道路交通流数据挖掘研究
|
摘要
研究交通流的各种形态及其运行规律,建立快速、稳定、高效的交通流模型是智能交通系统的重要研究内容。随着智能交通系统的发展,智能交通系统中积累了海量交通流数据,于是研究者开始研究利用先进的数据挖掘技术分析智能交通系统中的交通流信息,发现交通流信息中隐含的交通模式及规则。
本文针对交通流信息的特点以及智能交通系统的新的数据挖掘应用需求,对交通流数据预处理、交通流量预测、交通状态识别、交通流空间聚类以及实时交通流的查询等若干问题进行了研究,设计了适合的数据挖掘模型和算法。这些问题的研究对于智能交通系统的交通信号管理与控制、交通流诱导、动态交通分配等方面有着重要的意义。本文的主要研究内容和成果包括以下几个方面:
(1)智能交通系统是一个非常庞大的系统,其复杂性和稳定性使交通流数据的采集质量难以保证,对交通流数据进行异常检测及预处理对于后续的数据分析、挖掘结果的质量和预测的准确性具有重要意义。本文根据交通领域的流量—时间占有率的倒“V”字型曲线模型,提出了一种基于曲线拟合的交通流异常检测方法,利用三次多项式的最小二乘法拟合流量/时间占有率曲线,并且利用分箱的思想对拟合好曲线上下部分分别采用基于统计的方法划分上下界,有效的识别异常交通流数据。
(2)道路网络上运行的交通流具有不同的空间分布模式,如城市主干道的交通流具有“线”性模式、繁华路段的交通流具有“面”状模式等,根据交通流运行的空间分布特性,对城市道路交通网络进行实时、动态的交通区域划分是当前智能交通系统的研究热点之一。利用聚类分析方法对分布在道路网络空间中的环形感应线圈检测器检测的交通流数据进行空间聚类(Spatial Clustering)分析,使具有相似性质且具有空间关联性的交通流数据对象聚成一类,可以发现道路交通流的空间分布模式。本文基于凝聚的层次聚类算法思想,设计了一个高效的交通流空间聚类算法ESCA-TF(Efficient Spatial Clustering Algorithm ofTraffic Flow),自底向上的生成道路交通流的空间聚集类。ESCA-TF无需执行复杂的空间连接和空间合并操作,实验证明具有良好的时间效率。
(3)对道路交通流状态进行分析研究,及时、准确地识别和预测道路交通流的状态是智能交通系统实现动态交通管理的重要前提。交通流状态的识别和预测包括交通流量短时预测和交通状态的实时识别。对于路口短时交通流量预测,本文提出了基于二次聚类的交通流量序列分割和BP神经网络的组合模型的路口短时交通流量预测方法,实验证明基于二次聚类和BP神经网络组成的组合模型提高了神经网络模型的预测精度;对于道路交通状态的实时识别,本文提出了基于聚类分析的交通状态动态识别模型,基于该模型我们不需任何先验知识就可以识别道路交通状态,且具有较高的拥挤判别率和较快的判别反应时间。
(4)随着微电子技术、无线通信、移动定位技术的发展,在智能交通系统中,许多具有普适计算功能的移动装置(如PDAs、cellphone及各种GPS装置)可以跟踪人或车的实际位置,获取和传输与用户位置相关的各种有用信息,因此对于道路网络上的移动交通流提供基于位置(Location-Based Services,LBS)的服务也是当前交通信息化和智能交通系统的一个研究方向。Skyline查询提供了一种重要的基于位置服务的功能,本文设计了道路网络上移动对象的skyline连续查询算法。算法分为两个部分:独立查询点的Skyline查询算法RNASQ(Absolute Skyline Query)和Skyline连续查询算法RNCSQ(Continuous SkylineQuery)。RNASQ算法无需计算所有对象到查询点的网络距离,具有较好的时间效率。在RNASQ算法的基础上,本文提出了道路网络的上Skyline连续查询算法RNCSQ。在RNCSQ算法中,Skyline连续查询转化为对查询路径的顶点和查询对象与查询点距离的交叉点的有限个独立查询,可以快速地判断连续分段的分割点,有效地计算Skyline连续查询的连续分段。
(5)建立统一、开放、可扩展的智能交通系统数据挖掘平台是交通流数据挖掘研究的重要内容。本文提出了一个四层的ITS数据挖掘平台体系结构,主要划分为:数据层、数据挖掘算法工具层、分析逻辑层和应用系统层。这种层次的系统应用平台模式便于数据挖掘算法、分析功能的设立,方便数据挖掘系统的开发与配置,可以使用户轻松地根据实际应用的需要使用数据挖掘技术,基于此结构的数据挖掘系统具有良好的可扩展性及与实体的独立性,便于二次开发。在可扩展的智能交通系统数据挖掘应用平台体系架构的基础上,本文设计实现了一个基于SOA技术的智能数据挖掘平台UTDD(Urban TrafficData-Mining Development),实现了本文提出的交通流数据挖掘方法。
The research and application of Intelligent Transportation System has developed rapidly due to the demand on safe, convenient, comfortable and information-based modern transportation. It is important part of the research of Intelligent Transportation System to study different forms and operation rules on traffic flow and establish rapid, stable and effective traffic flow model. With the development of Intelligent Transportation System, mass traffic flow data have been accumulated in Intelligent Transportation System. More and more researchers have started to analyze the information of traffic flow by use of advanced data-mining technique, and discover hidden transportation mode and regulation amongst the information of traffic flow.
This paper has made the research on several questions such as traffic flow forecasting, traffic state identification, traffic spatial clustering and real-time inquiry on traffic flow etc. in light of the characteristics of the information of traffic flow and the application demand on new data-mining of Intelligent Transportation System. The research on these questions is of great significance to traffic signal management & control, traffic flow induction, dynamic traffic allocation of Intelligent Transportation System. In general, the main contents and achievements of this paper consist of the following aspects:
(1) Based on the characteristics of the transportation and the classic flow-occupancy inverse "V" model, implement polynomial fitting using least-squares algorithm and statistics method on flow curves to detect outliers which are proved to be not accord with practice through the actual implement, then use the moving average model to recorrect the outliers and absent.
(2) The traffic flows operated in road network have different distribution models in space, for example, the traffic flow in urban main roads has "line" model and that in busy downtown area has "plane" model etc. it is one of current research issues on Intelligent Transportation System to divide the urban road traffic network into dynamic real-time traffic areas according to the distribution features of operated traffic flow in space. This paper introduces the Spatial Clustering analysis method on traffic flow data from the loop induction coils arranged in road network space to collect the traffic flow data with similar characteristics and spatial relevancy into one category and discover the distribution models of road traffic flow.
(3) It is an important content of traffic flow data-mining to forecast up-to-date and accurate short-term traffic flow. The crossroad is intersected by several roads, which is critical component of road network and plays important role in the whole urban road transportation network. The research of short-time traffic flow forecasting in the crossroad may assist to optimize the real-time control and traffic flow induction on road transportation. The neural network model is an important classification forecasting model and different kinds of neural network models have been used for forecasting the short-term traffic flow in road transportation. This paper points out the forecasting method of short-term traffic flow in the crossroads based on relevance analysis and sequence partition by means of BP neural network to increase the accuracy of traffic flow forecasting.
(4) For mobile traffic objects in road network, we design an algorithm for the skyline continuous query. The algorithm is divided into two parts: RNASQ (Absolute Skyline Query) and RNCSQ (Continuous Skyline Query). For RNASQ, it is unnecessary to calculate the network distance of all objects from query point, which has good time efficiency. On the basis of RNASQ, we point out RNCSQ (Continuous Skyline Query) in road network. For RNCSQ, continuous Skyline query is transformed into limited independent inquiry among inquiry routine apex and the point of intersection between inquiry object and inquiry point to help judge the division points of continuous segments rapidly and calculate consecutive segments of continuous Skyline query.
(5) It is an important content of traffic flow data-mining research to establish unifying, open and extendable data-mining platform of Intelligent Transportation System. This paper introduces a four-layer system structure of ITS data-mining platform, which consists of data layer, data-mining algorithmic tool layer, logical analysis layer and application system layer. This system application platform model is served to establish the data-mining algorithm & analysis function and facilitate the development & configuration of data-mining system so that the clients may utilize the data-mining technique easily based on the needs of practical application. The data-mining system based on this structure has good extensibility and entity independence to facilitate the secondary development. On the basis of extensible system structure of Intelligent Transportation System data-mining application platform, we design an intelligent data-mining platform UTDD (Urban Traffic Data-Mining Development) based on SOA technique to realize the traffic flow data-mining method in this paper.
本文针对交通流信息的特点以及智能交通系统的新的数据挖掘应用需求,对交通流数据预处理、交通流量预测、交通状态识别、交通流空间聚类以及实时交通流的查询等若干问题进行了研究,设计了适合的数据挖掘模型和算法。这些问题的研究对于智能交通系统的交通信号管理与控制、交通流诱导、动态交通分配等方面有着重要的意义。本文的主要研究内容和成果包括以下几个方面:
(1)智能交通系统是一个非常庞大的系统,其复杂性和稳定性使交通流数据的采集质量难以保证,对交通流数据进行异常检测及预处理对于后续的数据分析、挖掘结果的质量和预测的准确性具有重要意义。本文根据交通领域的流量—时间占有率的倒“V”字型曲线模型,提出了一种基于曲线拟合的交通流异常检测方法,利用三次多项式的最小二乘法拟合流量/时间占有率曲线,并且利用分箱的思想对拟合好曲线上下部分分别采用基于统计的方法划分上下界,有效的识别异常交通流数据。
(2)道路网络上运行的交通流具有不同的空间分布模式,如城市主干道的交通流具有“线”性模式、繁华路段的交通流具有“面”状模式等,根据交通流运行的空间分布特性,对城市道路交通网络进行实时、动态的交通区域划分是当前智能交通系统的研究热点之一。利用聚类分析方法对分布在道路网络空间中的环形感应线圈检测器检测的交通流数据进行空间聚类(Spatial Clustering)分析,使具有相似性质且具有空间关联性的交通流数据对象聚成一类,可以发现道路交通流的空间分布模式。本文基于凝聚的层次聚类算法思想,设计了一个高效的交通流空间聚类算法ESCA-TF(Efficient Spatial Clustering Algorithm ofTraffic Flow),自底向上的生成道路交通流的空间聚集类。ESCA-TF无需执行复杂的空间连接和空间合并操作,实验证明具有良好的时间效率。
(3)对道路交通流状态进行分析研究,及时、准确地识别和预测道路交通流的状态是智能交通系统实现动态交通管理的重要前提。交通流状态的识别和预测包括交通流量短时预测和交通状态的实时识别。对于路口短时交通流量预测,本文提出了基于二次聚类的交通流量序列分割和BP神经网络的组合模型的路口短时交通流量预测方法,实验证明基于二次聚类和BP神经网络组成的组合模型提高了神经网络模型的预测精度;对于道路交通状态的实时识别,本文提出了基于聚类分析的交通状态动态识别模型,基于该模型我们不需任何先验知识就可以识别道路交通状态,且具有较高的拥挤判别率和较快的判别反应时间。
(4)随着微电子技术、无线通信、移动定位技术的发展,在智能交通系统中,许多具有普适计算功能的移动装置(如PDAs、cellphone及各种GPS装置)可以跟踪人或车的实际位置,获取和传输与用户位置相关的各种有用信息,因此对于道路网络上的移动交通流提供基于位置(Location-Based Services,LBS)的服务也是当前交通信息化和智能交通系统的一个研究方向。Skyline查询提供了一种重要的基于位置服务的功能,本文设计了道路网络上移动对象的skyline连续查询算法。算法分为两个部分:独立查询点的Skyline查询算法RNASQ(Absolute Skyline Query)和Skyline连续查询算法RNCSQ(Continuous SkylineQuery)。RNASQ算法无需计算所有对象到查询点的网络距离,具有较好的时间效率。在RNASQ算法的基础上,本文提出了道路网络的上Skyline连续查询算法RNCSQ。在RNCSQ算法中,Skyline连续查询转化为对查询路径的顶点和查询对象与查询点距离的交叉点的有限个独立查询,可以快速地判断连续分段的分割点,有效地计算Skyline连续查询的连续分段。
(5)建立统一、开放、可扩展的智能交通系统数据挖掘平台是交通流数据挖掘研究的重要内容。本文提出了一个四层的ITS数据挖掘平台体系结构,主要划分为:数据层、数据挖掘算法工具层、分析逻辑层和应用系统层。这种层次的系统应用平台模式便于数据挖掘算法、分析功能的设立,方便数据挖掘系统的开发与配置,可以使用户轻松地根据实际应用的需要使用数据挖掘技术,基于此结构的数据挖掘系统具有良好的可扩展性及与实体的独立性,便于二次开发。在可扩展的智能交通系统数据挖掘应用平台体系架构的基础上,本文设计实现了一个基于SOA技术的智能数据挖掘平台UTDD(Urban TrafficData-Mining Development),实现了本文提出的交通流数据挖掘方法。
The research and application of Intelligent Transportation System has developed rapidly due to the demand on safe, convenient, comfortable and information-based modern transportation. It is important part of the research of Intelligent Transportation System to study different forms and operation rules on traffic flow and establish rapid, stable and effective traffic flow model. With the development of Intelligent Transportation System, mass traffic flow data have been accumulated in Intelligent Transportation System. More and more researchers have started to analyze the information of traffic flow by use of advanced data-mining technique, and discover hidden transportation mode and regulation amongst the information of traffic flow.
This paper has made the research on several questions such as traffic flow forecasting, traffic state identification, traffic spatial clustering and real-time inquiry on traffic flow etc. in light of the characteristics of the information of traffic flow and the application demand on new data-mining of Intelligent Transportation System. The research on these questions is of great significance to traffic signal management & control, traffic flow induction, dynamic traffic allocation of Intelligent Transportation System. In general, the main contents and achievements of this paper consist of the following aspects:
(1) Based on the characteristics of the transportation and the classic flow-occupancy inverse "V" model, implement polynomial fitting using least-squares algorithm and statistics method on flow curves to detect outliers which are proved to be not accord with practice through the actual implement, then use the moving average model to recorrect the outliers and absent.
(2) The traffic flows operated in road network have different distribution models in space, for example, the traffic flow in urban main roads has "line" model and that in busy downtown area has "plane" model etc. it is one of current research issues on Intelligent Transportation System to divide the urban road traffic network into dynamic real-time traffic areas according to the distribution features of operated traffic flow in space. This paper introduces the Spatial Clustering analysis method on traffic flow data from the loop induction coils arranged in road network space to collect the traffic flow data with similar characteristics and spatial relevancy into one category and discover the distribution models of road traffic flow.
(3) It is an important content of traffic flow data-mining to forecast up-to-date and accurate short-term traffic flow. The crossroad is intersected by several roads, which is critical component of road network and plays important role in the whole urban road transportation network. The research of short-time traffic flow forecasting in the crossroad may assist to optimize the real-time control and traffic flow induction on road transportation. The neural network model is an important classification forecasting model and different kinds of neural network models have been used for forecasting the short-term traffic flow in road transportation. This paper points out the forecasting method of short-term traffic flow in the crossroads based on relevance analysis and sequence partition by means of BP neural network to increase the accuracy of traffic flow forecasting.
(4) For mobile traffic objects in road network, we design an algorithm for the skyline continuous query. The algorithm is divided into two parts: RNASQ (Absolute Skyline Query) and RNCSQ (Continuous Skyline Query). For RNASQ, it is unnecessary to calculate the network distance of all objects from query point, which has good time efficiency. On the basis of RNASQ, we point out RNCSQ (Continuous Skyline Query) in road network. For RNCSQ, continuous Skyline query is transformed into limited independent inquiry among inquiry routine apex and the point of intersection between inquiry object and inquiry point to help judge the division points of continuous segments rapidly and calculate consecutive segments of continuous Skyline query.
(5) It is an important content of traffic flow data-mining research to establish unifying, open and extendable data-mining platform of Intelligent Transportation System. This paper introduces a four-layer system structure of ITS data-mining platform, which consists of data layer, data-mining algorithmic tool layer, logical analysis layer and application system layer. This system application platform model is served to establish the data-mining algorithm & analysis function and facilitate the development & configuration of data-mining system so that the clients may utilize the data-mining technique easily based on the needs of practical application. The data-mining system based on this structure has good extensibility and entity independence to facilitate the secondary development. On the basis of extensible system structure of Intelligent Transportation System data-mining application platform, we design an intelligent data-mining platform UTDD (Urban Traffic Data-Mining Development) based on SOA technique to realize the traffic flow data-mining method in this paper.
引文
[AA06] Anand Meka, Ambuj K. Singh. Distributed Spatial Clustering in Sensor Networks. Y. Ioannidis et al. (Eds.): EDBT 2006, LNCS 3896, pp. 980-100, 2006.
[ABK+99] M. Ankerst, M. Breunig, H.P Kriegel, J. Sander. OPTICS: Ordering Points to Identify Clustering Structure. In Proc. Of the ACM SIGMOD Conference, pp.49-60, Philadelphia, PA, 1999.
[AFS93] R. Agrawal, C. Faloutsos, A. Swami. Efficient similarity search in sequence databases. In Procs. Of the Fourth International Conference on Foundations of Data Organization and Algorithms, 1993.
[AGG+98] R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan. Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. In Proc. Of the ACM SIGMOD Conference, pp.94-105, 1998.
[AIS93] R. Agrawal, T. Imielinski, A. Swami. Mining Association Rules Between Sets of Items in Large Databases. In Proc. Of the ACM-SIGMOD Int. of Conf. on Management of Data. Washington D.C., pp.207-216,1993.
[APR02] AbdIlll1ai B, Porwal H, Recker. Short-term Trafic Flow Prediction Using Neural-Genetic Algorithms[J]. ITS Journal, 2002, 7(1): 3 —41.
[AS94] R. Agrawal, R. Srikant. Fast Algorithms for Mining Association Rules. In Proc. Of the 20th VLDB Conference. Santiago Chile, pp.487-499,1994.
[ASK+03] J.Alon, S. Sclaro, G Kollios, V. Pavlovic. Discovering Clusters in Motion Time-series Data. In IEEE Computer Vision and Pattern Recognition Conference (CVPR), 2003.
[AWF36] Adams.W.F. J. Inst. Civil Engr., 1936; 4:121-130 [AY01] Aggarwal C, Yu P Outlier Detection for High Dimensional Data In Proc of ACM SIGMOD 2001.
[BC94] J.D Berndt and J. Clifford. Using Dynamic Time Warping to Find Patterns in Time Series. In Working Notes of the Knowledge Discovery in Databases Workshop, pp.359-370,1994.
[BGR+99] K. Beyer, J. Goldstein, R. Ramakrishnan, U. Shaft. When is Nearest Neighbors Meaningful? In ICDT Conference Preceedings, Jerusalem Israel, pp.217-235,1999.
[BJH89] Banks, J. H. Two-Capacity Phenomenon at Freeway Bottlenecks: A Basis for Ramp Metering? Transportation Research Record 1320, TRB, NRC, Washington, DC,pp. 83-90.
[BJR94] G.E Box, G.M Jenkins, and G.C Reinsel. Time Series Analysis Forecasting and Control (3th edition). Prentice Hall, Englewood Cliffs, 1994.
[BJZ03]A.J.Bagnall,G.Janakec,M.Zhang.Clustering Time Series from Mixtnre Polynomial Models with Discretised Data.Technical Report CMP-C03-17,School of Computing Sciences,University of East Anglia,2003
[BKN00]Breunig M,Kriegel H P,Ng R,et al LOF:Identifying Density.basedLocal Outliers.In ACM SIGMOD Conference Proceedings,2000.
[BKS+90]N.Beckman,H.P.Kriegel,R.Schneider,B.Seeger.The R~*-tree:An Efficient and Robust Access Method for Points and Rectangles.In Proc.ACM SIGMOD Conf.,pp.322-331,Atlantic City,NJ,May 1990.
[BML92]Biham.O.,Middleton,A.A.,Levine,n Phys.Rev.A.,1992,46:6124-6127
[BMU97]S.Brin,R.Motwani,J.D Ullman,S.Tsur.Dynamic Itemset Counting and Implication Rules for Market Basket Data,Proc.ACM SIGMOD Int'l Conf.Management of Data,ACM Press,New York,1997,pp.255-264.
[BSD99]Bhargab Maitra,P.K.Sikdar,S.L.Dhingra,Modeling Congrestion on Urban Roads and Assessing Level of Service.Transportation Engineering,1999
[BT96]R.J.Brachman and T.Anand,The Process of Knowledge Discovery in Databases,Charpter 2 in[Fayyad96],1996
[CGS01]Chen Hmbo,Grant-Muller,Susan.Use of Sequential Learning for Short-term Traffic Flow Forecasting[J].Transportation Reseamh(Part C),2001,9(5):319-336.
[CHG95]Chung.K.H.,Hui.P.M.,Gu.G..Q.Phys.Rev.E,1995;51:772-774
[CMY94]C.Fanoutsos,M.Ranganathan,and Y.Manolopounos.Fast subsequence matching in time-series databases.In Proc.ACM SIGMOD Int.Conf.on Management of Data,pages 419-429,1994
[CPZ97]P.Ciaccia,M.Patella,P.Zezula.M-tree:An Efficient Access Method for Similarity Search in Metric Spaces.In Proc.Of the 23~(th)International Conference on Very large Data Bases(VLDB),Athens,August 1997.
[Davis91]Gary A.Davis,Nonparametric Regression and Short-Term Freeway Traffic Forecast.Transportation Engineering,178-187,Vol.117,No.2,1991.
[DLQ05]董超俊、刘智勇、邱祖廉.城市交通控制智能优化配时及仿真.系统仿真学报,Vol.17 No.2,2005.2,pp472-475.
[DFS02]D.Kossmann,F.Ramsak,and S.Rost.Shooting stars in the sky:An online algorithm for skyline queries.In VLDB,275-286,2002.
[DHP01]D.Hand,H.mallina,ald P.Smyth.Principles of Data Mining.The MIT Press,2001
[DJ94]D.Berndt & J.Clifford.Using dynamic time warping to find patterns in time series[C].AAAI-94 Workshop on Knowledge Discovery in databases(KDD-94),Seattle,Washington,1994 D.J.Berndt and J.Clifford.Finding patterns in time series:A dynamic
[DJ96]programming approach.In advances in Knowledge Discovery and Data Mining,pages 229-248,1996.
[DLMJ83]D.L鸠洛夫,M.J.休伯,交通流理论,蒋璜等译.北京:人民变通出版社,1983
[DLM+98]G.Das,K.Lin,H.Mannila,G.Renganathan,P.Smyth.Rule Discovery from Time Series.KDD 1998,pp:16-22.
[DYGB05]D.Papadias,Y.Tao,G.Fu,and B.Seeger.Progressive skyline computation in database systems.TODS,30(1):41-82,2005.
[DZS07]Ke Deng,Xiaofang Zhou and Heng Tao Shen.Multi-source Skyline Query Processing in Road Networks.In Proceedings of 23rd IEEE International Conference on Data Engineering(ICDE),2007.
[EKS+96]M.Ester,H.P Kriegel,J.Sander,X.Xu.A Density-based Algorithm for Discovering Clusters in Large Spatial Databases.In Proc.1996 Int.Conf.Knowledge Discovery and Data Mining(KDD'96),pp226-231,Porland,Oregon,August 1996.
[FPS+96]U.M.Fayyad,G.Piatetsky-Shaperio,P.Smyth,R.Uthurusamy,Advances in Knowledge Discovery and Data Mining,AAAI/MIT Press,1996
[GAI00]M.Gavrilov,D.Anguelov,P.Indyk,R.Motwani.Mining the Stock Market:Which Measure is Best? In Proc.Of the KDD,pp.487-496,2000.
[GBD35]Greenshields,B.D.(1935).A Study of Traffic Capacity.Highway Research Board Proceedings 14,pp.448-477.
[GCH95]Gu.G.Q.(顾国庆),Chung.K.H.(钟家雄),Hui,P.M.(许伯铭).Pkysica A..1995;217:339-347
[GH59]Greenberg,H.(1959).An Analysis of Traffic Flow.Operations Research,Vol 7,pp.78-85.
[GHP59]Gazis.D.C,Herman.R.,Potts,R.B.Operations Res.,1959;7:499-510
[GHR61]Gazis.D.C,Herman.R.,Rothery,R.Operations.Res.,1961;9:545-567
[GRS98]S.Guha,R.Rastogi,K.Shim.CURE:An Efficient Clustering Algorithm for Large Databases.In Proceedings of the ACM SIGMOD Conference,Seattle,WA,pp73-84,1998.
[Guj03]D.N Gujarati.Basic Econometrics(4th edition).McGraw-Hill High Education,2003
[Gut84]A.Guttman.R-tree:A Dynamic Index Structure for Spatial Searching.Proc.ACM SIGMOD,pp.47-57,June 1984.
[GWY04]Guozhen Tan,Wenjiang Yuan,Hao Ding.Traffic Flow Prediction Based on Generalized Neural Network,2004 IEEE lnlelllgenl Transpollation Systems Conference.Washington,D.C.,USA,October 3-6,2004.
[HD80]Hawkins D Identification of Outliers Chapman and Hall,London,1980.
[HK98]Hinnebur g A,Keim D A.An efficient approach to clustering in large multimedia databases with noise.Discovery and Data Mining(KDD),1998
[HK01]J.W Han,M.Kambr.Data Mining:Concepts and Techniques.Academic Press,2001.
[HM86]Hall,F.L.and M.A.Gunter(1986).Further Analysis of the Flow-Concentration Relationship.Transportation Research Record 1091,TRB,NRC,Washington,DC,pp.1-9.
[HMS01]D.Hand,H.Mannila,P.Smyth.Principles of Data Mining,Massachusetts Institute of Technology,2001.
[HSW04]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系统仿真学报,vol16,No.7,2004,p1530-1533
[IO84]IWAO OKUTAKI.Dynamic Prediction of Traffic Volume Through Kaiman Filtering Theory.Transp Research Journal.1984,8(2),1-11.
[KCH01]E.Keogh,S.Chu,D.Hart D,M.Pazzani.An Online Algorithm for Segmenting Time Series.Proc of the IEEE International Conference on Data Mining.2001,289-296
[KHK99]G.Karypis,E.H Han,V.Kumar.CHAMELEON:A Hierarchical Clustering Algorithm Using Dynamic Modeling.COMPUTER,32:68-75,1999.
[KJP33]Kinzer.J.P.Application of the theory of probability to problem of highway traffic.B.C.E.thesis,Politech.Inst.Brooklyn.1933
[KK02]E.Keogh,S.Kasetty.On the Need for Time Series Data Mining Benchmarks:A Survey and Empirical Demonstration.In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.July 23-26,2002.Edmonton,Alberta,Canada.pp 102-111.
[KMI83]Koshi,M.,M.Iwasaki,and I.Okhura.Some Findings and an Overview on Vehicular Flow Characteristics.Proceedings,8th International Symposium on Transportation and Traffic Flow Theory(Edited by Hurdle,V.F.,Hauer,E.and Steuart,G.E)University of Toronto Press,Toronto,Canada,pp.403-426.
[KR90]L.Kaufman,P.J.Rousseeuw,Finding Groups in Data:an Introduction to Cluster Analysis,John Wiley and Sons,1990.
[LC03]Jia Lu,Li Cao.Congestion evaluation from traffic flow information based on fuzzy logic.IEEE,2003
[Liu03]刘智勇.智能交通控制理论和应用[M].北京:科学出版社,2003
[LP90]L.Kaufman and P.J.Rousseeuw.Finding Groups in Data:An introduction to Cluster Analysis.John Wiley & Sons,New York,1990
[LHP99]陆化普.《城市交通现代化管理》,人民交通出版社,1999.
[LLQ02]李炎,李皓,钱肖鲁,朱扬勇.异常检测算法分析.计算机工程,vol28,6,2002.6.p5-7.
[LTX02]李斌,谭立湘,解光军,李海鹰,庄镇泉.非同步多时间序列中频繁模式的发现算法,软件学报,2002,13(03)410-416,
[LW55]Lighthill.M.J.Whitham.G.B.Proc.Roy.Soc.Ser.A,1955,22:317-345
[LYD04]李存军,杨儒贵,邓红霞.基于小波和Kalman滤波的交叉路口流量组合预测模型.西南交通大学学报,vol39,5,2004.10.p577-p580.
[Mas99]Ichiro Masaki.A brief History of ITS[R].USA:Massachusetts Institute of Technology,1999.
[Mi199]T.C Mills.The Econometric Modelling of Financial Time Series(2nd edition).Cambridge University Press,UK,1999
[MTV95]H.Mannila,H.Toivonen,A.I Verkamo.Discovering Frequent episodes in Sequences.In Proc.Of KDD-95,pp:210-215,Montreal,Canada,Aug,1995.
[NH94]R.Ng,J.Han.Efficient and Effective Clustering Method for Spatial Data Mining.In Proc.Int.Conf.Very Large Data Bases(VLDB'94),pp.144-155,Santiago,Chile,1994.
[NT93]Nagatani.T.Phys.Rev.E.,1993;48:3290-3294
[OC96]T.Oates,P.R Cohen.Searching for Structure in Multiple Streams of Data.In:Proceedings of the 13~(th)International Conference on Machine Learning.Morgan Kaufmann Publishers,Inc.,1996
[PB98]Park B,Messer C J,Udamik T.Short-term Freeway Trafic Volume Fo~ asting using Radial Basis Function Neural Network[A].Transportation Research Record[C].Washington D C:National Academy Press,1998:39-47.
[PCY95]J.S Park,M.S Chen,P.S Yu.An Effective Hash-based Algorithm for Mining Association Rules.Proceedings of ACM SIGMOD International Conference on Management of Data,pages 175-186,San Jose,CA,May 1995.
[PH71]Prigogine I.Herman,R.Kinetic Theory of vechicular Traffic.New York Elsevier,1971
[PHJ71]Payne.H.J.Mathematical models of public systems.In:Bekey,A.G.(ed.)Simulation Council Proc.,La Jola,1971,1:51-61
[PHM+01]J.Pei,J.W.Han,B.Mortazavi-Asl,H.Pinto,Q.M.Chen,U.Dayal,M.C.Hsu.PrefixSpan:Mining Sequential Patterns by Prefix-Projected Growth,Proceedings of the 17~(th)International Conference on Data Engineering,pages 215-224,2001.
[PLA53]Pipes.L.A.J.Appl.Phys.,1953;24:274-281
[PNMD03]P.Levis,N.Lee,M.Welsh,and D.Culler.TOSSIM:Accurate and Scalable Simulation of Entire TinyOS Applications.In SenSys,2003.
[PR98]Park D,Rihtt L.Forecasting Multiple-period Freeway Link Travel Tunes Using Modular Neural Networks[A].Transportation Research Record[C].Washington D C,National Academy Pres,1998:163-t7
[PRH99]Park D,Rilett L R,Han G.Spectral Basis Neural Networks for Real-time Travel Time Forecasting[J].Journal of Transportation Engineering.1999,125(6):515-523.
[RPI56]Richards.I.Operations Res.1956;4:42-51
[RXY05]任江涛,谢琼琼,印鉴.交通流时间序列分离方法.计算机应用,2005
[RZL+01]任江涛,张毅,李志恒,胡东成.智能交通信息特征及亟待解决的相关问题.信息与控制,2001
[PZ05]潘笠,张晶.基于城市路口相关性的交通流量预测.交通与计算机.Vol23,122.2005.1.p31-p34.
[RG01]Robert Grossman,The Terabyte Challenge Discoverying Information in Distributed and Massive Data,KDD'01
[RR96]Ruts,Rousseeuw.P Computing Depth Contours of Bivariate Point Clouds.Computational Statistics and Data Analysis,1996.
[QCC06]覃明贵,崔中发等,iCentroView:城市交通监控与管理系统.计算机软件与应用,vol23.2,2006,p28-29
[SA95]R.Srikant and R.Agrawal.Mining Generalized Association Rules.VLDB'95,pp.407-419.
[SDK01]S.Borzsonyi,D.Kossmann,and K.Stocker.The skyline operator.ICDE,421-430,2001.
[Sha00]Gregory Piatetsky-Shapiro,Knowledge Discovery in Databases:10 years after,SIGKDD Explorations,ACM SIGKDD,January 2000.Volume 1,Issue2-Pages:59-61.
[SMP81]Stephanedes,Y.J.,Michalopoulos,P.G.,Plum,R.A.Improved estimation of traffic flow for real-time control.Transportation Research Record 795,Transportation Research Board,Washington,D.C.,28-29,1981.
[Smy97]P.Smyth.Clustering Sequences with Hidden Markov Models.In M.Mozer,M.Jordan,and T.Petsche,editors,Advances in Neural Information Processing Systems,volume 9,pages 648-654.MIT Press,1997.
[SON95]A.Savasere,E.Omiecinski,S.Navathe.An Efficient Algorithm for Mining Association Rules in Large Databases.Proceedings of the 21~(st)International Conference on Very large Database,1995.
[SS99]Z.Struzik,A.Sibes.Measuring Time Series Similarity Through Large Singular Features Revealed with Wavelet Transformation.In Proc.Of the 10~(th)Intl.Workshop on Database and Expert Systems Application,pp:162-166,1999.
[Stone77]Stone C.J.Consistent Nonparametric Regression.The Annuals of Statistics,595-645,1977.
[SWK02]Brian L.Smith,Billy M.Williams,R.Keith Oswald.Comparison of parametric and nonparametric models for traffic flow forecasting.Transportation Research Part C 10(2002)303-321.
[SYU+00]Y.Sakurai,M.Yoshikawa,S.Uemura,H.Kojima.The A-tree:An Index Structure for High-Dimensional Spaces Using Relative Approximation.In Proc.Of the 26~(th)International Conference on Very large Data Bases(VLDB),pp.516-526,Cairo,Egypt,2000.
[SZ04]史其信,郑为中.道路网短期交通流预测方法比较.交通运输工程学报,Vol14,No14,2004.12,pp68-71.
[TE01]K.-L.Tan,P.-K.Eng,and B.C.Ooi.Efficient progressive skyline computation.VLDB,301-310,2001.
[Toi96]H.Toivonen.Sampling Large Databases for Association Rules.Proceedings of the 22~(nd)International Conference on Very Large Database,Bombay,India,September 1996.
[TR99]T.Reinartz,Focusing Solutions for Data Mining,LNAI-1623,Springer Verlag,1999
[TW01]Trisha A.Hauser,William T.Scherer.Data mining tools for real-time traffic signal decision support & maintenance.IEEE,2001
[UGP96]U.M.Fayyad,G.Piatetsky-Shaperio,P.Smyth and R.Uthurusamy,Advances in Knowledge Discovery and Data Mining,AAAI/MIT Press,1996
[VPC93]VYTHOULKAS P.C.Alternative Approaches to Short Term Trafic Forecasting for Use in Driver Information Systems.Transportation and Traffic.Theory Elsevier Science Publishers.1993.
[WBM99]Williams,B.M.,1999.Modeling and Forecasting Vehicular Traffic Flow as a Seasonal Stochastic Time Series Process.Doctoral dissertation.Department of Civil Engineering,University of Virginia,Charlottesville.
[WR71]Wicdemann.R.Simulation des Strossenverkehrstrusses Schtriftreihe des Instituts fur Verkehrswesen der Universitat Karlsruhe,Karhruhe,1971
[WS05]王进,史其信.短时交通流预测模型综述,ITS通讯.2005,7(1).-10-13
[WSB98]R.Weber,H.J Schek,S.Blott.A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces.In Proc.Of 24~(th)VLDB Conference,pp194-205,New York,USA,1998.
[WW99]王衍文,王海滨.心音信号的识别与分类.[J]生物医学工程学杂志,1999,16(3):382-38.
[WW04]Z.J Wang,P.Willett.Joint Segmentation and Classification of Time Series Using Class-Specific Features.IEEE Transactions on Systems,Man and Cybernetics.2004,1056-1067
[WYM97]W.Wang,J.Yang,R.R.Muntz.STING:A Statistical Information Grid Approach to Spatial Data Ming.In Proc.Of the 23~(rd)Conference on VLDB,pp186-195,Athens,Greece,1997.
[XH01]徐中明,贺岩松,国外智能交通系统研究动向,汽车工业研究,2001年第一期
[YQ98]Hai Yang,Fengxiang Qiao.Neural network approach to classification of traffic flow states.Jorunal of Transportation Engineering,1998
[YWX02]YiIl H,Wong S C,Xu J,et al.Urban Tra 佑 c Flow Prediction Using a Fuzzy Neural Approach[J].Transportation Research(Part C),2002,10(2):85-98
[YZ99]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.中国公路学报.1999,12(3).-63-67.
[YZ04]G.Q Yu,C.S Zhang.Switching ARIMA Model based Forecasting for Traffic Flow,International Conference on Acoustics,Speech,and Signal Processing(ICASSP),May 17-21,2004,Canada.
[ZCW03]张静,蔡伯根,吴建平.移动检测技术的研究.北方交通大学学报.2003,27(3)80-83
[ZHS04]X.T Zhuang,X.Y Huang,Y.L Sun.Research on the Fractal Structure in the Chinese Stock Market.Physica a-Statistical Mechanics and Its Applications 333:293-305,2004.
[ZRL96]T.Zhang,R.Ramakrishnan,and M.Livny.BIRCH:An Efficient Data Clustering Method for Very Large Databases.In Porc.Of ACM SIGMOD,Montreal Canada,pp.103-114,June 1996.
[ZSH03]H.Q Zeng,Z.Shen,Y.F Hu.Mining Squence Pattern form Time Series Based on Inter-Relevant Successive Trees Model.In Proceedings of 9~(th)International Conference on Rough Sets,Fuzzy Sets,Data Mining and Granular Computing,LNCS/LNAI,Spring-Verlag,2003.
[ABK+99] M. Ankerst, M. Breunig, H.P Kriegel, J. Sander. OPTICS: Ordering Points to Identify Clustering Structure. In Proc. Of the ACM SIGMOD Conference, pp.49-60, Philadelphia, PA, 1999.
[AFS93] R. Agrawal, C. Faloutsos, A. Swami. Efficient similarity search in sequence databases. In Procs. Of the Fourth International Conference on Foundations of Data Organization and Algorithms, 1993.
[AGG+98] R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan. Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. In Proc. Of the ACM SIGMOD Conference, pp.94-105, 1998.
[AIS93] R. Agrawal, T. Imielinski, A. Swami. Mining Association Rules Between Sets of Items in Large Databases. In Proc. Of the ACM-SIGMOD Int. of Conf. on Management of Data. Washington D.C., pp.207-216,1993.
[APR02] AbdIlll1ai B, Porwal H, Recker. Short-term Trafic Flow Prediction Using Neural-Genetic Algorithms[J]. ITS Journal, 2002, 7(1): 3 —41.
[AS94] R. Agrawal, R. Srikant. Fast Algorithms for Mining Association Rules. In Proc. Of the 20th VLDB Conference. Santiago Chile, pp.487-499,1994.
[ASK+03] J.Alon, S. Sclaro, G Kollios, V. Pavlovic. Discovering Clusters in Motion Time-series Data. In IEEE Computer Vision and Pattern Recognition Conference (CVPR), 2003.
[AWF36] Adams.W.F. J. Inst. Civil Engr., 1936; 4:121-130 [AY01] Aggarwal C, Yu P Outlier Detection for High Dimensional Data In Proc of ACM SIGMOD 2001.
[BC94] J.D Berndt and J. Clifford. Using Dynamic Time Warping to Find Patterns in Time Series. In Working Notes of the Knowledge Discovery in Databases Workshop, pp.359-370,1994.
[BGR+99] K. Beyer, J. Goldstein, R. Ramakrishnan, U. Shaft. When is Nearest Neighbors Meaningful? In ICDT Conference Preceedings, Jerusalem Israel, pp.217-235,1999.
[BJH89] Banks, J. H. Two-Capacity Phenomenon at Freeway Bottlenecks: A Basis for Ramp Metering? Transportation Research Record 1320, TRB, NRC, Washington, DC,pp. 83-90.
[BJR94] G.E Box, G.M Jenkins, and G.C Reinsel. Time Series Analysis Forecasting and Control (3th edition). Prentice Hall, Englewood Cliffs, 1994.
[BJZ03]A.J.Bagnall,G.Janakec,M.Zhang.Clustering Time Series from Mixtnre Polynomial Models with Discretised Data.Technical Report CMP-C03-17,School of Computing Sciences,University of East Anglia,2003
[BKN00]Breunig M,Kriegel H P,Ng R,et al LOF:Identifying Density.basedLocal Outliers.In ACM SIGMOD Conference Proceedings,2000.
[BKS+90]N.Beckman,H.P.Kriegel,R.Schneider,B.Seeger.The R~*-tree:An Efficient and Robust Access Method for Points and Rectangles.In Proc.ACM SIGMOD Conf.,pp.322-331,Atlantic City,NJ,May 1990.
[BML92]Biham.O.,Middleton,A.A.,Levine,n Phys.Rev.A.,1992,46:6124-6127
[BMU97]S.Brin,R.Motwani,J.D Ullman,S.Tsur.Dynamic Itemset Counting and Implication Rules for Market Basket Data,Proc.ACM SIGMOD Int'l Conf.Management of Data,ACM Press,New York,1997,pp.255-264.
[BSD99]Bhargab Maitra,P.K.Sikdar,S.L.Dhingra,Modeling Congrestion on Urban Roads and Assessing Level of Service.Transportation Engineering,1999
[BT96]R.J.Brachman and T.Anand,The Process of Knowledge Discovery in Databases,Charpter 2 in[Fayyad96],1996
[CGS01]Chen Hmbo,Grant-Muller,Susan.Use of Sequential Learning for Short-term Traffic Flow Forecasting[J].Transportation Reseamh(Part C),2001,9(5):319-336.
[CHG95]Chung.K.H.,Hui.P.M.,Gu.G..Q.Phys.Rev.E,1995;51:772-774
[CMY94]C.Fanoutsos,M.Ranganathan,and Y.Manolopounos.Fast subsequence matching in time-series databases.In Proc.ACM SIGMOD Int.Conf.on Management of Data,pages 419-429,1994
[CPZ97]P.Ciaccia,M.Patella,P.Zezula.M-tree:An Efficient Access Method for Similarity Search in Metric Spaces.In Proc.Of the 23~(th)International Conference on Very large Data Bases(VLDB),Athens,August 1997.
[Davis91]Gary A.Davis,Nonparametric Regression and Short-Term Freeway Traffic Forecast.Transportation Engineering,178-187,Vol.117,No.2,1991.
[DLQ05]董超俊、刘智勇、邱祖廉.城市交通控制智能优化配时及仿真.系统仿真学报,Vol.17 No.2,2005.2,pp472-475.
[DFS02]D.Kossmann,F.Ramsak,and S.Rost.Shooting stars in the sky:An online algorithm for skyline queries.In VLDB,275-286,2002.
[DHP01]D.Hand,H.mallina,ald P.Smyth.Principles of Data Mining.The MIT Press,2001
[DJ94]D.Berndt & J.Clifford.Using dynamic time warping to find patterns in time series[C].AAAI-94 Workshop on Knowledge Discovery in databases(KDD-94),Seattle,Washington,1994 D.J.Berndt and J.Clifford.Finding patterns in time series:A dynamic
[DJ96]programming approach.In advances in Knowledge Discovery and Data Mining,pages 229-248,1996.
[DLMJ83]D.L鸠洛夫,M.J.休伯,交通流理论,蒋璜等译.北京:人民变通出版社,1983
[DLM+98]G.Das,K.Lin,H.Mannila,G.Renganathan,P.Smyth.Rule Discovery from Time Series.KDD 1998,pp:16-22.
[DYGB05]D.Papadias,Y.Tao,G.Fu,and B.Seeger.Progressive skyline computation in database systems.TODS,30(1):41-82,2005.
[DZS07]Ke Deng,Xiaofang Zhou and Heng Tao Shen.Multi-source Skyline Query Processing in Road Networks.In Proceedings of 23rd IEEE International Conference on Data Engineering(ICDE),2007.
[EKS+96]M.Ester,H.P Kriegel,J.Sander,X.Xu.A Density-based Algorithm for Discovering Clusters in Large Spatial Databases.In Proc.1996 Int.Conf.Knowledge Discovery and Data Mining(KDD'96),pp226-231,Porland,Oregon,August 1996.
[FPS+96]U.M.Fayyad,G.Piatetsky-Shaperio,P.Smyth,R.Uthurusamy,Advances in Knowledge Discovery and Data Mining,AAAI/MIT Press,1996
[GAI00]M.Gavrilov,D.Anguelov,P.Indyk,R.Motwani.Mining the Stock Market:Which Measure is Best? In Proc.Of the KDD,pp.487-496,2000.
[GBD35]Greenshields,B.D.(1935).A Study of Traffic Capacity.Highway Research Board Proceedings 14,pp.448-477.
[GCH95]Gu.G.Q.(顾国庆),Chung.K.H.(钟家雄),Hui,P.M.(许伯铭).Pkysica A..1995;217:339-347
[GH59]Greenberg,H.(1959).An Analysis of Traffic Flow.Operations Research,Vol 7,pp.78-85.
[GHP59]Gazis.D.C,Herman.R.,Potts,R.B.Operations Res.,1959;7:499-510
[GHR61]Gazis.D.C,Herman.R.,Rothery,R.Operations.Res.,1961;9:545-567
[GRS98]S.Guha,R.Rastogi,K.Shim.CURE:An Efficient Clustering Algorithm for Large Databases.In Proceedings of the ACM SIGMOD Conference,Seattle,WA,pp73-84,1998.
[Guj03]D.N Gujarati.Basic Econometrics(4th edition).McGraw-Hill High Education,2003
[Gut84]A.Guttman.R-tree:A Dynamic Index Structure for Spatial Searching.Proc.ACM SIGMOD,pp.47-57,June 1984.
[GWY04]Guozhen Tan,Wenjiang Yuan,Hao Ding.Traffic Flow Prediction Based on Generalized Neural Network,2004 IEEE lnlelllgenl Transpollation Systems Conference.Washington,D.C.,USA,October 3-6,2004.
[HD80]Hawkins D Identification of Outliers Chapman and Hall,London,1980.
[HK98]Hinnebur g A,Keim D A.An efficient approach to clustering in large multimedia databases with noise.Discovery and Data Mining(KDD),1998
[HK01]J.W Han,M.Kambr.Data Mining:Concepts and Techniques.Academic Press,2001.
[HM86]Hall,F.L.and M.A.Gunter(1986).Further Analysis of the Flow-Concentration Relationship.Transportation Research Record 1091,TRB,NRC,Washington,DC,pp.1-9.
[HMS01]D.Hand,H.Mannila,P.Smyth.Principles of Data Mining,Massachusetts Institute of Technology,2001.
[HSW04]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系统仿真学报,vol16,No.7,2004,p1530-1533
[IO84]IWAO OKUTAKI.Dynamic Prediction of Traffic Volume Through Kaiman Filtering Theory.Transp Research Journal.1984,8(2),1-11.
[KCH01]E.Keogh,S.Chu,D.Hart D,M.Pazzani.An Online Algorithm for Segmenting Time Series.Proc of the IEEE International Conference on Data Mining.2001,289-296
[KHK99]G.Karypis,E.H Han,V.Kumar.CHAMELEON:A Hierarchical Clustering Algorithm Using Dynamic Modeling.COMPUTER,32:68-75,1999.
[KJP33]Kinzer.J.P.Application of the theory of probability to problem of highway traffic.B.C.E.thesis,Politech.Inst.Brooklyn.1933
[KK02]E.Keogh,S.Kasetty.On the Need for Time Series Data Mining Benchmarks:A Survey and Empirical Demonstration.In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.July 23-26,2002.Edmonton,Alberta,Canada.pp 102-111.
[KMI83]Koshi,M.,M.Iwasaki,and I.Okhura.Some Findings and an Overview on Vehicular Flow Characteristics.Proceedings,8th International Symposium on Transportation and Traffic Flow Theory(Edited by Hurdle,V.F.,Hauer,E.and Steuart,G.E)University of Toronto Press,Toronto,Canada,pp.403-426.
[KR90]L.Kaufman,P.J.Rousseeuw,Finding Groups in Data:an Introduction to Cluster Analysis,John Wiley and Sons,1990.
[LC03]Jia Lu,Li Cao.Congestion evaluation from traffic flow information based on fuzzy logic.IEEE,2003
[Liu03]刘智勇.智能交通控制理论和应用[M].北京:科学出版社,2003
[LP90]L.Kaufman and P.J.Rousseeuw.Finding Groups in Data:An introduction to Cluster Analysis.John Wiley & Sons,New York,1990
[LHP99]陆化普.《城市交通现代化管理》,人民交通出版社,1999.
[LLQ02]李炎,李皓,钱肖鲁,朱扬勇.异常检测算法分析.计算机工程,vol28,6,2002.6.p5-7.
[LTX02]李斌,谭立湘,解光军,李海鹰,庄镇泉.非同步多时间序列中频繁模式的发现算法,软件学报,2002,13(03)410-416,
[LW55]Lighthill.M.J.Whitham.G.B.Proc.Roy.Soc.Ser.A,1955,22:317-345
[LYD04]李存军,杨儒贵,邓红霞.基于小波和Kalman滤波的交叉路口流量组合预测模型.西南交通大学学报,vol39,5,2004.10.p577-p580.
[Mas99]Ichiro Masaki.A brief History of ITS[R].USA:Massachusetts Institute of Technology,1999.
[Mi199]T.C Mills.The Econometric Modelling of Financial Time Series(2nd edition).Cambridge University Press,UK,1999
[MTV95]H.Mannila,H.Toivonen,A.I Verkamo.Discovering Frequent episodes in Sequences.In Proc.Of KDD-95,pp:210-215,Montreal,Canada,Aug,1995.
[NH94]R.Ng,J.Han.Efficient and Effective Clustering Method for Spatial Data Mining.In Proc.Int.Conf.Very Large Data Bases(VLDB'94),pp.144-155,Santiago,Chile,1994.
[NT93]Nagatani.T.Phys.Rev.E.,1993;48:3290-3294
[OC96]T.Oates,P.R Cohen.Searching for Structure in Multiple Streams of Data.In:Proceedings of the 13~(th)International Conference on Machine Learning.Morgan Kaufmann Publishers,Inc.,1996
[PB98]Park B,Messer C J,Udamik T.Short-term Freeway Trafic Volume Fo~ asting using Radial Basis Function Neural Network[A].Transportation Research Record[C].Washington D C:National Academy Press,1998:39-47.
[PCY95]J.S Park,M.S Chen,P.S Yu.An Effective Hash-based Algorithm for Mining Association Rules.Proceedings of ACM SIGMOD International Conference on Management of Data,pages 175-186,San Jose,CA,May 1995.
[PH71]Prigogine I.Herman,R.Kinetic Theory of vechicular Traffic.New York Elsevier,1971
[PHJ71]Payne.H.J.Mathematical models of public systems.In:Bekey,A.G.(ed.)Simulation Council Proc.,La Jola,1971,1:51-61
[PHM+01]J.Pei,J.W.Han,B.Mortazavi-Asl,H.Pinto,Q.M.Chen,U.Dayal,M.C.Hsu.PrefixSpan:Mining Sequential Patterns by Prefix-Projected Growth,Proceedings of the 17~(th)International Conference on Data Engineering,pages 215-224,2001.
[PLA53]Pipes.L.A.J.Appl.Phys.,1953;24:274-281
[PNMD03]P.Levis,N.Lee,M.Welsh,and D.Culler.TOSSIM:Accurate and Scalable Simulation of Entire TinyOS Applications.In SenSys,2003.
[PR98]Park D,Rihtt L.Forecasting Multiple-period Freeway Link Travel Tunes Using Modular Neural Networks[A].Transportation Research Record[C].Washington D C,National Academy Pres,1998:163-t7
[PRH99]Park D,Rilett L R,Han G.Spectral Basis Neural Networks for Real-time Travel Time Forecasting[J].Journal of Transportation Engineering.1999,125(6):515-523.
[RPI56]Richards.I.Operations Res.1956;4:42-51
[RXY05]任江涛,谢琼琼,印鉴.交通流时间序列分离方法.计算机应用,2005
[RZL+01]任江涛,张毅,李志恒,胡东成.智能交通信息特征及亟待解决的相关问题.信息与控制,2001
[PZ05]潘笠,张晶.基于城市路口相关性的交通流量预测.交通与计算机.Vol23,122.2005.1.p31-p34.
[RG01]Robert Grossman,The Terabyte Challenge Discoverying Information in Distributed and Massive Data,KDD'01
[RR96]Ruts,Rousseeuw.P Computing Depth Contours of Bivariate Point Clouds.Computational Statistics and Data Analysis,1996.
[QCC06]覃明贵,崔中发等,iCentroView:城市交通监控与管理系统.计算机软件与应用,vol23.2,2006,p28-29
[SA95]R.Srikant and R.Agrawal.Mining Generalized Association Rules.VLDB'95,pp.407-419.
[SDK01]S.Borzsonyi,D.Kossmann,and K.Stocker.The skyline operator.ICDE,421-430,2001.
[Sha00]Gregory Piatetsky-Shapiro,Knowledge Discovery in Databases:10 years after,SIGKDD Explorations,ACM SIGKDD,January 2000.Volume 1,Issue2-Pages:59-61.
[SMP81]Stephanedes,Y.J.,Michalopoulos,P.G.,Plum,R.A.Improved estimation of traffic flow for real-time control.Transportation Research Record 795,Transportation Research Board,Washington,D.C.,28-29,1981.
[Smy97]P.Smyth.Clustering Sequences with Hidden Markov Models.In M.Mozer,M.Jordan,and T.Petsche,editors,Advances in Neural Information Processing Systems,volume 9,pages 648-654.MIT Press,1997.
[SON95]A.Savasere,E.Omiecinski,S.Navathe.An Efficient Algorithm for Mining Association Rules in Large Databases.Proceedings of the 21~(st)International Conference on Very large Database,1995.
[SS99]Z.Struzik,A.Sibes.Measuring Time Series Similarity Through Large Singular Features Revealed with Wavelet Transformation.In Proc.Of the 10~(th)Intl.Workshop on Database and Expert Systems Application,pp:162-166,1999.
[Stone77]Stone C.J.Consistent Nonparametric Regression.The Annuals of Statistics,595-645,1977.
[SWK02]Brian L.Smith,Billy M.Williams,R.Keith Oswald.Comparison of parametric and nonparametric models for traffic flow forecasting.Transportation Research Part C 10(2002)303-321.
[SYU+00]Y.Sakurai,M.Yoshikawa,S.Uemura,H.Kojima.The A-tree:An Index Structure for High-Dimensional Spaces Using Relative Approximation.In Proc.Of the 26~(th)International Conference on Very large Data Bases(VLDB),pp.516-526,Cairo,Egypt,2000.
[SZ04]史其信,郑为中.道路网短期交通流预测方法比较.交通运输工程学报,Vol14,No14,2004.12,pp68-71.
[TE01]K.-L.Tan,P.-K.Eng,and B.C.Ooi.Efficient progressive skyline computation.VLDB,301-310,2001.
[Toi96]H.Toivonen.Sampling Large Databases for Association Rules.Proceedings of the 22~(nd)International Conference on Very Large Database,Bombay,India,September 1996.
[TR99]T.Reinartz,Focusing Solutions for Data Mining,LNAI-1623,Springer Verlag,1999
[TW01]Trisha A.Hauser,William T.Scherer.Data mining tools for real-time traffic signal decision support & maintenance.IEEE,2001
[UGP96]U.M.Fayyad,G.Piatetsky-Shaperio,P.Smyth and R.Uthurusamy,Advances in Knowledge Discovery and Data Mining,AAAI/MIT Press,1996
[VPC93]VYTHOULKAS P.C.Alternative Approaches to Short Term Trafic Forecasting for Use in Driver Information Systems.Transportation and Traffic.Theory Elsevier Science Publishers.1993.
[WBM99]Williams,B.M.,1999.Modeling and Forecasting Vehicular Traffic Flow as a Seasonal Stochastic Time Series Process.Doctoral dissertation.Department of Civil Engineering,University of Virginia,Charlottesville.
[WR71]Wicdemann.R.Simulation des Strossenverkehrstrusses Schtriftreihe des Instituts fur Verkehrswesen der Universitat Karlsruhe,Karhruhe,1971
[WS05]王进,史其信.短时交通流预测模型综述,ITS通讯.2005,7(1).-10-13
[WSB98]R.Weber,H.J Schek,S.Blott.A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces.In Proc.Of 24~(th)VLDB Conference,pp194-205,New York,USA,1998.
[WW99]王衍文,王海滨.心音信号的识别与分类.[J]生物医学工程学杂志,1999,16(3):382-38.
[WW04]Z.J Wang,P.Willett.Joint Segmentation and Classification of Time Series Using Class-Specific Features.IEEE Transactions on Systems,Man and Cybernetics.2004,1056-1067
[WYM97]W.Wang,J.Yang,R.R.Muntz.STING:A Statistical Information Grid Approach to Spatial Data Ming.In Proc.Of the 23~(rd)Conference on VLDB,pp186-195,Athens,Greece,1997.
[XH01]徐中明,贺岩松,国外智能交通系统研究动向,汽车工业研究,2001年第一期
[YQ98]Hai Yang,Fengxiang Qiao.Neural network approach to classification of traffic flow states.Jorunal of Transportation Engineering,1998
[YWX02]YiIl H,Wong S C,Xu J,et al.Urban Tra 佑 c Flow Prediction Using a Fuzzy Neural Approach[J].Transportation Research(Part C),2002,10(2):85-98
[YZ99]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.中国公路学报.1999,12(3).-63-67.
[YZ04]G.Q Yu,C.S Zhang.Switching ARIMA Model based Forecasting for Traffic Flow,International Conference on Acoustics,Speech,and Signal Processing(ICASSP),May 17-21,2004,Canada.
[ZCW03]张静,蔡伯根,吴建平.移动检测技术的研究.北方交通大学学报.2003,27(3)80-83
[ZHS04]X.T Zhuang,X.Y Huang,Y.L Sun.Research on the Fractal Structure in the Chinese Stock Market.Physica a-Statistical Mechanics and Its Applications 333:293-305,2004.
[ZRL96]T.Zhang,R.Ramakrishnan,and M.Livny.BIRCH:An Efficient Data Clustering Method for Very Large Databases.In Porc.Of ACM SIGMOD,Montreal Canada,pp.103-114,June 1996.
[ZSH03]H.Q Zeng,Z.Shen,Y.F Hu.Mining Squence Pattern form Time Series Based on Inter-Relevant Successive Trees Model.In Proceedings of 9~(th)International Conference on Rough Sets,Fuzzy Sets,Data Mining and Granular Computing,LNCS/LNAI,Spring-Verlag,2003.