiyi, Liu Haidong. The forecast of dynamic traffic flow[C]. Proceedings of 2nd International Conference on Traffic and Transportation Studies. Beijing:Northern Jiaotong University,2000:507-512.
[130]Yin H, Wong S C, Xu J, et al. Urban traffic flow prediction using a fuzzy neural approach[J]. Transportation Research Part C,2002,10(2):85-98.
[131]Gang Tong, Chunling Fan, Fengying Cui, et al. Fuzzy neural network model applied in the traffic flow prediction[C]. Information Acquisition,2006 IEEE International Conference on,2006:1229-1233.
[132]董超俊,刘智勇.多层混沌神经网络及其在交通量预测中的应用[J].系统仿真学报,2007,19(10)：10-16.
[133]Boto-Giralda D, Anton-Rodriguez M, Diaz-pernas F J, et al. Neural network model based on fuzzy ARTMAP for forecasting of highway traffic data[J]. Lecture Notes Electrical Engineering,2008,15:95-105.
[134]Wen Guo, Yizheng Qiao, Haiyan Hou. BP neural network optimized with PSO algorithm and its application in forecasting[C]. Information Acquisition,2006 IEEE International Conference on,2006:617-621.
[135]赵建玉,贾磊,杨立才.基于粒子群优化的RBF神经网络交通流预测[J].公路交通科技,2006,23(7)：116-119.
[136]Hui Gao, Jianyu Zhao, Lei Jia. Short-term traffic flow forecasting model of elman neural network based on dissimilation particle swarm optimization[C]. IEEE International Conference on Networking, Sensing and Control,2008,4:1305-1309.
[137]Mascha V D, Dougherty M, Watson S. Combining Kohonen maps with ARIMA time series models to forecast traffic flow[J]. Transfortation Research Part C,1996,.4(5):307-318.
[138]谭满春,冯荦斌,徐建闽.基于ARIMA与人工神经网络组合模型的交通流预测[J].中国公路学
报,2007,20(4)：118-121.
[139]张晓利,贺国光.基于主成分分析和组合神经网络的短时交通流预测方法[J].系统工程理论与实践,2007,(8)：167-171.
[140]杜长海,黄席樾,杨祖元.基于神经网络和Markov链的交通流实时滚动预测[J].系统仿真学报,2008,20(9)：2464-2468.
[141]Darong H, Xing-rong B. A wavelet neural network optimal control model for traffic-flow prediction in intelligent transport systems[J]. Lecture Notes in Computer Science,2007, 20(6):59-65.
[142]朱胜雪,周君,包旭.基于小波分解—支持向量机的短时交通量预测[J].苏州科技学院学报(工程技术版),2007,20(3)：79-82.
[143]任其亮.基于小波与混沌集成的短时交通流预测[J].重庆交通大学学报自然科学版,2008,27(4)：630-633.
[144]Liang Zhao, Fei-Yue Wang. Short-term fuzzy traffic flow prediction using self-organizing TSK-type fuzzy neural network[C]. IEEE International Conference on Vehicular Electronics and Safety,2007:1-6.
[145]Kai Cao, Xiu-song Liu, Feng Cao, et al. A dynamic traffic network monitoring algorithm[C]. Intelligent Transportation Systems Conference,2006. ITSC'06. 2006:481-486.
[146]Cao Kai, Kai-Wang Zhang, Hamamatsu Y. Traffic information real-time monitoring based on a short-long term algorithm[C]. Systems, Man and Cybernetics,2006. ICSMC'06. IEEE International Conference on,2006,1:651-656.
[147]Liu Bin-sheng, Xing Zhan-wen, Yang Hai-tao. A combination predicted model of short term traffic flow[C]. Management Science and Engineering,2006. ICMSE'06.2006 International Conference on,2006:2075-2080.
[148]李婧瑜,李歧强,侯海燕.基于遗传算法的小波神经网络交通流预测[J].山东大学学报(工学版),2007,37(2)：109-112.
[149]谭满春,李英俊,徐建闽.基于小波消噪的ARIMA与SVM组合交通流预测[J].公路交通科技.2009,26(7)：127-132.
[150]马俊.交通流理论基础[M].北京：中国人民公安大学出版社,2004.
[151]刘焰,杨亚芬.交通工程学[M].北京：机械工业出版,2004.
[152]姜桂艳,丁同强.交通工程学[M].北京：国防工业出版社,2007.
[153]Tan G Z, Yuan W J, Ding H. Traffic flow prediction based on generalized neural network. Proceedings of the 7th IEEE International Conference on Intelligent Transportation Systems, Washington, USA,2004:406-409.
[154]田珠.大规模路网实时交通流预测模型及应用研究[D].大连：大连理工大学,2007.
[155]黄欣,杨新苗,常玉林,等.城市交通拥挤的成因探析[J].交通运输工程与信息学报,2007,5(2)：108-113.
[156]谭国真,瞿晓高.大连市实时交通信息发布系统[J].计算机系统应用,2003,15(07)：50-52.
[157]王炜,过秀成.交通工程学[M].南京：东南大学出版社,2000.
[158]徐循初.城市道路网系统规划思想及实例解析[J].城市交通,2006,4(1)：30-35.
[159]Cambridge Systematics, Inc. Traffic congestion and reliability-final report of the trends and advanced strategies for congestion mitigation [R].Massachusetts:FHWA,2005.
[160]Fan Wang, Chao Deng, Huimin Shi, et al. Support vector machine based on data mining technology in traffic flow forecasting[J]. Journal of Information and Computational Science,2009,6(3):1287-1294.
[161]Fan Wang, Guozhen Tan, Chao Deng, et al. Real-time traffic flow forecasting model and parameter selection based on ε-SVR[C]. In Proceedings of the 7th World Congress on Intelligent Control and Automation,2008:2870-2875.
[162]朱顺应,王红,向红艳.交通流参数及交通事件动态预测方法[M].东南大学出版社,2008.
[163]J. W. C. van Lint. Reliable real-time framework for short-term freeway travel time prediction[J]. Journal of Transportation Engineering,2006,132(12):921-932.
[164]张学工译.统计学习理论的本质[M].北京：清华大学出版社,2000.
[165]孙向东,刘拥军,黄保续,等.蛋白质结构预测-支持向量机的应用[M].北京：科学出版社2008.
[166]Keerthi S S, Lin C J. Asymptotic behaviors of support vector machines with Gaussian kernel[J].Neural Computation.2003,15(7):1667-1689
[167]朱国强,刘士荣,俞金寿.支持向量机及其在函数逼近中的应用[J].华东理工大学学报,2002,18(5)：555-559,568
[168]乔弘,周黎辉,潘卫华等.基于参数自适应SVR的火电厂热工参数软测量[J].华北电力大学学报,2008,35(2)：94-97.
[169]Buers J,.FruminM, Hom G, et al. FLID-DL:Congestion control for layered multicast[C]. Proc. Networked Group Commucication (NGC2000). Palo Alto, USA,2000:71-81.
[170]Wahba G. Support vector machine, reproducing kernel hilbert spaces and the randomized GACV[R]. In:Scholkopf B, Burges C, Smola A, ed. Advances in Kernel Methods-Support Vector Learning. Cambridge, MA:MITPress,1999.
[171]Joachims T. Estimating the generalization performance of a SVM efficiently[C]. Proceedings of the International Conference on Machine Learning, San Francisco:Morgan Kaufman,2000:431-438.
[172]Lee J H, Lin C J. Automatic model selection for support vector machines:Technical Report[R]. Taiwan:Department of Computer Science and Information Engineering, National Taiwan University,2000.
[173]Keerthi S S, Chong J, Martin S L. Two efficient methods for computing leave-one-out error in SVM algorithms:Techinical Report CD-00-10[R]. Singapore:Dept. of Mechanical Engineering National University of Singapore,2001.
[174]Cawley G C, Talbot N L C. Fast exact leave-one-out cross-validation of sparse least-squares support vector machines[J]. Neural Networks,2004,17(10):1467-1475.
[175]Ming-Wei Chang, Chih-Jen Lin. Leave-one-out bounds for support vector regression model selection[J]. Neural Computation,2005,17:1188-1222.
[176]Chapelle 0, Vapnik V, Bousquet O, et al. Choosing multiple parameters for support vector machines[J]. Machine Learning,2002,46(1):131-159.
[177]Chen P W, Wang J Y, Lee H M. Model selection of SVMs using GA approach[C]. In: Proceedings of 2004 IEEE International Joint Conference on Neural Networks, Budapest, Hungary,2004,3:2035-2040.
[178]Wang X, Qin C H, Gui W H. Parameter selection of support vector regression based on hybrid optimization algorithm and its application[J]. Journal of Control Theory and Application,2005,3(4):372-376.
[179]邵信光,杨慧中,陈刚.基于粒子群优化算法的支持向量机参数选择及应用[J].控制理论及应用,2006,23(5)：740-743.
[180]Ef ron B, Hastie T, Johnstone I, et al. Least angle regression [J]. Annals of Statistics, 2004,32 (2):407-499.
[181]Hastie T, Rosset S, Tibshirani R. The entire regularization path for the support vector machine[J]. Journal of Machine Learning Research,2004,5:1391-1415.
[182]袁小芳,王耀南.基于混沌优化算法的支持向量机参数选取方法[J].控制与决策,2006,21(1)：111-113.
[183]刘昌平,范明钰,王光卫.基于梯度算法的支持向量机参数优化方法[J],控制与决策,2008,23(11)：1291-1295.
[184]熊伟丽,徐保国.基于PSO的SVR参数优化选择方法研究[J].系统仿真学报,2006,18(9)：2442-2445.
[185]杜京义,侯媛彬.基于遗传算法的支持向量回归机参数选取[J].系统工程与电子技术,2006,28(9)：1430-1433.
[186]Vladimir Cherkassky, Yunqian Ma. Practical selection of SVM regression parameters and noise estimation for SVM regression[J]. Neural Networks,2004,17(1):113-126.
[187]李存军.基于集成神经网络的城市道路交通流量融合预测研究[D].成都：西南交通大学,2005.
[188]Freeway Performance Measurement System (PEMS), version7.0 [Online].
[189]任世锦,吴铁军.基于径向基小波核的多尺度小波支持向量机[J].电路与系统学报,2008,13(4)：70-75.
[190]Scholkopf B, Smola A, Muller K. Nonlinear component analysis as a kernel eigenvalue problem[J]. Neural Computations,1998,10(5):1299-1319.
[191]Mike S, WestonJ, Mullet K, et al. Fisher discriminant analysis with kernels [J]. Neural Networks for Signal Processing IX,1998:41-48.
[192]Weinert H. L. Reproducing kernel hibert space[M]. Hutchinson Ross:Stroudsburg,1999.
[193]Ha-Nam Nguyen, Syng-Yup Ohn, Jaehyun Parke, et al. Combined kernel function approach in SVM for diagnosis of cancer[J]. Lecture Notes in Computer Science,2005, 3610:1017-1026.
[194]Mingzhu L, PHILIP C, Jianbing H. Optimization of combined kernel function for SVM by particle swarm optimization[C]. Proceedings of the Eighth International Conference on Machine Learning and Cybernetics,2009:1160-1166.
[195]封筠,陈志军,李莉蓉.基于修正核函数的SVM分类器研究[J].系统仿真学报,2006,18(3)：570-572.
[196]Christian Wallraven, Barbara Caputo. Recognition with local features:the kernelrecipe[C]. Proceedings of the Ninth IEEE International Conference on Computer Vision,2003,1:257-264.
[197]吴涛,贺汉根,贺明科.基于插值的核函数构造[J].计算机学报,2003,26(8)：990-996.
[198]张淮清,俞集辉,郑亚利.径向基函数及其耦合方法在电磁场数值计算中的应用[J].计算物理,2009,26(2)：299-309.
[199]黄啸,浦小祥.SVM核函数的研究及其在语音激活检测中的应用[J].苏州大学学报,2008,28(3)：56-59.
[200]李元诚,方廷健,郑国祥.短期电力负荷预测的小波支持向量机方法研究[J].中国科学技术大学学报,2003,33(6)：726-732.
[201]Zhang L, Zhou W, Jiao L. Wavelet support vector machine[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics,2004,34(1):34-39.
[202]隋学深,齐中英.基于多尺度特征和支持向量机的股市趋势预测[J].哈尔滨工业大学学报,2008,10(4)：77-81.
[203]郭磊,陈进,朱义等.小波支持向量机在滚动轴承故障诊断中的应用[J].上海交通大学学报,2009,43(4)：678-682.
[204]飞思科技产品研发中心.小波分析理论与Matlab7实现[M].北京：电子工业出版社,2005.
[205]Yang F S. Engineer analysis and application of wavelet transform[M]. Beijing:Science Press,2000.
[206]Mercer J. Function of positive and negative type and their connection with the theory of integral equations[J]. Philos. Trans. Roy. Soc. London,1909, A209:415-446.
[207]Burges C J C. Geometry and invariance in kernel based methods[M]. Advance in Kernel Methods support Vector Learning. Cambridge:MIT Press,1999.
[208]李青.核优化方法[D].西安：西安电子科技大学,2007.
[209]Junshui Ma, James Theiler, Simon Perkins. Accurate on-line support vector regression[J]. Neural Computation,2003,15(11):2683-2703.
[210]Syed N A S, Liu H, Sung K. From incremental learning to model independent instance selection-a support vector machine approach [R]. Singapore:National University of
Singapore,1999.
[211]Mitra P, Murthy C A, Pal S K. Data Condensation in large databases by incremental learning with support vector machines[J]. Int. Conf. Pattern Recognition,2000:708-711.
[212]Cauwenberghs G, Poggio T. Incremental and decremental support vector machines[J]. Machines Learning,2001,44(13):409-415.
[213]Ralaivola L, Alche-Buc F. Incremental support vector machine learning:A local approach[J]. Lecture Notes in Computer Science, Springer 2001(2130):322-330.
[214]萧嵘,王继成,孙正兴.一种SVM增量学习算法[J].软件学报,2001,12(12)：1818-1824.
[215]曾文华,马健.一种新的SVM增量学习算法[J].厦门大学学报(自然科学版).2002,41(6)：687-691.
[216]李凯,黄厚宽.支持向量机增量学习算法研究[J].北方交通大学学报,2003,27(5)：34-37.
[217]史朝辉,王晓丹,杨建勋.一种SVM增量训练淘汰算法[J].计算机工程与应用,2005,23：187-189.
[218]曾嵘,刘建成,蒋新华.一种基于支持向量机的增量学习算法[J].铁道科学与工程学报,2005,2(1)：94-96.
[219]李忠伟,张健沛,杨静.基于支持向量机的增量学习算法研究[J].哈尔滨工程大学学报,2005,26(5)：643-646.
[220]孔波,刘小茂,张钧.基于中心距离比值的增量SVM[J].计算机应用,2006,26(6)：1434-1436.
[221]毛建洋,黄道.一种新的支持向量机增量算法[J].华东理工大学学报(自然科学版),2006,32(8)：989-991.
[222]李东晖.基于壳向量的线性支持向量机快速增量学习算法[J].浙江大学学报(工学版),2006,40(2)：202-206.
[223]孔锐,张冰.一种快速支持向量机增量学习算法[J].控制与决策,2005,20(10)：1129-1132.
[224]廖东平,魏玺章,黎湘.一种支持向量机增量学习淘汰算法[J].国防科技大学学报,2007,29(3)：65-70.
[225]田翔,邓飞其.精确在线支持向量回归在股指预测中的应用[J].计算机工程,2005,31(22)：18-20.
[226]刘大同,彭宇,彭喜元.基于残差预测修正的局部在线时间序列预测方法[J].电子学报.2008,36(12A)：81-85.
[227]Manoel Castro-Neto, Young-Seon Jeong, Myong-Kee Jeong, et al. Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions[J]. Expert Systems with Applications,2008,36(3):6164-6173.
[228]吴微,张凌.自适应参数的AOSVR算法及其在股票预测中应用[J].大连理工大学学报.2009,49(4)：605-610.
[229]Giovanni Montana, Francesco Parrella. Learning to trade with incremental support vector regression [J]. Lecture Notes in Computer Science,2008,5271:591-598.
[230]曹凯,李士明.一种基于在线训练的交通信息实时预测快速算法[J].数学的实践与认识,2008,38(11)：73-75.
[231]邓清清.交通诱导系统的流量预测和路径优化并行算法研究[D].大连：大连理工大学,2008.
[232]陈国良.并行算法实践[M].北京：高等教育出版社,2004.
[233]Collobert R, Bengio S, Bengio Y. A parallel mixture of SVMs for very large scale problems[J]. Neural Computation,2002,14(5):1105-1114.
[234]Dong J X, Krzyzak A. Fast SVM training algorithm with decomposition on very large datasets[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence,2005, 27:89-95.
[235]Zanghirati G, Zanni L. A parallel solver for large quadratic programs in training support vector machines[J]. Parallel Computing,2003,29:535-551.
[236]Zanni L, Seraf ini T, Zanghirati G. Parallel software for training large scale support vector machines on multiprocessor systems[C]. PPJMLR,2006:1467-1492.
[237]Zanni L. An improved gradient p rojection-based decomposition technique for support vector machines[J]. Computational Management Science,2006,3(2):131-145.
[238]林继鹏,刘君华,凌振宝.并行支持向量机算法及其应用[J].吉林大学学报(信息科学版),2004,22(5)：45-53.
[239]Graf H P, Cosatto E, Bottou L, et al. Parallel support vector machines:the cascade SVM [C]. Advances in Neural Information Processing Systems. Cambridge, MA:MITPress, 2005:521-528.
[240]Yang J. An improved cascade SVM training algorithm with crossed feedbacks[C]. IEEE Proceedings of IMSCCS'06. Hangzhou,2006,2:735-738.
[241]LUB L, WANG K A, WEN Y M. Comparison of parallel and cascade methods for training support vector machines on large-scale problems [J]. Machine Learning and Cybernetics, 2004,5:3056-3061.
[242]贾华丁,游志胜,王磊.基于MTC结构的支持向量机并行训练算法[J].四川大学学报(工程科学版),2007,39(6)：123-128.
[243]文益民.基于支持向量的分层并行筛选训练样本方法[J].计算机工程,2006,32(21)：177-179.
[244]雷咏梅,王雄,郭恒明.一种基于支持向量机的并行训练策略[J].上海大学学报(自然科学版).2007,13(25)：545-549
[245]Runarsson T P, Sigurdsson S. Asynchronous Parallel Evolutionary Model Selection for Support Vector Machines[J]. Neural Information Processing-Letters and Reviews.2004, 3(3):59-68.
[246]刘胜,江娜.基于SVM的故障预报中的并行优化仿真研究[J].系统仿真学报,2008,20(13)：3445-3448.
[247]Cao L J, Keerthi S S, Ong C J, et al. Parallel sequential minimal optimization for the training of support vector machines[J]. IEEE Trans. Neural Network,2006,17(4): 1039-1049.
[248]都志辉.MPI并行程序设计[M].北京：清华大学出版社,2006.
[249]周翔,荣建,任福田.Mobitex无线网在动态交通信息服务系统中的应用[J].计算机应用与软件.2009,26(1)：69-71.
[250]张伟,梅宏.一种面向特征的领域模型及其建模过程[J].软件学报,2003,14(8)：1345-1356.
[251]王凡,谭国真,王皓.面向交通领域构件的特征建模方法研究.计算机工程.2009,35(1)：280-282.
[252]袁剑君.试论交通信息服务的关键问题[J].湖南交通科技.2009,35(1)：170-172.
[253]刘学军,徐鹏.交通地理信息系统[M].北京：科学出版社,2006.
[254]黄罡,王千祥,曹东刚等.PKUAS:一种面向领域的构件运行支撑平台[J].电子学报,2002,30(12A)：1938-1942
[2]杨兆升.新一代智能交通控制系统关键技术及其应用[M].北京：中国铁道出版社,2008
[3]《中国智能运输系统体系框架》专题组著.中国智能运输系统体系框架[M].北京：人民交通出版社,2003.
[4]朱茵,王军利,周彤梅.智能交通系统导论[M].北京：中国人民公安大学出版社,2007.
[5]陆化普.智能交通系统概论[M].北京：人民交通出版社,2004.
[6]翁小雄,谭国贤,姚树申.城市交叉口交通流特征与短时预测模型[J].交通运输工程学报,2006,6(2)：103-107.
[7]Smith-BL, Demetsky M J. Traffic flow forecasting:comparison of modeling approaches[J]. Journal of Transportation Engineering,1997,123(4):261-266.
[8]贺国光,李宇,马寿峰.基于数学模型的短时交通流预测方法探讨[J].系统工程理论与实践,2000,(12)：51-56.
[9]刘静,关伟.交通流预测方法综述[J].公路交通科技,2004,(3)：82-85.
[10]姚智胜,邵春福.道路交通状态预测研究[J].哈尔滨工业大学学报,2009,41(4)：247-249.
[11]王进,史其信.短时交通流预测模型综述[J].中国公共安全,2005,1(1)：92-98.
[12]Ahmaed M S, Cook A R. Analysis of freeway traffic time-series data by using Box-Jenkins technique[J].Transportation Research Record,1979,722:1-9.
[13]Davis G A, Nihan N L. Using time-series designs to estimate changes in freeway level of service, despite missing data[J]. Transportation Research Part A:General,1984, 18(5):431-438.
[14]Kim Changkyun, Hobeika. Antonie G. Short-term demand forecasting model from real-time traffic data[C]. Proceedings of the Infrastructure Planning and Management, USA:ASCE, 1993:540-550.
[15]Lee S, Fambro D B. Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting[C]. Transportation Research Record, Washington DC,1999,1678:179-188.
[16]Willams B M.Multivariate vehicular traffic flow prediction:an evaluation of ARIMAX modeling [C]. Transportation Research Record, Washington DC,2001,1776:194-200.
[17]Kamarianakis Y, Prastacos P. Forecasting traffic flow conditions in an urban network: comparison of multivariate and univariate approaches[C]. Transportation Research Record, Washington DC,2003,1857:74-84.
[18]Willams B M, HOEL L A.Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process:a theoretical basis and empirical results [J]. Journal of Transportation Engineering,2003,129(6):583-588.
[19]韩超,宋苏,王成红.基于ARIMA模型的短时交通流实时自适应预测[J].系统仿真学报,2004,16(7)：1530-1535.
[20]张辉,刘嘉,柳湘月等.交通流的季节ARIMA模型与预报[J].天津大学学报,2008,25(1)：129-133.
[21]孙湘海,刘潭秋.基于SARIMA模型的城市道路短期交通流预测研究[J].公路交通科技,2008,25(1)：129-133.
[22]陈岳明,萧德云.基于跳转模型的路网交通流预测[J].控制与决策,2009,24(8)：1177-1180.
[23]李振龙,张利国,钱海峰.基于非参数回归的短时交通流预测研究综述[J].交通运输工程与信息学报,2008,6(4)：34-37.
[24]Oswald R K, Scherer W T, Smith B. L. Traffic flow forecasting using approximate nearest neighbor nonparametric regression[R]. Research Report No. UVACTS-15-13-7, Center for Transportation Studies, University of Virginia,2001.
[25]达庆东,段里仁.交通流非参数回归模型[J].数理统计与管理,2003,22(4)：41-46.
[26]王晓原,张敬磊.基于非参数样条拟合的交通流预测方法研究[J].计算机工程与应用,2006,42(26)：218-221.
[27]Davis G A, Nihan N L. Nonparametric regression and short-term freeway traffic forecasting[J]. Journal of Transportation Engineering,1991,117(2):178-188.
[28]Smith B L, Demetsky M J. Traffic flow forecasting:comparison of modeling approaches[J]. Journal of Transportation Engineering,1997,123(4):261-266.
[29]Smith BL, Williams BM, Oswald RK. Comparison of parametric and nonparametric modelsfor traffic flow forecasting[J]. Transportation Research Part C,2002,10(4):303-321.
[30]Smith B L, Oswald R K. Meeting real time traffic flow forecasting requirements with imprecise computations [J]. Computer-aided Civil and Infrastructure Engineering,2003, 18(3):201-213.
[31]Clark S. Traffic prediction using multivariate nonparametric regression[J]. Journal of Transportation Engineering,2003,129(2):161-167.
[32]宫晓燕,汤淑明.基于非参数回归的短时交通流量预测与事件检测综合算法[J].中国公路学报,2003,16(1)：82-86.
[33]周小鹏,冯奇,孙立军.基于最近邻法的短时交通流预测[J].同济大学学报(自然科学版),2006,34(10)：1494-1498.
[34]张晓利,贺国光.考虑交通吸纳点的非参数回归组合型短时交通流预测方法[J].系统工程,2006,24(12)：21-25.
[35]张晓利,贺国光,陆化普.基于K-邻域非参数回归短时交通流预测方法[J].系统工程学报,2009,24(2)：178-183.
[36]Iwao O, Stephanedes Y J. Dynamic prediction of traffic volume through kalman filtering theory[J]. Transportation Research Part B,1984,18(1):1-11.
[37]Vythoulkas P C. Alternative approaches to short-term traffic forecasting for use in
driver information systems[J]. Transportation and Traffic Theory,1993,12:485-506.
[38]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,(3)：63-67.
[39]YangF, Yin Z, Liu HX. Online recursive algorithm for short-term traffic predict ion [J]. Transportation Research Record,2004,1879:1-8.
[40]李嘉,刘小兰.基于卡尔曼滤波理论的交通信息预测模型及其应用[J].中南公路工程,2005,30(4)：108-123.
[41]Ye Zhirui, Zhang Yunlong, Middleton D R. Unscented Kalman filter method for speed estimation using single loop detector data[J]. Transportation Research Record,2006, 1968:117-125.
[42]王均,关伟.基于Kalman滤波的城市环路交通流短时预测研究[J].交通与计算机,2006,24(5)：16-19.
[43]Wang Y, Papageorgiou M, Messmer A. A real-time freeway network traffic surveillance tool[J]. IEEE Trans. Control Systems Technology,2006,14(1):18-32.
[44]Yibing W, Papageorgiou M, Messmer A. Real-time freeway traffic stateestimation based on extended kalman filter:a case study [J]. Transportation Science,2007,41(2):167-181.
[45]聂佩林,余志,何兆成.基于约束卡尔曼滤波的短时交通流量组合预测模型[J].交通运输工程学报,2008,8(5)：86-90.
[46]Dougherty M. A review of neural networks applied to transport[J]. Transportation Research Part C,1995,3(4):247-260.
[47]Zhang H J, Ritchie S G, Lo Z P. Macroscopic modeling of freeway traffic using an artificial neural network[J]. Transportation Research Record,1997,1588:110-119.
[48]Faghri A, Hua J. Evaluation of artificial neural network applications in transportation engineering[J]. Transportation Research Record,1992,1358:71-80.
[49]Dougherty M S, Kirby H C. The use of neural networks to recognize and predict traffic congestion[J]. Traffic Engineering and Control,1993,34(6):311-314.
[50]Park B, Messer C J, Urbanik T. Short-term freeway traffic volume forecasting using radial basis function neural network[J]. Transportation Research Record,1998, 1651:39-47.
[51]Abdulhai B, Porwal H, Recker W. Short-term freeway traffic flow prediction using genetically optimized time delay based neural networks[R]. Institute of Transportation Studies, University of California,1998.
[52]Dia H. An object-oriented neural network approach to shortterm traffic forecasting [J]. European Journal of Operational Research,2001,131(2):253-261.
[53]Ishak S, ASCE M, Alecsandru C. Optimizing traffic prediction performance of neural networks under various topological input and traffic condition settings[J]. Journal of Transportation Engineering,2004,130(7):452-465.
[54]Xiao H, Sun H, Ran B. Special factor adjustment model using fuzzy-neural network in traffic prediction[J]. Transportation Research Record,2004,1879:17-23.
[55]Dia H. An object-oriented neural network approach to short-term traffic forecasting[J]. European Journal of Operational Research,2001,131(2):253-261.
[56]Vanajakshi L, Rilett L R. A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed[C]. In IEEE intelligent vehicles symposium,2004:194-199.
[57]Zhang H M. Recursive prediction of traffic conditions with neural network models [J]. Journal of Transportation Engineering,2000,126(6):472-481.
[58]Yun S Y, Namkoong S. A performance evaluation of neural network models in traffic volume forecasting [J]. Mathematical and Computer Modeling,1998,27(6):293-310.
[59]Guozhen Tan, Qingqing Deng, Zhu Tian, et al. A dish parallel BP for traffic flow forecasting [C]. International Conference on Computational Intelligence and Security, Harbin,2007:546-549.
[60]Jiang X, Adeli H. Dynamic wavelet neural network model for traffic flow forecasting [J]. Journal of Transportation Engineering,2005,131(10):771-779.
[61]Lingras P, Sharma S. C, Osborne P, et al. Traffic volume time-series analysis according to type of road use[J]. Computer-Aided Civil and InfrastructureEngineering,2000, 15(5):365-373.
[62]Park B. Hybrid neuro-fuzzy application in short-term freeway traffic volume forecasting[J]. Transportation Research Record,2002,1802:190-196.
[63]Zheng W, Lee D. H, Shi Q. Short-term freeway traffic flow prediction:Bayesian combined neural network approach[J]. Journal of Transportation Engineering,2006, 132(2):114-121.
[64]Smith B L, Demetsky M J. Short-term traffic flow prediction:Neural network approach [J]. Transportation Research Record,1995,1453:98-104.
[65]Chang E C. Traffic estimation for proactive traffic control[J]. Transportation Research Record,1999,1679:81-86.
[66]Chen H, Grant-Muller S. Use of sequential learning for short-term traffic flow forecasting [J]. Transportation Research Part C,2001,9(5):319-336.
[67]Dougherty M S., Cobbett M R. Short-term inter-urban traffic forecast using neural networks [J]. International Journal of Forecasting,1997,13(1):21-31.
[68]Kirby H R., Watson S M, Dougherty M S. Should we use neural networks or statistical models for short-term traffic forecasting[J]. International Journal of Forecasting, 1997,13(1):43-50.
[69]Vlahogianni E I, Karlaftis M G, Golias J C. Optimized and metaoptimized neural networks for short-term traffic flow prediction:A genetic approach [J]. Transportation Research
Part C,2005,13(3):211-234.
[70]Zhong M, Sharma S, Lingras P. Short-term traffic prediction on different types of roads with genetically designed regression and time delay neural network models[J]. Journal of Computing in CivilEngineering,2005,19(1):94-103.
[71]Manoel Castro-Neto, Young-Seon Jeong, Myong-Kee Jeong, et al. Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions [J]. Expert Systems with Applications,2009,36(3):6164-6173.
[72]V. N. Vapnik. The Nature of Statistical Learning Theory[M]. New York:Springer,1995..
[73]邓乃扬,田英杰.数据挖掘中的新方法—支持向量机[M].北京：科学出版,2004.
[74]Ren J T, Ou X L, Zhang Y. Research on network level traffic pattern recognition[C]. IEEE 5th International Conference on Intelligent Transportation Systems, Singapore, 2002:500-504.
[75]Wu C H, Wei C C, Chang M. Travel time prediction with support vector regression [J]. IEEE Transaction on Intelligent Transportation Systems.2004,5(12):276-281.
[76]Scholkopf B, Alex J. New Support Vector Algorithms [J]. Neural Computation,2000,12(5): 1207-1245.
[77]丁爱玲.基于统计学习理论的交通流量时间序列预测[J].交通与计算机,2002,20(2)：27-30.
[78]Ding A, Zhao X, Jiao L. Traffic flow time series prediction based on statistics learning theory[C]. In Proceedings IEEE 5th Int. Conference on Intelligent Transportation Systems,2002:727-730.
[79]徐启华,杨瑞.支持向量机在交通流量实时预测中的应用[J].公路交通科技,2005,22(12)：131-134.
[80]蒋刚,肖建.基于核机器方法的城市交通流量实时预测[J].计算机工程,2006,32(17)：48-51.
[81]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.
[82]姚智胜,邵春福,高永亮.基于支持向量回归机的交通状态短时预测方法研究[J].北京交通大学学报,2006,30(3)：19-22.
[83]姚智胜,邵春福,熊志华.基于主成分分析和支持向量机的道路网短时交通流量预测[J].吉林大学学报(工学版),2008,38(1)：48-52.
[84]张朝元,陈丽.改进的LS-SVM算法及在交通流量预测上的应用[J].昆明理工大学学报(理工版),2008,33(6)：72-75.
[85]杨建华,于小宁.高速公路动态交通流支持向量机预测模型[J].西安工业大学学报,2009,29(3)：280-284.
[86]贺国光,马寿峰,李宇.基于小波分解与重构的交通流短时预测法[J].系统工程理论与实践,2002,(9)：101-106.
[87]Chang S C, Kim R S, Kim S. J. Traffic flow forecasting using a 3-stage model [C]. IEEE
Intelligent Vehicles Symposium, Proceedings,2000:451-456.
[88]Kaye BH. A random walk through fractal dimensions [M]. New York:VCH Publishers,1989.
[89]王正武,黄中祥.短时交通流预测模型的分析与评价[J].系统工程,2003,21(6)：97-100.
[90]Frankhouser P. Aspects Fractals des Structures Urbaines[J]. L Espace Geographic, 1990,19(1):45-49.
[91]刘继生,陈彦光.交通网络空间结构的分形维数及其测算方法探讨[J].地理学报,1999,54(5)：471-478.
[92]李江,郭庆胜.基于GIS的城市交通网络复杂性定量描述[J].华中师范大学学报(自然科学版),2002,36(4)：534-537.
[93]刘妙龙,黄佩蓓.分形理论在城市交通网络时空演变特征研究中的应用[J].武汉大学学报(信息科学版),2003,28(6)：749-753.
[94]刘妙龙,黄佩蓓.上海大都市交通网络分形的时空特征演变研究[J].地理科学,2004,24(2)：144-149.
[95]姜世国,周一星.北京城市形态的分形集聚特征及其实践意义[J].地理研究,2006,25(2)：204-211.
[96]张鹏,韩增林.辽宁省公路交通网络的分形研究[J].交通运输系统工程与信息,2006,6(1)：123-127.
[97]郭建科,韩增林,许妍.基于集聚分形的大连城市交通网络演变研究[J].交通运输系统工程与信息,2007,7(5)：121-126.
[98]王秋平,张琦.城市中心区交通量时间序列的分形特征[J].西安建筑科技大学学报(自然科学版),2008,40(1)：101-107.
[99]凌复华.突变理论及其应用[M].上海：上海交通大学出版社,1987.
[100]Dendrinos D S. Operating speeds and volume to capacity ratios[J]. Transportation Research.1978,12:191-194.
[101]Navin F P D. Traffic congestion catastrophes[J]. Transportation Planning and Technology,1986,11(1):19-25.
[102]Persaud B N, Hall F L. Catastrophe theory and patterns in 30-second freeway traffic data implications for incident detection[J]. Transportation Research, Part A (General),1989,23,103-113.
[103]Acha-Daza J A. The application of catastrophe theory to traffic flow variables[J]. Transportation Research Part B:Methodological,1994,28(3):235-250.
[104]王晓原,隽志才,朴基男.局部比较的变点统计理论及其在交通流突变研究中的应用[J].公路交通科技,2002,19(6)：112-115.
[105]王英平,王殿海,杨少辉.突变理论在交通流分析理论中应用综述[J].交通运输系统工程与信息,2005,5(6)：68-71.
[106]陈涛,陈森发.基于突变理论的拥挤控制模型研究[J].系统工程学报.2006,211(6)：598-605.
[107]郭健,陈兴林,金鸿章.基于尖点突变对交通流模型的研究[J].控制与决
策,2008,23(2)：237-240.
[108]Disbro J E, Frame M. Traffic flow theory and chaotic behaviour[J]. Transportation research record,1989,1225:109-115.
[109]Dendrinos D S. Traffic-flow dynamics:A search for chaos[J]. Chaos, Solitons and Fractals,1994,4(4):605-617.
[110]李英,刘豹,马寿峰.交通流时间序列中混沌特性判定的替代数据方法[J].系统工程,2000,18(6)：54-58.
[111]宗春光,宋靖雁,任江涛.基于相空间重构的短时交通流预测研究[J].公路交通科技,2003,20(4)：71-75.
[112]Huang Kun, Chen Senfa, Zhou Zhenguo. Research on a non-linear chaotic prediction model for urban traffic flow[J]. Journal of Southeast University (English Edition),2003, 19(4):411-413.
[113]王东山,贺国光.交通混沌研究综述与展望[J].土木工程学报,2003,36(1)：68-74.
[114]王进,史其信.基于延迟坐标状态空间重构的短期交通流预测研究[J].ITS通讯,2005,7(1)：14-18.
[115]田晶,杨玉珍,陈阳舟.短时交通流量两种预测方法的研究[J].公路交通科技,2006,23(4)：103-106.
[116]Xue Jieni, Shi Zhongke. Short-time traffic flow prediction based on chaos time series theory [J]. Journal of Transportation Systems Engineering and Information Technology, 2008,8(5):68-72.
[117]Chrobo K R, Wahl E J, Schreckenbergm. Traffic forecast using simulations of large scale networks[C].4th IEEE Int. Conf. Intelligent Transportaion Systems, Oakland, CA,2001: 434-439.
[118]李奎,王坚.基于微观仿真的快速路短时交通流预测[J].安徽理工大学学报(自然科学版).2008,28(4)：47-52.
[119]马云龙,王坚,任子晖.基于微观仿真的快速路短时交通流预测研究[J].系统仿真学报,2009,21(14)：4501-4503.
[120]李一龙.基于动态规划的城市交通流预测与分配[J].交通运输系统工程与信息,2009,9(3)：135-139.
[121]Park D, Rilett L. Forecasting multiple-period freeway link travel times usingmodular neural networks[J]. Transportation Research Record,1998,1617:163-170.
[122]Abdulhai B, Porwal H, Recker W. Short-term traffic flow prediction using neural genetic algorithms[J]. ITS Journal,2002,7(1):35-41.
[123]Lingras P, Sharma S, Zhong M. Prediction of recreational travel using genetically designed regression and time delay neural networks [J]. Transportation Research Record, 2002,1805:16-24.
[124]刘洁,魏连雨,杨春风.基于遗传一神经网络的交通量预测[J].长安大学学报(自然科学 版),2003,23(1)：68-70.
[125]Liu M Z, Wang R L, Wu J S, et al. A genetic-algorithm-based neural network approach for short-term traffic flow forecasting[C]. Second International Symposium on Neural Networks:Advances in Neural Networks. Chongqing China,2005:965-970.
[126]Tao Ji, Qingle Pang, Xinyun Liu. Study of traffic flow forecasting based on genetic neural network[C]. Intelligent Systems Design and Applications,2006. ISDA'06. Sixth International Conference on,2006,1:960-965.
[127]Zhanquan Sun, Yinglong Wang, Jingshan Pan. Feature selection to traffic flow forecasting[C]. Proceedings of the 7th World Congress on Intelligent Control and Automation,2008:2864-2869.
[128]Matsui H, Fujita M. Travel time prediction for freeway traffic information by neural network driven fuzzy reasoning[C]. Neural Networks in Transport Application. Brookfield: Ashgate Publishing Company,1998:355-364.
[129]Yuan Zhenzhon, Li Weiyi, Liu Haidong. The forecast of dynamic traffic flow[C]. Proceedings of 2nd International Conference on Traffic and Transportation Studies. Beijing:Northern Jiaotong University,2000:507-512.
[130]Yin H, Wong S C, Xu J, et al. Urban traffic flow prediction using a fuzzy neural approach[J]. Transportation Research Part C,2002,10(2):85-98.
[131]Gang Tong, Chunling Fan, Fengying Cui, et al. Fuzzy neural network model applied in the traffic flow prediction[C]. Information Acquisition,2006 IEEE International Conference on,2006:1229-1233.
[132]董超俊,刘智勇.多层混沌神经网络及其在交通量预测中的应用[J].系统仿真学报,2007,19(10)：10-16.
[133]Boto-Giralda D, Anton-Rodriguez M, Diaz-pernas F J, et al. Neural network model based on fuzzy ARTMAP for forecasting of highway traffic data[J]. Lecture Notes Electrical Engineering,2008,15:95-105.
[134]Wen Guo, Yizheng Qiao, Haiyan Hou. BP neural network optimized with PSO algorithm and its application in forecasting[C]. Information Acquisition,2006 IEEE International Conference on,2006:617-621.
[135]赵建玉,贾磊,杨立才.基于粒子群优化的RBF神经网络交通流预测[J].公路交通科技,2006,23(7)：116-119.
[136]Hui Gao, Jianyu Zhao, Lei Jia. Short-term traffic flow forecasting model of elman neural network based on dissimilation particle swarm optimization[C]. IEEE International Conference on Networking, Sensing and Control,2008,4:1305-1309.
[137]Mascha V D, Dougherty M, Watson S. Combining Kohonen maps with ARIMA time series models to forecast traffic flow[J]. Transfortation Research Part C,1996,.4(5):307-318.
[138]谭满春,冯荦斌,徐建闽.基于ARIMA与人工神经网络组合模型的交通流预测[J].中国公路学
报,2007,20(4)：118-121.
[139]张晓利,贺国光.基于主成分分析和组合神经网络的短时交通流预测方法[J].系统工程理论与实践,2007,(8)：167-171.
[140]杜长海,黄席樾,杨祖元.基于神经网络和Markov链的交通流实时滚动预测[J].系统仿真学报,2008,20(9)：2464-2468.
[141]Darong H, Xing-rong B. A wavelet neural network optimal control model for traffic-flow prediction in intelligent transport systems[J]. Lecture Notes in Computer Science,2007, 20(6):59-65.
[142]朱胜雪,周君,包旭.基于小波分解—支持向量机的短时交通量预测[J].苏州科技学院学报(工程技术版),2007,20(3)：79-82.
[143]任其亮.基于小波与混沌集成的短时交通流预测[J].重庆交通大学学报自然科学版,2008,27(4)：630-633.
[144]Liang Zhao, Fei-Yue Wang. Short-term fuzzy traffic flow prediction using self-organizing TSK-type fuzzy neural network[C]. IEEE International Conference on Vehicular Electronics and Safety,2007:1-6.
[145]Kai Cao, Xiu-song Liu, Feng Cao, et al. A dynamic traffic network monitoring algorithm[C]. Intelligent Transportation Systems Conference,2006. ITSC'06. 2006:481-486.
[146]Cao Kai, Kai-Wang Zhang, Hamamatsu Y. Traffic information real-time monitoring based on a short-long term algorithm[C]. Systems, Man and Cybernetics,2006. ICSMC'06. IEEE International Conference on,2006,1:651-656.
[147]Liu Bin-sheng, Xing Zhan-wen, Yang Hai-tao. A combination predicted model of short term traffic flow[C]. Management Science and Engineering,2006. ICMSE'06.2006 International Conference on,2006:2075-2080.
[148]李婧瑜,李歧强,侯海燕.基于遗传算法的小波神经网络交通流预测[J].山东大学学报(工学版),2007,37(2)：109-112.
[149]谭满春,李英俊,徐建闽.基于小波消噪的ARIMA与SVM组合交通流预测[J].公路交通科技.2009,26(7)：127-132.
[150]马俊.交通流理论基础[M].北京：中国人民公安大学出版社,2004.
[151]刘焰,杨亚芬.交通工程学[M].北京：机械工业出版,2004.
[152]姜桂艳,丁同强.交通工程学[M].北京：国防工业出版社,2007.
[153]Tan G Z, Yuan W J, Ding H. Traffic flow prediction based on generalized neural network. Proceedings of the 7th IEEE International Conference on Intelligent Transportation Systems, Washington, USA,2004:406-409.
[154]田珠.大规模路网实时交通流预测模型及应用研究[D].大连：大连理工大学,2007.
[155]黄欣,杨新苗,常玉林,等.城市交通拥挤的成因探析[J].交通运输工程与信息学报,2007,5(2)：108-113.
[156]谭国真,瞿晓高.大连市实时交通信息发布系统[J].计算机系统应用,2003,15(07)：50-52.
[157]王炜,过秀成.交通工程学[M].南京：东南大学出版社,2000.
[158]徐循初.城市道路网系统规划思想及实例解析[J].城市交通,2006,4(1)：30-35.
[159]Cambridge Systematics, Inc. Traffic congestion and reliability-final report of the trends and advanced strategies for congestion mitigation [R].Massachusetts:FHWA,2005.
[160]Fan Wang, Chao Deng, Huimin Shi, et al. Support vector machine based on data mining technology in traffic flow forecasting[J]. Journal of Information and Computational Science,2009,6(3):1287-1294.
[161]Fan Wang, Guozhen Tan, Chao Deng, et al. Real-time traffic flow forecasting model and parameter selection based on ε-SVR[C]. In Proceedings of the 7th World Congress on Intelligent Control and Automation,2008:2870-2875.
[162]朱顺应,王红,向红艳.交通流参数及交通事件动态预测方法[M].东南大学出版社,2008.
[163]J. W. C. van Lint. Reliable real-time framework for short-term freeway travel time prediction[J]. Journal of Transportation Engineering,2006,132(12):921-932.
[164]张学工译.统计学习理论的本质[M].北京：清华大学出版社,2000.
[165]孙向东,刘拥军,黄保续,等.蛋白质结构预测-支持向量机的应用[M].北京：科学出版社2008.
[166]Keerthi S S, Lin C J. Asymptotic behaviors of support vector machines with Gaussian kernel[J].Neural Computation.2003,15(7):1667-1689
[167]朱国强,刘士荣,俞金寿.支持向量机及其在函数逼近中的应用[J].华东理工大学学报,2002,18(5)：555-559,568
[168]乔弘,周黎辉,潘卫华等.基于参数自适应SVR的火电厂热工参数软测量[J].华北电力大学学报,2008,35(2)：94-97.
[169]Buers J,.FruminM, Hom G, et al. FLID-DL:Congestion control for layered multicast[C]. Proc. Networked Group Commucication (NGC2000). Palo Alto, USA,2000:71-81.
[170]Wahba G. Support vector machine, reproducing kernel hilbert spaces and the randomized GACV[R]. In:Scholkopf B, Burges C, Smola A, ed. Advances in Kernel Methods-Support Vector Learning. Cambridge, MA:MITPress,1999.
[171]Joachims T. Estimating the generalization performance of a SVM efficiently[C]. Proceedings of the International Conference on Machine Learning, San Francisco:Morgan Kaufman,2000:431-438.
[172]Lee J H, Lin C J. Automatic model selection for support vector machines:Technical Report[R]. Taiwan:Department of Computer Science and Information Engineering, National Taiwan University,2000.
[173]Keerthi S S, Chong J, Martin S L. Two efficient methods for computing leave-one-out error in SVM algorithms:Techinical Report CD-00-10[R]. Singapore:Dept. of Mechanical Engineering National University of Singapore,2001.
[174]Cawley G C, Talbot N L C. Fast exact leave-one-out cross-validation of sparse least-squares support vector machines[J]. Neural Networks,2004,17(10):1467-1475.
[175]Ming-Wei Chang, Chih-Jen Lin. Leave-one-out bounds for support vector regression model selection[J]. Neural Computation,2005,17:1188-1222.
[176]Chapelle 0, Vapnik V, Bousquet O, et al. Choosing multiple parameters for support vector machines[J]. Machine Learning,2002,46(1):131-159.
[177]Chen P W, Wang J Y, Lee H M. Model selection of SVMs using GA approach[C]. In: Proceedings of 2004 IEEE International Joint Conference on Neural Networks, Budapest, Hungary,2004,3:2035-2040.
[178]Wang X, Qin C H, Gui W H. Parameter selection of support vector regression based on hybrid optimization algorithm and its application[J]. Journal of Control Theory and Application,2005,3(4):372-376.
[179]邵信光,杨慧中,陈刚.基于粒子群优化算法的支持向量机参数选择及应用[J].控制理论及应用,2006,23(5)：740-743.
[180]Ef ron B, Hastie T, Johnstone I, et al. Least angle regression [J]. Annals of Statistics, 2004,32 (2):407-499.
[181]Hastie T, Rosset S, Tibshirani R. The entire regularization path for the support vector machine[J]. Journal of Machine Learning Research,2004,5:1391-1415.
[182]袁小芳,王耀南.基于混沌优化算法的支持向量机参数选取方法[J].控制与决策,2006,21(1)：111-113.
[183]刘昌平,范明钰,王光卫.基于梯度算法的支持向量机参数优化方法[J],控制与决策,2008,23(11)：1291-1295.
[184]熊伟丽,徐保国.基于PSO的SVR参数优化选择方法研究[J].系统仿真学报,2006,18(9)：2442-2445.
[185]杜京义,侯媛彬.基于遗传算法的支持向量回归机参数选取[J].系统工程与电子技术,2006,28(9)：1430-1433.
[186]Vladimir Cherkassky, Yunqian Ma. Practical selection of SVM regression parameters and noise estimation for SVM regression[J]. Neural Networks,2004,17(1):113-126.
[187]李存军.基于集成神经网络的城市道路交通流量融合预测研究[D].成都：西南交通大学,2005.
[188]Freeway Performance Measurement System (PEMS), version7.0 [Online].
[189]任世锦,吴铁军.基于径向基小波核的多尺度小波支持向量机[J].电路与系统学报,2008,13(4)：70-75.
[190]Scholkopf B, Smola A, Muller K. Nonlinear component analysis as a kernel eigenvalue problem[J]. Neural Computations,1998,10(5):1299-1319.
[191]Mike S, WestonJ, Mullet K, et al. Fisher discriminant analysis with kernels [J]. Neural Networks for Signal Processing IX,1998:41-48.
[192]Weinert H. L. Reproducing kernel hibert space[M]. Hutchinson Ross:Stroudsburg,1999.
[193]Ha-Nam Nguyen, Syng-Yup Ohn, Jaehyun Parke, et al. Combined kernel function approach in SVM for diagnosis of cancer[J]. Lecture Notes in Computer Science,2005, 3610:1017-1026.
[194]Mingzhu L, PHILIP C, Jianbing H. Optimization of combined kernel function for SVM by particle swarm optimization[C]. Proceedings of the Eighth International Conference on Machine Learning and Cybernetics,2009:1160-1166.
[195]封筠,陈志军,李莉蓉.基于修正核函数的SVM分类器研究[J].系统仿真学报,2006,18(3)：570-572.
[196]Christian Wallraven, Barbara Caputo. Recognition with local features:the kernelrecipe[C]. Proceedings of the Ninth IEEE International Conference on Computer Vision,2003,1:257-264.
[197]吴涛,贺汉根,贺明科.基于插值的核函数构造[J].计算机学报,2003,26(8)：990-996.
[198]张淮清,俞集辉,郑亚利.径向基函数及其耦合方法在电磁场数值计算中的应用[J].计算物理,2009,26(2)：299-309.
[199]黄啸,浦小祥.SVM核函数的研究及其在语音激活检测中的应用[J].苏州大学学报,2008,28(3)：56-59.
[200]李元诚,方廷健,郑国祥.短期电力负荷预测的小波支持向量机方法研究[J].中国科学技术大学学报,2003,33(6)：726-732.
[201]Zhang L, Zhou W, Jiao L. Wavelet support vector machine[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics,2004,34(1):34-39.
[202]隋学深,齐中英.基于多尺度特征和支持向量机的股市趋势预测[J].哈尔滨工业大学学报,2008,10(4)：77-81.
[203]郭磊,陈进,朱义等.小波支持向量机在滚动轴承故障诊断中的应用[J].上海交通大学学报,2009,43(4)：678-682.
[204]飞思科技产品研发中心.小波分析理论与Matlab7实现[M].北京：电子工业出版社,2005.
[205]Yang F S. Engineer analysis and application of wavelet transform[M]. Beijing:Science Press,2000.
[206]Mercer J. Function of positive and negative type and their connection with the theory of integral equations[J]. Philos. Trans. Roy. Soc. London,1909, A209:415-446.
[207]Burges C J C. Geometry and invariance in kernel based methods[M]. Advance in Kernel Methods support Vector Learning. Cambridge:MIT Press,1999.
[208]李青.核优化方法[D].西安：西安电子科技大学,2007.
[209]Junshui Ma, James Theiler, Simon Perkins. Accurate on-line support vector regression[J]. Neural Computation,2003,15(11):2683-2703.
[210]Syed N A S, Liu H, Sung K. From incremental learning to model independent instance selection-a support vector machine approach [R]. Singapore:National University of
Singapore,1999.
[211]Mitra P, Murthy C A, Pal S K. Data Condensation in large databases by incremental learning with support vector machines[J]. Int. Conf. Pattern Recognition,2000:708-711.
[212]Cauwenberghs G, Poggio T. Incremental and decremental support vector machines[J]. Machines Learning,2001,44(13):409-415.
[213]Ralaivola L, Alche-Buc F. Incremental support vector machine learning:A local approach[J]. Lecture Notes in Computer Science, Springer 2001(2130):322-330.
[214]萧嵘,王继成,孙正兴.一种SVM增量学习算法[J].软件学报,2001,12(12)：1818-1824.
[215]曾文华,马健.一种新的SVM增量学习算法[J].厦门大学学报(自然科学版).2002,41(6)：687-691.
[216]李凯,黄厚宽.支持向量机增量学习算法研究[J].北方交通大学学报,2003,27(5)：34-37.
[217]史朝辉,王晓丹,杨建勋.一种SVM增量训练淘汰算法[J].计算机工程与应用,2005,23：187-189.
[218]曾嵘,刘建成,蒋新华.一种基于支持向量机的增量学习算法[J].铁道科学与工程学报,2005,2(1)：94-96.
[219]李忠伟,张健沛,杨静.基于支持向量机的增量学习算法研究[J].哈尔滨工程大学学报,2005,26(5)：643-646.
[220]孔波,刘小茂,张钧.基于中心距离比值的增量SVM[J].计算机应用,2006,26(6)：1434-1436.
[221]毛建洋,黄道.一种新的支持向量机增量算法[J].华东理工大学学报(自然科学版),2006,32(8)：989-991.
[222]李东晖.基于壳向量的线性支持向量机快速增量学习算法[J].浙江大学学报(工学版),2006,40(2)：202-206.
[223]孔锐,张冰.一种快速支持向量机增量学习算法[J].控制与决策,2005,20(10)：1129-1132.
[224]廖东平,魏玺章,黎湘.一种支持向量机增量学习淘汰算法[J].国防科技大学学报,2007,29(3)：65-70.
[225]田翔,邓飞其.精确在线支持向量回归在股指预测中的应用[J].计算机工程,2005,31(22)：18-20.
[226]刘大同,彭宇,彭喜元.基于残差预测修正的局部在线时间序列预测方法[J].电子学报.2008,36(12A)：81-85.
[227]Manoel Castro-Neto, Young-Seon Jeong, Myong-Kee Jeong, et al. Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions[J]. Expert Systems with Applications,2008,36(3):6164-6173.
[228]吴微,张凌.自适应参数的AOSVR算法及其在股票预测中应用[J].大连理工大学学报.2009,49(4)：605-610.
[229]Giovanni Montana, Francesco Parrella. Learning to trade with incremental support vector regression [J]. Lecture Notes in Computer Science,2008,5271:591-598.
[230]曹凯,李士明.一种基于在线训练的交通信息实时预测快速算法[J].数学的实践与认识,2008,38(11)：73-75.
[231]邓清清.交通诱导系统的流量预测和路径优化并行算法研究[D].大连：大连理工大学,2008.
[232]陈国良.并行算法实践[M].北京：高等教育出版社,2004.
[233]Collobert R, Bengio S, Bengio Y. A parallel mixture of SVMs for very large scale problems[J]. Neural Computation,2002,14(5):1105-1114.
[234]Dong J X, Krzyzak A. Fast SVM training algorithm with decomposition on very large datasets[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence,2005, 27:89-95.
[235]Zanghirati G, Zanni L. A parallel solver for large quadratic programs in training support vector machines[J]. Parallel Computing,2003,29:535-551.
[236]Zanni L, Seraf ini T, Zanghirati G. Parallel software for training large scale support vector machines on multiprocessor systems[C]. PPJMLR,2006:1467-1492.
[237]Zanni L. An improved gradient p rojection-based decomposition technique for support vector machines[J]. Computational Management Science,2006,3(2):131-145.
[238]林继鹏,刘君华,凌振宝.并行支持向量机算法及其应用[J].吉林大学学报(信息科学版),2004,22(5)：45-53.
[239]Graf H P, Cosatto E, Bottou L, et al. Parallel support vector machines:the cascade SVM [C]. Advances in Neural Information Processing Systems. Cambridge, MA:MITPress, 2005:521-528.
[240]Yang J. An improved cascade SVM training algorithm with crossed feedbacks[C]. IEEE Proceedings of IMSCCS'06. Hangzhou,2006,2:735-738.
[241]LUB L, WANG K A, WEN Y M. Comparison of parallel and cascade methods for training support vector machines on large-scale problems [J]. Machine Learning and Cybernetics, 2004,5:3056-3061.
[242]贾华丁,游志胜,王磊.基于MTC结构的支持向量机并行训练算法[J].四川大学学报(工程科学版),2007,39(6)：123-128.
[243]文益民.基于支持向量的分层并行筛选训练样本方法[J].计算机工程,2006,32(21)：177-179.
[244]雷咏梅,王雄,郭恒明.一种基于支持向量机的并行训练策略[J].上海大学学报(自然科学版).2007,13(25)：545-549
[245]Runarsson T P, Sigurdsson S. Asynchronous Parallel Evolutionary Model Selection for Support Vector Machines[J]. Neural Information Processing-Letters and Reviews.2004, 3(3):59-68.
[246]刘胜,江娜.基于SVM的故障预报中的并行优化仿真研究[J].系统仿真学报,2008,20(13)：3445-3448.
[247]Cao L J, Keerthi S S, Ong C J, et al. Parallel sequential minimal optimization for the training of support vector machines[J]. IEEE Trans. Neural Network,2006,17(4): 1039-1049.
[248]都志辉.MPI并行程序设计[M].北京：清华大学出版社,2006.
[249]周翔,荣建,任福田.Mobitex无线网在动态交通信息服务系统中的应用[J].计算机应用与软件.2009,26(1)：69-71.
[250]张伟,梅宏.一种面向特征的领域模型及其建模过程[J].软件学报,2003,14(8)：1345-1356.
[251]王凡,谭国真,王皓.面向交通领域构件的特征建模方法研究.计算机工程.2009,35(1)：280-282.
[252]袁剑君.试论交通信息服务的关键问题[J].湖南交通科技.2009,35(1)：170-172.
[253]刘学军,徐鹏.交通地理信息系统[M].北京：科学出版社,2006.
[254]黄罡,王千祥,曹东刚等.PKUAS:一种面向领域的构件运行支撑平台[J].电子学报,2002,30(12A)：1938-1942
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