城市隧道智能监控系统及交通数据智能分析
|
摘要
随着城市建设和发展需要,隧道作为缓解拥塞的交通要道,数量逐年增加,其隧道运营安全日益引起人们的重视。本论文将以城市公路隧道监控为对象,研究通用的监控系统架构、异构多数据源的集成、联动控制方案以及交通数据智能分析方法等关键技术,从而建立一个集隧道运营监测、交通控制、灾害预警报警等功能于一体的较为通用的城市公路隧道智能监控系统,其主要工作包括如下几方面：
首先,根据武汉市多条隧道监控系统的设计和实施经验,总结和分析了城市公路隧道监控系统的通用架构、子系统划分及基本功能、实现方案及技术手段。提出了一种多个子系统分散控制和集中管理的体系架构。然后,研究并建立了一种城市公路隧道的交通态势本体模型,采用语义本体技术建立城市公路隧道交通态势本体模型和知识化表示。进一步提出了一种本体集成的通用框架,它在语义网中用全局本体来提供一个统一的本地本体视图查询,该框架描述了一种设计空间,可用来解决语义网应用程序中本体集成的问题。接着,分析和整合各个监控子系统的数据和功能,提出了基于XML的各类数据的统一表示和存储方法,探究了基于数据挖掘的多源数据融合的联动控制机制,并建立了隧道预案管理平台。更主要的是,研究了隧道交通数据的特征,以最小二乘法的数据压缩方法为基础,提出了一种针对隧道交通数据的核主成分分析(KPCA)两步降维算法,以约简数据；研究了隧道交通数据流的分类和预测方法,以时间段划分训练样本,提出了一种基于RBF核函数的支持向量回归(KSVMR)堵车情况预测模型。
根据武汉市多条城市公路隧道监控项目的实际需要和测试结果,本文提出的通用城市公路隧道监控架构及关键技术,能较好地解决实际问题,保证了隧道的安全环境,舒适行车和有效管理。
With the development and construction of the modern city, the number and quality of the urban highway tunnels which have become the traffic arteries easing congestion are improved. It is necessary to pay much attention to these tunnels' monitoring system and operational safety. This thesis proposes a common tunnel monitoring architecture, integration of heterogeneous multisource, the linkage traffic control scheme and traffic data intelligent analysis methods in order to build an appropriate monitoring system integrating running monitoring, traffic control, disaster warning and maintenance and management and so on. The main works of this thesis are as follows.
Firstly, according to our design and implementation experience of multiple tunnel monitoring systems in Wuhan, urban highway tunnel monitoring system's general architecture, subsystems division and basic functions, implementation and technical methodologies are summarized and analyzed. A new mechanism of multiple subsystems decentralized control and centralized management is proposed.
Secondly, the tunnel traffic trend ontology model and knowledgeable representation reflecting basic concepts and semantic model of integrated transport network trend are researched and the established. A general framework for ontology integration is further proposed which used in the global semantic web ontology to provide a unified view of the local check. The framework describing a design space can be used to solve the semantic web ontology application integration issues in order to support integrated transport network management and improve the whole efficiency.
Thirdly, various monitoring subsystem data and functionality are analyzed and integrated. XML-based unified data representation and storage methods for all kinds of data are proposed. The multi-source data fusion and data mining linkage control mechanism is explored. At last, the tunnel planning management platform is established.
Last but not least, the characteristics of the tunnel traffic data are studied. On one hand, a new Kernel Principle Content Analysis (KPCA) for tunnel for traffic data using two-dimensionality reduction algorithm on the basis of the least square method is proposed and tested to reduce data. On the other hand, the tunnel traffic data stream classification and prediction methods for time-division training samples based on Kernel Support Vector Machine Regression (KSVMR) model for the traffic jam situation are proposed and verified.
On the basis of practical requirements and testing outcomes of Wuhan urban highway tunnel projects, the framework and techniques proposed in this thesis are able to solve the practical problems efficiently. The aim of this thesis is to build a common intelligent urban highway tunnel monitoring system in order to ensure the safe environment, comfort driving and efficient management.
首先,根据武汉市多条隧道监控系统的设计和实施经验,总结和分析了城市公路隧道监控系统的通用架构、子系统划分及基本功能、实现方案及技术手段。提出了一种多个子系统分散控制和集中管理的体系架构。然后,研究并建立了一种城市公路隧道的交通态势本体模型,采用语义本体技术建立城市公路隧道交通态势本体模型和知识化表示。进一步提出了一种本体集成的通用框架,它在语义网中用全局本体来提供一个统一的本地本体视图查询,该框架描述了一种设计空间,可用来解决语义网应用程序中本体集成的问题。接着,分析和整合各个监控子系统的数据和功能,提出了基于XML的各类数据的统一表示和存储方法,探究了基于数据挖掘的多源数据融合的联动控制机制,并建立了隧道预案管理平台。更主要的是,研究了隧道交通数据的特征,以最小二乘法的数据压缩方法为基础,提出了一种针对隧道交通数据的核主成分分析(KPCA)两步降维算法,以约简数据；研究了隧道交通数据流的分类和预测方法,以时间段划分训练样本,提出了一种基于RBF核函数的支持向量回归(KSVMR)堵车情况预测模型。
根据武汉市多条城市公路隧道监控项目的实际需要和测试结果,本文提出的通用城市公路隧道监控架构及关键技术,能较好地解决实际问题,保证了隧道的安全环境,舒适行车和有效管理。
With the development and construction of the modern city, the number and quality of the urban highway tunnels which have become the traffic arteries easing congestion are improved. It is necessary to pay much attention to these tunnels' monitoring system and operational safety. This thesis proposes a common tunnel monitoring architecture, integration of heterogeneous multisource, the linkage traffic control scheme and traffic data intelligent analysis methods in order to build an appropriate monitoring system integrating running monitoring, traffic control, disaster warning and maintenance and management and so on. The main works of this thesis are as follows.
Firstly, according to our design and implementation experience of multiple tunnel monitoring systems in Wuhan, urban highway tunnel monitoring system's general architecture, subsystems division and basic functions, implementation and technical methodologies are summarized and analyzed. A new mechanism of multiple subsystems decentralized control and centralized management is proposed.
Secondly, the tunnel traffic trend ontology model and knowledgeable representation reflecting basic concepts and semantic model of integrated transport network trend are researched and the established. A general framework for ontology integration is further proposed which used in the global semantic web ontology to provide a unified view of the local check. The framework describing a design space can be used to solve the semantic web ontology application integration issues in order to support integrated transport network management and improve the whole efficiency.
Thirdly, various monitoring subsystem data and functionality are analyzed and integrated. XML-based unified data representation and storage methods for all kinds of data are proposed. The multi-source data fusion and data mining linkage control mechanism is explored. At last, the tunnel planning management platform is established.
Last but not least, the characteristics of the tunnel traffic data are studied. On one hand, a new Kernel Principle Content Analysis (KPCA) for tunnel for traffic data using two-dimensionality reduction algorithm on the basis of the least square method is proposed and tested to reduce data. On the other hand, the tunnel traffic data stream classification and prediction methods for time-division training samples based on Kernel Support Vector Machine Regression (KSVMR) model for the traffic jam situation are proposed and verified.
On the basis of practical requirements and testing outcomes of Wuhan urban highway tunnel projects, the framework and techniques proposed in this thesis are able to solve the practical problems efficiently. The aim of this thesis is to build a common intelligent urban highway tunnel monitoring system in order to ensure the safe environment, comfort driving and efficient management.
引文
[1]王清波.城市公路隧道监控系统集成方法研究[D].武汉：武汉理工大学,2009.
[2]马成前.城市公路隧道智能监控方法和系统研究[D].武汉：武汉理工大学,2009.
[3]刘明华.隧道监控系统控制策略与接口技术研究[D].西安:长安大学,2009.
[4]徐波.基于PLC与CAN总线的隧道监控系统的设计与研究[D].武汉：武汉理工大学,2008.
[5]戴远.公路隧道监控系统集成平台的设计[D].武汉：武汉理工大学,2010.
[6]朱振兴.城市交通数据融合与交通态势处理方法的研究[D].济南：山东大学,2011.
[7]欧金成,欧世乐,林德杰等.组态软件的现状与发展[D].工业控制计算机,2002,15(4)：11-15.
[8]钟珞,王炜,袁景凌.组态软件在地下通道监控系统上的应用[J].计算机与数字工程,2008,(5)：145-146.
[9]张汝华,杨晓光,严海.智能交通信息特征分析与处理系统设计[J].交通运输系统工程与信息,3(4)：27-33,2003.
[10]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)：1530-1533.
[11]刘智勇.智能交通控制理论和应用[M].北京：科学出版社,2003：3-5.
[12]王进,史其信.短时交通流预测模型综述[J].ITS通讯,2005,7(1)：10-13.
[13]Antoniou C., Ben-Akiva M. and Koutsopoulos H. N. Dynamic Traffic Demand Prediction Using Conventional and Emerging Data Sources. IEEE Proceedings Intelligent Transport Systems,2006,153(1):97-104.
[14]徐军莉,喻国平.关联规则挖掘算法的研究和应用[J].微计算机信息,2009,6(12)：193-194.
[15]Xiaodan Zhang. Plain discussion of data mining technology research[C].3rd International Conference on Communication Software and Networks,2011.
[16]Xindong Wu.10 Years of Data Mining Research:Retrospect and Prospect [C]. International Conference on Data Mining, ICDM,2010.
[17]Han J. and Kamber M.. Data Preprocessing. In Data mining:Concepts and techniques, second edition[M]. San Francisco:Morgan Kaufmann,2006:47-104.
[18]Ciszak L.. Applications of clustering and association methods in data cleaning[J]. The International Multiconference on Computer Science and Information Technology,2008, 3(2):97-103.
[19]Fei Chiang, Ren6e J. Miller. Discovering data quality rules[C]. VLDB,2008:1166-1177.
[20]Jerry Ryan. A Practical Guide to Achieving Enterprise Data Quality[EB/OL]. http:// www.techguide.com.
[21]Miao Wana, Cong Wang, Lixiang Lia, Yixian Yang. Chaotic ant swarm approach for data clustering[J]. Applied Soft Computing,2012,12(8):2387-2393.
[22]Ben Arieh D., D.K. Gullipalli. Data Envelopment Analysis of clinics with sparse data:Fuzzy clustering approach[J]. Computers & Industrial Engineering,2012,63(1):13-21.
[23]段明秀.结合SOFM的改进CLARA聚类算法[J].计算机工程与应用,2010,(22)：210-212.
[24]Thakran Y., Toshniwal D. Unsupervised outlier detection in streaming data using weighted clustering[C]. Intelligent Systems Design and Applications (ISDA),2012:947-952.
[25]Ng R T, Han Jia-wei. Efficient and effective clustering methods for spatial data mining[C]. The 20th VLDB Con-ference Santiago, Chile,1994:144-155.
[26]Knox E M, Ng R T. Algorithms for mining distance-based outliers in large datasets[C]. The International Conference on Very Large Data Bases,1998.
[27]Brauckhoff D, Salamatian K, May M. Applying PCA for traffic anomaly detection:Problems and solutions[C]. INFOCOM 2009, IEEE,2009:2866-2870.
[28]]M. Dougherty, M. R. Cobbett. Short-term inter-urban traffic foreasts using neuralnetworks[J]. International journal of forecasting,1997,13(1):21-31.
[29]ZHAO Nale, YU Lei, GENG Yanbin, CHEN Xumei. Support vector machine-based approach to data layer mdti-source ITS data fusion[J]. Journal of Transportation Syslems Engineering and Information Technology,2007,7(2):32-37.
[30]M. Papageorgiou and G. Vigos. Relating time-occupancy measurements to space-occupancy and link vehicle count[J]. Transportation Research Part C:Emerging Technologies,2008, 16(1):1-17.
[31]Suicheng Gu, Yuhong Guo. Learning SVM Classifiers with Indefinite Kernels[C]. AAAI 2012.
[32]Thanes Wassantachat, Zhidong Li, Jing Chen, Yang Wang, Evan Tan. Traffic Density Estimation with On-line SVM Classifier[C]. Advanced Video and Signal Based Surveillance, 2009:13-18.
[33]Rajeev Kumaraswamy, Lekhesh V. Prabhu, K. Suchithra, P. S. Sreejith Pai. SVM Based Classification of Traffic Signs for Realtime Embedded Platform[C]. Advances in Computing and Communications,2011:339-348.
[34]Zhao Lihong, Song Ying, Zhu Yushi, Zhang Cheng, Zheng Yi. Face Recognition based on multi-class SVM[C]. Control and Decision Conference,2009:5871-5873.
[35]Xiangyang Wang, Jing-Wei Chen, Hong-Ying Yang. A new integrated SVM classifiers for relevance feedback content-based image retrieval using EM parameter estimation[J]. Applied Soft Computing,2011,11(2):2787-2804.
[36]段晓宇.基于SOA的分布式交通信息共享平台研究[D].北京：北京交通大学,2009.
[37]王亚琴.道路交通流数据挖掘研究[D].复旦大学,2007.
[38]覃明贵.城市道路交通数据挖掘研究与应用[D]。复旦大学,2010.
[39]方烈.分类方法在交通数据挖掘的应用研究[D].上海交通大学,2006.
[40]Dieter Fensel. Ontologies:A silver bullet for Knowledge Management and Electronic-Commerce[M]. Berlin:Spring-Verlag,2000.
[41]Marek Obitko. Ontologies:Description and Applications[R]. Series of Research Reports, Gerstner Laboratory,2001:1-35.
[42]Jorge E. Lopez de Vergara, Javier Aracil, Jesus Martinez, et al. Application of ontologies for the integration of network monitoring platforms[C].1st European Workshop on Mechanisms for Mastering Future Internet, Salzburg,2008:1-6.
[43]Neela J Deshpande, Rajendra Kumbhar. Construction and Applications of Ontology:Recent Trends[J]. Journal of Library & Information Technology,2011,31(2):84-89.
[44]张鸰,张淑梅.城市综合交通态势评估方法研究[J].软件工程师,2010,1(6)：52-55.
[45]曹妍.本体理论在城市智能交通系统语义集成中的应用研究[D]。大连：大连海事大学,2010.
[46]李阳,翟军,陈燕.用本体实现智能交通系统的语义集成[J].信息技术,2005,29(6)：10-13.
[47]黄坷萍,蒋昌俊.基于本体的城市交通的知识分析和推理[J].计算机科学,2007,34(3)：192-196.
[48]王煌,陈学广,洪流.基于SUMO的交通网格决策本体层次模型设计[J].计算机应用研究,2009,26(4)：1373-1376.
[49]马金素,陈毛狗,袁捷.基于本体的问题求解模型在CSP中应用的研究[J].计算机应用与软件,2006,23(10)：60-61,70.
[50]刘小红.长大隧道紧急事件预案管理平台设计与实现[D].西安：长安大学,2008.
[51]曹国辉.车辆特征提取方法综述[J].中国水运(理论版),2006,4(3)：32-37.
[52]沈艳超.车辆特征提取和分类方法的研究[J].长沙理工大学,2010,4(15)：115-119.
[53]耿彦峰,马钺.基于模糊模式识别的车辆分类研究[J].计算机工程,2002,28(1)：134-135.
[54]王琪.关于运动目标特征提取以及车辆颜色识别算法的研究[D].北京：电子科技大学,2011.
[55]M.Merler. Car color and logo recognition[C]. CSE190A Projects in Vision and Learning, 2006.
[56]李贵俊,刘正熙,志胜等.一种基于色差和彩色归一化的车身颜色识别算法[J].计算机应用,2004,24(9)：4749.
[57]张旭亚.基于特征提取和机器学习的医学图像分析[D].南京：南京邮电大学,2011.
[58]叶青.道路交通参数检测系统设计[J].中南公路工程,2007,30(30)：110-112.
[59]唐国侠.基于视频监控的车速检测算法研究[D].广东：广东工业大学,2008.
[60]廖庆斌,李舜酩,覃小攀.车辆振动信号的特征提取方法比较[J].吉林大学学报(工学报),2007,37(4)：910-914.
[61]柳涛.多通道图像MSER局部不变特征提取算法研究[D].长沙：国防科学技术大学,2010.
[62]蒋菠.基于视频图像的汽车车标检测技术研究与实现[D].成都：西南交通大学,2012
[63]贾永涛,张帆.车辆识别专家系统的设计[J].计算机测量与控制,2006,14(4)：472473.
[64]莫林,廖鹏,刘勋.一种基于背景减除与三帧差分的运动目标检测算法[J].微计算机信息,2009,25(4)：274-276.
[65]王孝艳,张艳珠,董慧颖.运动目标检测的三帧差法算法研究[J].沈阳理工大学学报,2011,30(6)：82-85.
[66]卢明,刘学军,晋蓓.基于视频的车辆几何参数提取与车型分类[J].计算机工程,2009,35(20)：173-175.
[67]D. Lowe. Object recognition from local scale-invariant features[C]. The seventh IEEE international conference,1999:1150-1157.
[68]A. Jain, R. Duin, J. Mao. Statistical pattern recognition:A review[C]. IEEE Transactions on Pat-tern Analysis and Machine Intelligence,2000:4-37.
[69]Bahadir Karasulu, Serdar Korukoglu. Moving object detection and tracking by using annealed background subtraction method in videos:Performance Optimization[J]. Expert Systems with Application,2012,39(1):33-43.
[70]Michael J.North, Charles M. Macal. Product design patterns for agent-based modeling[C]. Winter Simulation Conference,2011:3087-3098.
[71]刘玉花,王丽琴.Observer模式的探索及其在MFC中的应用[J].中国科技信息,2006,1(19)：112-113.
[72]于爱荣,王俊,刘晓明.Observer设计模式的改进型模式的实现与研究[C].中国自动化学会系统仿真专业委员会中国系统仿真学会仿真计算机与软件专业委员会2004学术年会,2004.
[73]Ping Zhou. Improved Disturbance Observer Design and Its Application [C]. Control and Decision Conference (CCDC),2012:3967-3972.
[74]Ian Bayley, Hong Zhu. Formal specification of the variants and behavioural features of design patterns [J]. The Journal of Systems and Software,2010,83(2):209-221.
[75]张宁,王越,王东.观察者模式及其在软件开发中的应用[J].大众科技,2008,1(11)：35-36.
[76]郑阿奇.Qt4开发实践[M].北京：电子工业出版社,2011.
[77]Chengqian Ma, Desheng Jiang, Xi Yue. Intelligent Model of Urban Road Tunnel Ventilation System Based on Multi-Level Neural Network [C]. PACCS 2009,2009:636-639.
[78]闫凤良.城市交通信息共享平台的设计及其信息交换技术的研究[D].北京：北京交通大学,2006.
[79]李慧双.交通观测数据信息共享平台的设计研究[D].西安：长安大学,2009.
[80]余尧.基于Agent的城市隧道监控智能决策系统的研究[D].武汉：武汉理工大学,2012.
[81]王清波,钟珞.城市公路隧道监控系统集成方法[J].武汉理工大学学报(信息与管理工程版),2009,31(5)：729-732.
[82]QingJi Zhang, Luo Zhong, Jingling Yuan, et al. Design and Realization of a City Tunnel Environment Monitoring System Based on Observer Pattern [C]. ICETCE2012,2012: 639-642.
[83]钟珞,李琳,袁景凌,夏红霞,王清波,张庆吉.基于XML的城市公路隧道监控数据存储方法及其系统[P].中国,发明专利,201110458995.2,2012.8.1.
[84]刘华婷,郭仁祥,姜浩.关联规则挖掘Apriori算法的研究与改进[J].计算机应用与软件,2009,26(1)：146-149.
[85]张华.隧道紧急事件联动控制系统设计与实现[D].西安：长安大学,2009.
[86]Yimei Song, Guoxiang Song. An Improved Reaction-Diffusion Filters for Denoising. Journal of Information & Computational Science,2010,7(6):1247-1252.
[87]Han, Jiawei, Micheline Kamber, and Jian Pei. Data mining:concepts and techniques[M]. San Francisco:Morgan Kaufmann,2006:383-466.
[88]毛国君,段立娟,王实,石云.数据挖掘原理与算法(第二版)[M].北京：清华大学出版社,2007.
[89]张敏,于剑.基于划分的模糊聚类算法[J].软件学报,2004,15(06)：858-868.
[90]段明秀.结合SOFM的改进CLARA聚类算法[J].计算机工程与应用,2010,6(22)：210-212.
[91]F. S. Tsai. Comparative study of dimensionality reduction techniques for data visualization[J]. Journal of Artificial Intelligence,2010,3(3):119-134.
[92]Burges, Christopher JC. Dimension reduction:A guided tour[J]. Machine Learning,2009, 2(4):275-365.
[93]M. Kallas, C. Francis, L. Kanaan, D. Merhed, P. Honeine and H. Amoud. Multi-class SVM Classification Combined with Kernel PCA Feature Extraction of ECG Signals[C]. In 19th International Conference on Telecommunications, ICT2012,2012:1-5.
[94]Scholkopf B, Smola A, Muller K R. Nonlinear component analysis as a kernel eigenvalue problem[J]. Neural computation,1998,10(5):1299-1319.
[95]Kruger, U., Zhang, J. and Xie, L. Developments and Applications of Nonlinear Principal Component Analysis-a Review, Principal Manifolds for Data Visualization and Dimension Reduction[J]. Lecture Notes in Computational Science and Engineering,2008,58(1):41-43.
[96]Jenssen R, Storas O. Kernel Entropy Component Analysis Pre-images for Pattern Denoising, Image Analysis[C]. The 16th Scandinavian Conference,2009.
[97]Jenssen R. Kernel entropy component analysis[J]. Pattern Analysis and Machine Intelligence, 2010,32(5):847-860.
[98]Heo G, Gader P, Frigui H. RKF-PCA:robust kernel fuzzy PCA[J]. Neural networks,2009, 22(5):642-650.
[99]WANG Heyong, YAO Zheng'an, LI Lei. The Application of Feature Extraction on Using Kernel Principal Component Analysis Based on Clustering(J). Computer Science,2005, 32(4):64-66.
[100]Xiaoqiang Zhao, Xinming Wang, Wu Yang. An Improved FVS-KPCA Method of Fault Detection on TE Process[C]. In Third International Conference on Digital Manufacturing and Automation, ICDMA 2012,2012:186-189.
[101]Rosipal R, Girolami M, Trejo L J, et al. Kernel PCA for feature extraction and de-noising in nonlinear regression[J]. Neural Computing & Applications,2001,10(3):112-121.
[102]Van der Maaten, Postma E. O, Van Den Herik H J. Dimensionality reduction:A comparative review[J]. Journal of Machine Learning Research,2009,10(1):1-41.
[103]David Boardman, Mark Reinhard, Alison Flynn. Principal Component Analysis of Gamma-Ray Spectra for Radition Portal Monitors[J]. Nuclear Science,2012,59(1): 154-160.
[104]苗润华.基于聚类和孤立点检测的数据预处理方法的研究[D].北京：北京交通大学,2012.
[105]Wan, Miao, Cong Wang, Lixiang Li, and Yixian Yang. Chaotic ant swarm approach for data clustering[J]. Applied Soft Computing,2012,12(8):2387-2393.
[106]Ben Arieh, David, and Deep Kumar Gullipalli. Data Envelopment Analysis of clinics with sparse data:Fuzzy clustering approach[J]. Computers & Industrial Engineering,2012, 63(1):13-21.
[107]赵海龙,张干,吕安涛,姚宁.基于EMD-WAVELET模型的实时交通流噪声数据清洗方法[J].交通科技,2013,1(2)：150-153.
[108]王国华,窦慧丽,郭敏.基于小波分析的交通数据自适应消噪算法研究[J].计算机应用与软件,2011,28(10)：4-6.
[109]Jiawei Han, Micheline Kamber.数据挖掘概念与技术[M].北京：机械工业出版社,2007.
[110]Zhao Qiankun, Sourav S. Bhowmick. Association rule mining:A survey [A]. Nanyang Technological University, Singapore,2003.
[111]QingJi Zhang, Luo Zhong, Jingling Yuan. Design and Realization of a City Tunnel Environment Monitoring System Based on Observer Pattern [C]. ICETCE2012,2012: 639-642.
[112]http://www.mathworks.cn/products/matlab/index.html
[113]钟珞,饶文碧,邹承明.人工神经网络及其融合应用技术[M].北京：科学出版社,2007.
[2]马成前.城市公路隧道智能监控方法和系统研究[D].武汉：武汉理工大学,2009.
[3]刘明华.隧道监控系统控制策略与接口技术研究[D].西安:长安大学,2009.
[4]徐波.基于PLC与CAN总线的隧道监控系统的设计与研究[D].武汉：武汉理工大学,2008.
[5]戴远.公路隧道监控系统集成平台的设计[D].武汉：武汉理工大学,2010.
[6]朱振兴.城市交通数据融合与交通态势处理方法的研究[D].济南：山东大学,2011.
[7]欧金成,欧世乐,林德杰等.组态软件的现状与发展[D].工业控制计算机,2002,15(4)：11-15.
[8]钟珞,王炜,袁景凌.组态软件在地下通道监控系统上的应用[J].计算机与数字工程,2008,(5)：145-146.
[9]张汝华,杨晓光,严海.智能交通信息特征分析与处理系统设计[J].交通运输系统工程与信息,3(4)：27-33,2003.
[10]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)：1530-1533.
[11]刘智勇.智能交通控制理论和应用[M].北京：科学出版社,2003：3-5.
[12]王进,史其信.短时交通流预测模型综述[J].ITS通讯,2005,7(1)：10-13.
[13]Antoniou C., Ben-Akiva M. and Koutsopoulos H. N. Dynamic Traffic Demand Prediction Using Conventional and Emerging Data Sources. IEEE Proceedings Intelligent Transport Systems,2006,153(1):97-104.
[14]徐军莉,喻国平.关联规则挖掘算法的研究和应用[J].微计算机信息,2009,6(12)：193-194.
[15]Xiaodan Zhang. Plain discussion of data mining technology research[C].3rd International Conference on Communication Software and Networks,2011.
[16]Xindong Wu.10 Years of Data Mining Research:Retrospect and Prospect [C]. International Conference on Data Mining, ICDM,2010.
[17]Han J. and Kamber M.. Data Preprocessing. In Data mining:Concepts and techniques, second edition[M]. San Francisco:Morgan Kaufmann,2006:47-104.
[18]Ciszak L.. Applications of clustering and association methods in data cleaning[J]. The International Multiconference on Computer Science and Information Technology,2008, 3(2):97-103.
[19]Fei Chiang, Ren6e J. Miller. Discovering data quality rules[C]. VLDB,2008:1166-1177.
[20]Jerry Ryan. A Practical Guide to Achieving Enterprise Data Quality[EB/OL]. http:// www.techguide.com.
[21]Miao Wana, Cong Wang, Lixiang Lia, Yixian Yang. Chaotic ant swarm approach for data clustering[J]. Applied Soft Computing,2012,12(8):2387-2393.
[22]Ben Arieh D., D.K. Gullipalli. Data Envelopment Analysis of clinics with sparse data:Fuzzy clustering approach[J]. Computers & Industrial Engineering,2012,63(1):13-21.
[23]段明秀.结合SOFM的改进CLARA聚类算法[J].计算机工程与应用,2010,(22)：210-212.
[24]Thakran Y., Toshniwal D. Unsupervised outlier detection in streaming data using weighted clustering[C]. Intelligent Systems Design and Applications (ISDA),2012:947-952.
[25]Ng R T, Han Jia-wei. Efficient and effective clustering methods for spatial data mining[C]. The 20th VLDB Con-ference Santiago, Chile,1994:144-155.
[26]Knox E M, Ng R T. Algorithms for mining distance-based outliers in large datasets[C]. The International Conference on Very Large Data Bases,1998.
[27]Brauckhoff D, Salamatian K, May M. Applying PCA for traffic anomaly detection:Problems and solutions[C]. INFOCOM 2009, IEEE,2009:2866-2870.
[28]]M. Dougherty, M. R. Cobbett. Short-term inter-urban traffic foreasts using neuralnetworks[J]. International journal of forecasting,1997,13(1):21-31.
[29]ZHAO Nale, YU Lei, GENG Yanbin, CHEN Xumei. Support vector machine-based approach to data layer mdti-source ITS data fusion[J]. Journal of Transportation Syslems Engineering and Information Technology,2007,7(2):32-37.
[30]M. Papageorgiou and G. Vigos. Relating time-occupancy measurements to space-occupancy and link vehicle count[J]. Transportation Research Part C:Emerging Technologies,2008, 16(1):1-17.
[31]Suicheng Gu, Yuhong Guo. Learning SVM Classifiers with Indefinite Kernels[C]. AAAI 2012.
[32]Thanes Wassantachat, Zhidong Li, Jing Chen, Yang Wang, Evan Tan. Traffic Density Estimation with On-line SVM Classifier[C]. Advanced Video and Signal Based Surveillance, 2009:13-18.
[33]Rajeev Kumaraswamy, Lekhesh V. Prabhu, K. Suchithra, P. S. Sreejith Pai. SVM Based Classification of Traffic Signs for Realtime Embedded Platform[C]. Advances in Computing and Communications,2011:339-348.
[34]Zhao Lihong, Song Ying, Zhu Yushi, Zhang Cheng, Zheng Yi. Face Recognition based on multi-class SVM[C]. Control and Decision Conference,2009:5871-5873.
[35]Xiangyang Wang, Jing-Wei Chen, Hong-Ying Yang. A new integrated SVM classifiers for relevance feedback content-based image retrieval using EM parameter estimation[J]. Applied Soft Computing,2011,11(2):2787-2804.
[36]段晓宇.基于SOA的分布式交通信息共享平台研究[D].北京：北京交通大学,2009.
[37]王亚琴.道路交通流数据挖掘研究[D].复旦大学,2007.
[38]覃明贵.城市道路交通数据挖掘研究与应用[D]。复旦大学,2010.
[39]方烈.分类方法在交通数据挖掘的应用研究[D].上海交通大学,2006.
[40]Dieter Fensel. Ontologies:A silver bullet for Knowledge Management and Electronic-Commerce[M]. Berlin:Spring-Verlag,2000.
[41]Marek Obitko. Ontologies:Description and Applications[R]. Series of Research Reports, Gerstner Laboratory,2001:1-35.
[42]Jorge E. Lopez de Vergara, Javier Aracil, Jesus Martinez, et al. Application of ontologies for the integration of network monitoring platforms[C].1st European Workshop on Mechanisms for Mastering Future Internet, Salzburg,2008:1-6.
[43]Neela J Deshpande, Rajendra Kumbhar. Construction and Applications of Ontology:Recent Trends[J]. Journal of Library & Information Technology,2011,31(2):84-89.
[44]张鸰,张淑梅.城市综合交通态势评估方法研究[J].软件工程师,2010,1(6)：52-55.
[45]曹妍.本体理论在城市智能交通系统语义集成中的应用研究[D]。大连：大连海事大学,2010.
[46]李阳,翟军,陈燕.用本体实现智能交通系统的语义集成[J].信息技术,2005,29(6)：10-13.
[47]黄坷萍,蒋昌俊.基于本体的城市交通的知识分析和推理[J].计算机科学,2007,34(3)：192-196.
[48]王煌,陈学广,洪流.基于SUMO的交通网格决策本体层次模型设计[J].计算机应用研究,2009,26(4)：1373-1376.
[49]马金素,陈毛狗,袁捷.基于本体的问题求解模型在CSP中应用的研究[J].计算机应用与软件,2006,23(10)：60-61,70.
[50]刘小红.长大隧道紧急事件预案管理平台设计与实现[D].西安：长安大学,2008.
[51]曹国辉.车辆特征提取方法综述[J].中国水运(理论版),2006,4(3)：32-37.
[52]沈艳超.车辆特征提取和分类方法的研究[J].长沙理工大学,2010,4(15)：115-119.
[53]耿彦峰,马钺.基于模糊模式识别的车辆分类研究[J].计算机工程,2002,28(1)：134-135.
[54]王琪.关于运动目标特征提取以及车辆颜色识别算法的研究[D].北京：电子科技大学,2011.
[55]M.Merler. Car color and logo recognition[C]. CSE190A Projects in Vision and Learning, 2006.
[56]李贵俊,刘正熙,志胜等.一种基于色差和彩色归一化的车身颜色识别算法[J].计算机应用,2004,24(9)：4749.
[57]张旭亚.基于特征提取和机器学习的医学图像分析[D].南京：南京邮电大学,2011.
[58]叶青.道路交通参数检测系统设计[J].中南公路工程,2007,30(30)：110-112.
[59]唐国侠.基于视频监控的车速检测算法研究[D].广东：广东工业大学,2008.
[60]廖庆斌,李舜酩,覃小攀.车辆振动信号的特征提取方法比较[J].吉林大学学报(工学报),2007,37(4)：910-914.
[61]柳涛.多通道图像MSER局部不变特征提取算法研究[D].长沙：国防科学技术大学,2010.
[62]蒋菠.基于视频图像的汽车车标检测技术研究与实现[D].成都：西南交通大学,2012
[63]贾永涛,张帆.车辆识别专家系统的设计[J].计算机测量与控制,2006,14(4)：472473.
[64]莫林,廖鹏,刘勋.一种基于背景减除与三帧差分的运动目标检测算法[J].微计算机信息,2009,25(4)：274-276.
[65]王孝艳,张艳珠,董慧颖.运动目标检测的三帧差法算法研究[J].沈阳理工大学学报,2011,30(6)：82-85.
[66]卢明,刘学军,晋蓓.基于视频的车辆几何参数提取与车型分类[J].计算机工程,2009,35(20)：173-175.
[67]D. Lowe. Object recognition from local scale-invariant features[C]. The seventh IEEE international conference,1999:1150-1157.
[68]A. Jain, R. Duin, J. Mao. Statistical pattern recognition:A review[C]. IEEE Transactions on Pat-tern Analysis and Machine Intelligence,2000:4-37.
[69]Bahadir Karasulu, Serdar Korukoglu. Moving object detection and tracking by using annealed background subtraction method in videos:Performance Optimization[J]. Expert Systems with Application,2012,39(1):33-43.
[70]Michael J.North, Charles M. Macal. Product design patterns for agent-based modeling[C]. Winter Simulation Conference,2011:3087-3098.
[71]刘玉花,王丽琴.Observer模式的探索及其在MFC中的应用[J].中国科技信息,2006,1(19)：112-113.
[72]于爱荣,王俊,刘晓明.Observer设计模式的改进型模式的实现与研究[C].中国自动化学会系统仿真专业委员会中国系统仿真学会仿真计算机与软件专业委员会2004学术年会,2004.
[73]Ping Zhou. Improved Disturbance Observer Design and Its Application [C]. Control and Decision Conference (CCDC),2012:3967-3972.
[74]Ian Bayley, Hong Zhu. Formal specification of the variants and behavioural features of design patterns [J]. The Journal of Systems and Software,2010,83(2):209-221.
[75]张宁,王越,王东.观察者模式及其在软件开发中的应用[J].大众科技,2008,1(11)：35-36.
[76]郑阿奇.Qt4开发实践[M].北京：电子工业出版社,2011.
[77]Chengqian Ma, Desheng Jiang, Xi Yue. Intelligent Model of Urban Road Tunnel Ventilation System Based on Multi-Level Neural Network [C]. PACCS 2009,2009:636-639.
[78]闫凤良.城市交通信息共享平台的设计及其信息交换技术的研究[D].北京：北京交通大学,2006.
[79]李慧双.交通观测数据信息共享平台的设计研究[D].西安：长安大学,2009.
[80]余尧.基于Agent的城市隧道监控智能决策系统的研究[D].武汉：武汉理工大学,2012.
[81]王清波,钟珞.城市公路隧道监控系统集成方法[J].武汉理工大学学报(信息与管理工程版),2009,31(5)：729-732.
[82]QingJi Zhang, Luo Zhong, Jingling Yuan, et al. Design and Realization of a City Tunnel Environment Monitoring System Based on Observer Pattern [C]. ICETCE2012,2012: 639-642.
[83]钟珞,李琳,袁景凌,夏红霞,王清波,张庆吉.基于XML的城市公路隧道监控数据存储方法及其系统[P].中国,发明专利,201110458995.2,2012.8.1.
[84]刘华婷,郭仁祥,姜浩.关联规则挖掘Apriori算法的研究与改进[J].计算机应用与软件,2009,26(1)：146-149.
[85]张华.隧道紧急事件联动控制系统设计与实现[D].西安：长安大学,2009.
[86]Yimei Song, Guoxiang Song. An Improved Reaction-Diffusion Filters for Denoising. Journal of Information & Computational Science,2010,7(6):1247-1252.
[87]Han, Jiawei, Micheline Kamber, and Jian Pei. Data mining:concepts and techniques[M]. San Francisco:Morgan Kaufmann,2006:383-466.
[88]毛国君,段立娟,王实,石云.数据挖掘原理与算法(第二版)[M].北京：清华大学出版社,2007.
[89]张敏,于剑.基于划分的模糊聚类算法[J].软件学报,2004,15(06)：858-868.
[90]段明秀.结合SOFM的改进CLARA聚类算法[J].计算机工程与应用,2010,6(22)：210-212.
[91]F. S. Tsai. Comparative study of dimensionality reduction techniques for data visualization[J]. Journal of Artificial Intelligence,2010,3(3):119-134.
[92]Burges, Christopher JC. Dimension reduction:A guided tour[J]. Machine Learning,2009, 2(4):275-365.
[93]M. Kallas, C. Francis, L. Kanaan, D. Merhed, P. Honeine and H. Amoud. Multi-class SVM Classification Combined with Kernel PCA Feature Extraction of ECG Signals[C]. In 19th International Conference on Telecommunications, ICT2012,2012:1-5.
[94]Scholkopf B, Smola A, Muller K R. Nonlinear component analysis as a kernel eigenvalue problem[J]. Neural computation,1998,10(5):1299-1319.
[95]Kruger, U., Zhang, J. and Xie, L. Developments and Applications of Nonlinear Principal Component Analysis-a Review, Principal Manifolds for Data Visualization and Dimension Reduction[J]. Lecture Notes in Computational Science and Engineering,2008,58(1):41-43.
[96]Jenssen R, Storas O. Kernel Entropy Component Analysis Pre-images for Pattern Denoising, Image Analysis[C]. The 16th Scandinavian Conference,2009.
[97]Jenssen R. Kernel entropy component analysis[J]. Pattern Analysis and Machine Intelligence, 2010,32(5):847-860.
[98]Heo G, Gader P, Frigui H. RKF-PCA:robust kernel fuzzy PCA[J]. Neural networks,2009, 22(5):642-650.
[99]WANG Heyong, YAO Zheng'an, LI Lei. The Application of Feature Extraction on Using Kernel Principal Component Analysis Based on Clustering(J). Computer Science,2005, 32(4):64-66.
[100]Xiaoqiang Zhao, Xinming Wang, Wu Yang. An Improved FVS-KPCA Method of Fault Detection on TE Process[C]. In Third International Conference on Digital Manufacturing and Automation, ICDMA 2012,2012:186-189.
[101]Rosipal R, Girolami M, Trejo L J, et al. Kernel PCA for feature extraction and de-noising in nonlinear regression[J]. Neural Computing & Applications,2001,10(3):112-121.
[102]Van der Maaten, Postma E. O, Van Den Herik H J. Dimensionality reduction:A comparative review[J]. Journal of Machine Learning Research,2009,10(1):1-41.
[103]David Boardman, Mark Reinhard, Alison Flynn. Principal Component Analysis of Gamma-Ray Spectra for Radition Portal Monitors[J]. Nuclear Science,2012,59(1): 154-160.
[104]苗润华.基于聚类和孤立点检测的数据预处理方法的研究[D].北京：北京交通大学,2012.
[105]Wan, Miao, Cong Wang, Lixiang Li, and Yixian Yang. Chaotic ant swarm approach for data clustering[J]. Applied Soft Computing,2012,12(8):2387-2393.
[106]Ben Arieh, David, and Deep Kumar Gullipalli. Data Envelopment Analysis of clinics with sparse data:Fuzzy clustering approach[J]. Computers & Industrial Engineering,2012, 63(1):13-21.
[107]赵海龙,张干,吕安涛,姚宁.基于EMD-WAVELET模型的实时交通流噪声数据清洗方法[J].交通科技,2013,1(2)：150-153.
[108]王国华,窦慧丽,郭敏.基于小波分析的交通数据自适应消噪算法研究[J].计算机应用与软件,2011,28(10)：4-6.
[109]Jiawei Han, Micheline Kamber.数据挖掘概念与技术[M].北京：机械工业出版社,2007.
[110]Zhao Qiankun, Sourav S. Bhowmick. Association rule mining:A survey [A]. Nanyang Technological University, Singapore,2003.
[111]QingJi Zhang, Luo Zhong, Jingling Yuan. Design and Realization of a City Tunnel Environment Monitoring System Based on Observer Pattern [C]. ICETCE2012,2012: 639-642.
[112]http://www.mathworks.cn/products/matlab/index.html
[113]钟珞,饶文碧,邹承明.人工神经网络及其融合应用技术[M].北京：科学出版社,2007.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |