基于多智能体的交通控制与交通诱导协同理论和方法研究
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摘要
交通管理在城市交通问题治理中十分重要,其目的是提供一个畅通、有序的交通环境。建立智能交通管理系统,广泛应用科学技术手段,全面提高管理水平和管理效益,是解决当前道路交通拥堵、事故频繁发生和环境污染严重的有效途径。在交通管理实施中涉及到很多管理策略的制定,包括交通诱导和控制策略的制定。由于交通系统本身的复杂性,在交通管理中涉及的决策数据的信息量巨大,完全依靠人工来完成城市交通控制和诱导策略的制定存在很大困难,难以实现。基于多智能体的智能决策支持技术的出现,可以满足交通管理中的决策需求。本文深入剖析交通控制与交通流诱导系统的运行机理,在现有的相关研究中,着重对比较先进的多智能技术和智能决策理论在交通控制和诱导协调中的应用进行了深入的研究。提出了将多智能体理论和智能决策理论相结合应用在交通协调控制之中,建立一种交通管理决策的模型,并分析了其实施的方法,更好地实现交通控制和诱导,为城市交通拥挤问题的治理提供了一个良好的尝试,为更好的解决现有交通拥挤问题,寻求区域交通优化的控制提供了理论基础。
Presently urban traffic problem is a globally puzzle, especially in China. In which urbanization becomes more and more with great economic development. Serious traffic congestions and frequent traffic accidents always exist. The extension and construction of roads cannot solve the problem. As a means of improving urban traffic, Intelligent Transportation Systems (ITS) obtain more and more recognition especially in China. The traffic management system, as an important constituent of ITS, greatly benefit and improve traffic management. Urban Traffic Control System (UTCS) and Urban Traffic Flow Guidance System (UTFGS) are two important subsystems of Intelligent Transportation Systems (ITS). The cooperation of the two subsystems is the key method of resolving the traffic problem. For the Characteristics of modern traffic are complex, levity and distribution, the Multi-Agent Technology is adopted in the study of the urban traffic flow guidance system coordinated with traffic control , and the Intelligent Decision Support Technology is used in the global solution optimizing, in order to reduce the traffic jam, decrease the exhaust gas, improve the efficiency of the traffic management.
This research consists of six chapters. The main works and contributions are described as follows:
The main contents as follows.
1. Introduction. Chapter one mainly summarizes the research background, the aim, the meaning and the content of this thesis. Through the analyzing of the present study and the approach of the Traffic Control and the Traffic Guidance cooperation, find out the exiting problem, and summarized the cooperation model and the arithmetic.
2. The study on the framework construction of the cooperation of the UTCS and the UTGS. Combining the complex characteristic of the traffic and the Multi-Agent, analyzing the feasibility of the Multi-Agent technology applied in the cooperation of the two systems. The distributed hierarchal architecture of the cooperative implementing system is presented. The Intelligent Decisions Support System theory is also adopted for affording the basis for the globe solution established. There are the Section Agent, Control Agent, Cooperation Agent, Area Agent and the Centre Agent in this system, All the Agents Share the common information, and the upper Agent is charge of the lower Agent.
3. The information processing of the Traffic Control and the Traffic Guidance cooperation.In traffic management, timely access to accurate, comprehensive traffic information directly related to the correctness and rationality made by traffic management strategy. Travel information which can token traffic flow parameters and react traffic state is the basis of achieving traffic control and traffic management strategy. Only based on real-time, high-precision basic traffic parameters information and network traffic flow state information,can we formulate the corresponding traffic control and induced strategy .then manage traffic flow to the best running reasonablely and effectively.collection and processing of transportation infrastructure information is the guarantee of achieving the Traffic Control and the Traffic Guidance cooperation.after traffic information is collected and processed, it provides traffic control and traffic-induced collaborative applications.This chapter focuses on the information processing technology of the Traffic Control and the Traffic Guidance cooperation,and proposes a fusion algorithm used in the space traffic speeds.The algorithm can analyze automatically and treat integratedly the detected traffic parameters (traffic flow, average speed, instantaneous speed, distance at the front, the model classification, lane share, etc.) in a certain criteria, then complete the necessary decision-making and assessment.it can coordinate multi-source data reasonsablely, integrate useful information fully,increase the correct decision-making capacity in the volatile environment.
4. Research on traffic control and optimization of induced synergies based on the multi-agent.presently, traffic control and induced synergies has been a national focus on transport workers.it refers to under the macroeconomic regulation and control in the centre of traffic, based on different traffic flow, make full use of the advantages of complementarity between the junction, balance traffic flow of each intersection, So as to enhance the capacity of the road. This chapter mainly research on how to use the multi-agent means to achieve control and induced optimize synergies.apply strengthening the study to realize study characteristicof multi-agent.combining genetic algorithm, establish the model of traffic control and induced optimize synergies and solve algorithms.with VISSIM software, simulate using analog network,verify the effectiveness of the algorithm.
5. Research on overall optimization decision. This chapter analyzed the foundation of the overall optimization strategy. Because the coordination of traffic control and traffic navigation confront to larger regional context, based on multi-agent's self-government, before the overall optimization, the whole region is divided into several sub-region, and the complex tasks of the coordination of traffic control and traffic navigation is divided into several sub-tasks and the overall optimization strategy is easily achieved. This chapter introduced knowledge-based model, which involves the acquisition of knowledge, repository and reasoning mechanism, hold the experts' knowledge and experience in a particular field, and can use the knowledge like the experts, and make intelligent decisions by reasoning in the field, and simulate the process of decision-making to solve the complex problems which can be solved by the experts. At the same time, combine the characteristics of multi-agent study, apply Q-learning to establish the intersection model and the model of regional coordination in traffic control. According to the decision-making strategy of winning or losing, Game Theory will be applied to strategic planning of decision-making level the overall optimize and give an evaluation index of the model and system.
6. Concluding remarks of the research. The main research achievements and further study problems are pointed out.
Presently urban traffic problem is a globally puzzle, especially in China. In which urbanization becomes more and more with great economic development. Serious traffic congestions and frequent traffic accidents always exist. The extension and construction of roads cannot solve the problem. As a means of improving urban traffic, Intelligent Transportation Systems (ITS) obtain more and more recognition especially in China. The traffic management system, as an important constituent of ITS, greatly benefit and improve traffic management. Urban Traffic Control System (UTCS) and Urban Traffic Flow Guidance System (UTFGS) are two important subsystems of Intelligent Transportation Systems (ITS). The cooperation of the two subsystems is the key method of resolving the traffic problem. For the Characteristics of modern traffic are complex, levity and distribution, the Multi-Agent Technology is adopted in the study of the urban traffic flow guidance system coordinated with traffic control , and the Intelligent Decision Support Technology is used in the global solution optimizing, in order to reduce the traffic jam, decrease the exhaust gas, improve the efficiency of the traffic management.
This research consists of six chapters. The main works and contributions are described as follows:
The main contents as follows.
1. Introduction. Chapter one mainly summarizes the research background, the aim, the meaning and the content of this thesis. Through the analyzing of the present study and the approach of the Traffic Control and the Traffic Guidance cooperation, find out the exiting problem, and summarized the cooperation model and the arithmetic.
2. The study on the framework construction of the cooperation of the UTCS and the UTGS. Combining the complex characteristic of the traffic and the Multi-Agent, analyzing the feasibility of the Multi-Agent technology applied in the cooperation of the two systems. The distributed hierarchal architecture of the cooperative implementing system is presented. The Intelligent Decisions Support System theory is also adopted for affording the basis for the globe solution established. There are the Section Agent, Control Agent, Cooperation Agent, Area Agent and the Centre Agent in this system, All the Agents Share the common information, and the upper Agent is charge of the lower Agent.
3. The information processing of the Traffic Control and the Traffic Guidance cooperation.In traffic management, timely access to accurate, comprehensive traffic information directly related to the correctness and rationality made by traffic management strategy. Travel information which can token traffic flow parameters and react traffic state is the basis of achieving traffic control and traffic management strategy. Only based on real-time, high-precision basic traffic parameters information and network traffic flow state information,can we formulate the corresponding traffic control and induced strategy .then manage traffic flow to the best running reasonablely and effectively.collection and processing of transportation infrastructure information is the guarantee of achieving the Traffic Control and the Traffic Guidance cooperation.after traffic information is collected and processed, it provides traffic control and traffic-induced collaborative applications.This chapter focuses on the information processing technology of the Traffic Control and the Traffic Guidance cooperation,and proposes a fusion algorithm used in the space traffic speeds.The algorithm can analyze automatically and treat integratedly the detected traffic parameters (traffic flow, average speed, instantaneous speed, distance at the front, the model classification, lane share, etc.) in a certain criteria, then complete the necessary decision-making and assessment.it can coordinate multi-source data reasonsablely, integrate useful information fully,increase the correct decision-making capacity in the volatile environment.
4. Research on traffic control and optimization of induced synergies based on the multi-agent.presently, traffic control and induced synergies has been a national focus on transport workers.it refers to under the macroeconomic regulation and control in the centre of traffic, based on different traffic flow, make full use of the advantages of complementarity between the junction, balance traffic flow of each intersection, So as to enhance the capacity of the road. This chapter mainly research on how to use the multi-agent means to achieve control and induced optimize synergies.apply strengthening the study to realize study characteristicof multi-agent.combining genetic algorithm, establish the model of traffic control and induced optimize synergies and solve algorithms.with VISSIM software, simulate using analog network,verify the effectiveness of the algorithm.
5. Research on overall optimization decision. This chapter analyzed the foundation of the overall optimization strategy. Because the coordination of traffic control and traffic navigation confront to larger regional context, based on multi-agent's self-government, before the overall optimization, the whole region is divided into several sub-region, and the complex tasks of the coordination of traffic control and traffic navigation is divided into several sub-tasks and the overall optimization strategy is easily achieved. This chapter introduced knowledge-based model, which involves the acquisition of knowledge, repository and reasoning mechanism, hold the experts' knowledge and experience in a particular field, and can use the knowledge like the experts, and make intelligent decisions by reasoning in the field, and simulate the process of decision-making to solve the complex problems which can be solved by the experts. At the same time, combine the characteristics of multi-agent study, apply Q-learning to establish the intersection model and the model of regional coordination in traffic control. According to the decision-making strategy of winning or losing, Game Theory will be applied to strategic planning of decision-making level the overall optimize and give an evaluation index of the model and system.
6. Concluding remarks of the research. The main research achievements and further study problems are pointed out.
引文
[1]全永燊,城市交通控制[M],北京,人民交通出版社,1989
[2]段里仁,道路交通自动控制[M],北京,中国人民公安大学出版社,1991
[3]王笑京,智能交通系统研发历程与动态述评[J],城市交通,Vol.6 No.1 Jan,2008-6-12
[4]杨兆升,论智能运输系统,中国公路学报[J],1995年第8卷第4期,102-110
[5]王笑京,智能运输系统体系框架研究,交通与计算机[J],2001年第4期,4-6
[6]黄卫,陈里得,智能运输系统(ITS)概论[M],北京,人民交通出版社,2001
[7]郭治安,协同学入门[M],成都,四川人民出版社,1988,第1版,23-30
[8]H.哈肯著[德],信息与自组织[M],成都,四川教育出版社,1988,第1版
[9]陈昕,基于协同学的城市交通控制与诱导系统协同的理论与方法研究[D],吉林大学,2006
[10]陈小锋,城市交通信号动态优化控制技术研究[D],西北工业大学,2003
[11]尹宏宾,徐建闽,道路交通控制技术,广州,华南理工大学出版社,2000
[12]Allsop R.E,Some possibilities for using traffic control to influence t rip distribution and route choice[A],Proceeding 6th International Symposium Transportation and Traffic Theory[C],Sydney,1974
[13]Tan H.N,S.Gershwin,M.Athans.Hybrid optimization in urban traffic networks[R].Research and special programs administration[C],U.S Department of Transportation,1979
[14]刘虹秀,魏武,彭驰,基于Multi-agent的城市交通控制与诱导协调研究[J],公路与汽运,2007(5),42-44
[15]M.J.Smith and T.Van Vuren,Traffic equilibrium with responsive traffic control[J],Transportation Science,Vol.27,No.2,1993,118-132.
[16]Y.Sheffi.and W.B.Powell,Optimal signal settings over transportation networks[J],Journal of Transportation Engineering,Vol.109,No.6,1983,824-839.
[17]M.J.Smith,Traffic signals in assignment[J],Transportation Research,Vol.19B,No.2,1985,155-160
[18]Gartner,Nathan H,Stamatiadis,Integration of dynamic traffic assignment with real-time traffic adaptive control system[J],Transportation Research Record,1998,150-156
[19]M.J.Smith,T.Van Vuren,B.G.Heydecker and D.Van Vliet,The interactions between signal control policies and route choice[C],Proceedings of the Tenth International Symposium on Transportation and Traffic Theory,1987,319-338
[20]徐岩宇,冯蔚东,贺国光,VRGS与交通控制系统的一体化研究,公路交通科技,1997,9,24-2
[21]徐立群,城市交通流诱导与控制一体化理论和模型研究[D],吉林大学,2000,12-17
[22]李瑞敏,史其信,基于多智能体系统的城市交通控制与诱导集成化研究[J],公路交通科技,2004年05期,15-19
[23]魏连雨,李巧茹,城市交通系统的可协调发展[J],河北工业大学学报,1998年03期,55-62
[24]Wooldridge,M.,Muller,J.M.,and Tambe,M.Agents,theories,architectures and languages[J],(ATAL-95)Springer Verlag Lecture Notes in Artificial Intelligence(LNAI 1037)
[25]Russell,S.and Norvig.p.Artificial Intelligence[J],A Modem Approach Prentice Hall,New Jersey,1995
[26]孙晋文,基于Agent的智能交通控制策略与可视化动态仿真研究[D],中国农业大学,2001,5,10-25
[27]Kadar B,Monnstori L,Szelke E,An object-oriented framework for developing distributed manufacturing architecture[J],Journal of Intelligent Manufacturing,1998,9(2),173-179
[28]董宏斌,石纯一,移动Agent技术研究[J],计算机科学,2000,27(4),35-38
[29]李海刚,吴启迪多Agent系统综述[J],同济大学学报,2003,Vol.31(6),728-732
[30]贺建立,多智能体系统的开发[D],合肥,安徽大学,2004
[31]Nicholas R.Jennings,Michael J.Wooldinge,Agent Technology-Foundation[J],Application and Markets,Springer,1997
[32]H.Van Dyke PARUNAK,Practical and Industrial Applications of Agent-based Systems,http://www.cs.umbc.edu/agents,1999
[33]D.M.Lane,A.G.Mcfadzean,Distributed problem solving and real-time mechanisms in robot architectures[J],Engineering Application Intelligence,1994,7(2),105-117
[34]M.Ljunberg and A,Jucas.The OASIS air traffic management system[C],Proceeding of the 2nd Pacific Rim International Conference on AI,Seoul,Korea,1992
[35]B.Burmeister,A.Haddadi,G.Matylis,Applications of multi-agent systems in traffic and transportation[J],IEEE Trans on Software Engineering,1997,144(1),51-60
[36]C.V.Goldman,J.S.Rosenschein,Mutual supervised learning in multiagent systems[A],Proc of IJCAI'95 Workshop[C],Berlin,1996,85-96
[37]N.V.Findler,Distributed control of collaborating and learning expert systems for street traffic signals[A].IFA C Distributed Intelligence Systems[C],Pergam on Press,1991,125-130
[38]G.Adorni,A.Poggl,Route guidance as a just-in-time multi-agent task[J],Applied Artificial Intelligence 1996,10(2),95-120
[39]K.H.Funk,J.H.Lind,Agent-based pilot-vehicle inter-faces:Concept and prototype[J],IEEE Trans on Systems,Man and Cybernatics,1992,22(6),1309-1322
[40]G.Vernazza,R.Zunino.A,distributed intelligence methodology for railway traffic control[J],IEEE Trans on Vehicular Technology,1990,39(3),263-270
[41]Y.Jin,T Koyama,Z.J.Zhang,The marine traffic control systems as a distributed problem solving network[A],Proc IEEE Int Conf on Systems,Man and Cybernetics[C],USA
[42]Xiaoming Liu,Fei-yue Wang.Study of Intersection Traffic Flow Control On the Basis of Agents[J],Journal of Sytem Simulation,2004,16(4),853-855
[43]朱茵,唐祯敏,钱大琳,基于多智能体技术的交通管理系统的研究[J],中国公路学报,2002,15(3),80-83
[44]Gorry G A.,Scott Morton M S,A framework management information system,Sloan Management Review,1971,13(1),55-70
[45]陈文伟,决策支持系统教程[M],北京:清华大学出版社,2000
[46]Bonczek R H,et al.Foundations of Decision Support Systems[M],New York,Academic Press,1981
[47]Scott M S,Management Decision System,Computer Based Support for Decision Division of Research[R],Harvard University,Cambridge,Massachusetts,MA,1997
[48]冯仁德,孙在国编著.现代企业决策支持系统[M],成都:西南财经大学出版社,2003
[49]Sen A,Decision Support Systems:A Activity-oriented Design[J],Journal of Information Science,1983,7,23-30
[50]钱学森,一个科学新领域--开放的复杂巨系统及其方法论[M],自然杂志,1990,13(1),3-10
[51]于景元,钱学森关于开放的复杂巨系统的研究[J],系统工程理论与实践,1992,12(5),8-12
[52]刘大有,杨鲲,陈建中,Agent研究现状与发展趋势[J],软件学报,2000,11(3),315-321
[53]Shoham Y,Agent oriented programming[J],Artificial Intelligence,1993,60,51-92
[54]Michael W,Nicholas R J,Intelligent agents:theory and practice[J],Knowledge Engineering Review,1995,10(2),115-152
[55]李燕,樊玮,基于Agent的用户界面模型研究[J],计算机工程与应用,2002,38(8),26-28
[56]刘立,胡立臣,懂玉等,基于Agent的决策支持系统[J],计算机工程与科学,2002,24(3),70-74
[57]李英,刘豹,预测支持系统中的人机界面Agent及其机器学习[J],系统工程理论与实践,2000,12(6),459-467
[58]梁义芝,刘云飞,基于Multi-Agent技术的决策支持系统[J],计算机科学,1998,(8),1319
[59]刘金琨,王树青,基于Agent技术的人工智能决策支持系统研究[J],系统工程理论与实践,2000(2),5-20
[60]张宏斌,唐华俊,姜丽华等.基于Agent的智能决策支持系统开发与应用[J],计算机应用与工程,2004,25(40),190-192
[61]毛海军,唐焕文,基于Agent的智能决策支持系统研究[J],计算机工程与应用,2001,15(37),12-13
[62]李祯,倪天倪,基于Agent的智能决策支持系统模型的研究及应用[J],计算机工程,2002,5,5(28),120-122
[63]樊玮,陈增强,袁著扯,基于Agent的智能决策支持系统模型[J],计算机科学,2004,1.31(1),122-125
[64]邓贵仕,王众托,新一代决策支持系统(DSS)构成与软集成方法[J],大连理工大学学报,2000,9,613-617
[65]李秀,应维云等,第四方物流的体系结构和运作模式研究[J],计算机集成制造系统,2004,10,1233-1237
[66]况漠,物流决策支持系统开发研究[J],四川工业学院学报,2003,2(22),67-70
[67]王志远,耿祥义,决策支持物流管理信息系统的设计与开发[J],大连铁道学院学报,2004,2(25),46-49
[68]郝鹏飞,何世伟,陈艳青,基于Mufti-Agent的物流调度决策支持系统[J],物流科技,2006,1(29),64-66
[69]周宏,物流战略联盟的组建及运营管理[J],物流科技,2006,1(29),163-165
[70]Konstantinos G.Zografos,Konstantinos N.Androutsopoulos,George M.Vasilakis,A real-time decision support system for roadway network incident response logistics[J],Transportation Research Part C:Emerging Technologies,2002,10,1-18
[71]安实等,城市智能交通管理技术与应用[M],北京:科学出版社,2005
[72]刘志超,城市交通拥挤管理和控制的策略研究[D],南京,东南大学硕士论文,2002,9
[73]Kashani H R,Sardis G N,Intelligent control for urban traffic systems[J],Automatic,1983,19(2),191-197
[74]Krause B,Intelligent highway by fuzzy logic,congestion detection and traffic control on multi-lane roads with variable road signs[C],Proc of the 5th IEEE International conf,On Fuzzy systems,1996,3,1832-1837
[75]黄铮,邹群,邹国平,基于Multi-Agent的高速公路集成交通控制系统[J], 交通运输工程学报,2005,5(1),116-119
[76]Cai,Chang-Qing,Yang,Zhao-Sheng Study on urban traffic management based on multi-agent systems[C],Proceedings of the 2007 International Conference on Machine Learning and Cybernetics,ICMLC 2007,Aug 19-22 2007,Hong Kong,China,25-29
[77]仝秋红,郑永春,智能交通控制中的黑板系统结构[J],现代电子技术,2000(11),76-78
[78]徐丽群,基于多智能体技术的出行诱导管理系统研究[J],东南大学学报,2004,34(6),814-818
[79]保丽霞,基于信息集成的城市交通流诱导与交通控制协同的关键理论及技术研究[D],长春,吉林大学,2006,6
[80]镇海燕,王炜,基于GIS的城市交通安全管理研究[J],交通与计算机,2002,20(3),6-9
[81]李伯楠,沈志纲,“数字地球”体系下GIS-T技术的应用[J],信息技术,2000(1),30-33
[82]杨晓光,中国交通信息化与发展智能交通运输系统问题研究[J],交通运输系统工程与信息,2001年03期,25-35
[83]周剑峰,城市交通共用信息平台数据处理技术研究[D],长春,吉林大学,2005,6
[84]吉林大学交通学院,《深圳市智能交通系统调研报告》[R],2003.10,85-90
[85]九五国家重点科技攻关项目《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,2003.1.195-206
[86]卫金茂,黄道,知识发现中缺损数据的处理[J],华东理工大学学报,2000,25(5):521-524
[87]Jiawe Han,Micheline Kamber,Data Mining-concepts and techniques[M],Morgan Kaufman Publishers,2001,98-103
[88]姜桂艳,道路交通状态判别技术及应用[M],人民交通出版社,2004,9,第1版:103-105
[89]邵佩英,分布式数据库系统及其应用[M],北京:科学出版社,2000年6月
[90]Cai,Chang-Qing,Yang Zhao-Sheng,Xin Chen,Study on Application of D-S Evidential Theory in Traffic Volume Information collection[C],12th World Congress on ITS,2005.11
[91]俞峥,李建永,多智能体在交通控制系统中的应用[J],交通运输工程学报,Vol,No.1,2001,PP55-57
[92]Hai Yang,Traffic Assignment and Signal Control in Saturated Road Networks[J],Transpn.Res.-A.1995,Vol.29A,No.2.125-139,
[93]Halim Ceylan,Micheal G.H.Bell,Traffic signal timing optimisation based on genetic algorithm approach,including drivers' routing[J],Transportation Research Part B 38(2004),329-342
[94]荆便顺,道路交通控制工程[M],北京,人民交通出版社,1995,第1版,78-84.
[95]孙燕,服务于交通流诱导系统的动态路径选择模型与方法研究[D],东南大学,2003,6:70-72.
[96]马万经,杨晓光,信号控制交叉口实时延误计算与仿真研究[J],交通与计算机,2006,24(5):1-4
[97]万绪军,陆化普,线控系统中相位差优化模型的研究[J],中国公路学报,2001,14(2),99-102
[98]陈世福,陈兆乾,人工智能与知识工程[M],南京,南京大学出版社,1997
[99]Li Ning,Gao Yang,Lu Xin,Chen Shi-Fu,A learning agent based on reinforcement learning,Computer Research and Development,2001,38(9):1051-1056
[100]高阳,陈世福,陆鑫,强化学习研究综述[J],自动化学报,2004,1:Vol30,No1
[101]Singh S,Agents and reinforcement learning,San Mateo,CA,USA:Miller Freeman Publish Inc,1997
[102]Kaelbling L.P,Littman M.L,Moore A W,Reinforcement learning:a survey[J],Journal of Artificial Intelligence Research,1996,4:237-285
[103]Weiss G,Dillenbourg P,What is multi in multi-Agent learning? In:P Dillenbourg,eds,Collaborative learning[J],Cognitive and computational approaches,Amsterdam,Pergamon Press,1998,64-80
[104]M Bowling,Convergence and no-regret in multiAgent learning[M],In:Advances in Naural Information Processing Systems,2004
[105]王小平,曹立明,遗传算法理论、应用与软件实现[M],西安,西安交 通大学出版社,2002
[106]Goldberg D E,Genetic Algorithm in Search,Optimization,and Machine Learning[M],Addison-Wesley Publishing Co.,Reading,Massachusetts,1989
[107]玄光男,称润伟,遗传算法与工程设计[M],北京,科学出版社,2000
[108]杨煜普,欧海涛,基于再励学习与遗传算法的交通信号自组织控制[J],自动化学报,Vol.28,No.4,2002,PP564-568
[109]Joseph Giarratano,Gary Riley著,印鉴,刘星成,汤庸译.专家系统原理与编程[M],机械工业出版社,40-63.
[110]José Cuena,Josefa Hernández,Martín Molina,Knowledge-based models for adaptive traffic management systems[J],Transportation Research Part C:Emerging Technologies,Volume 3,Issue 5,October 1995,311-337
[111]Sascha Ossowski,Josefa Z.Hernández,María-Victoria Belmonte,Decision support for traffic management based on organisational and communicative multiagent abstractions[J],Transportation Research Part C:Emerging Technologies,Volume 13,Issue 4,August 2005,272-298
[112]Littman M L,Markov games as a framework for multi agent reinforcement learning[A],Proceedings of the Eleventh International Conference on Machine Learning[C],CA,1994,157-163
[113]C.J.C.H.Watkins,P.Dayan,Q-learning[J],Machine Learning,8(3-4),May,1992,279-292
[114]施锡锉,博弈论[M],上海,上海财经大学出版社,2000,5-17
[115]肖条军,博弈论及其应用[M],上海,上海三联书店,2004,1-173
[116]Flavien Balbo,Suzanne Pinson,Dynamic modeling of a disturbance in a multi-agent system for traffic regulation[J],Decision Support Systems,Volume 41,Issue 1,November 2005,131-146
[117]刘金琨,王树青,多智能体控制系统的设计与实现[J],控制理论与应用.Vol.16,No.4 Aug,1999,580-582.
[2]段里仁,道路交通自动控制[M],北京,中国人民公安大学出版社,1991
[3]王笑京,智能交通系统研发历程与动态述评[J],城市交通,Vol.6 No.1 Jan,2008-6-12
[4]杨兆升,论智能运输系统,中国公路学报[J],1995年第8卷第4期,102-110
[5]王笑京,智能运输系统体系框架研究,交通与计算机[J],2001年第4期,4-6
[6]黄卫,陈里得,智能运输系统(ITS)概论[M],北京,人民交通出版社,2001
[7]郭治安,协同学入门[M],成都,四川人民出版社,1988,第1版,23-30
[8]H.哈肯著[德],信息与自组织[M],成都,四川教育出版社,1988,第1版
[9]陈昕,基于协同学的城市交通控制与诱导系统协同的理论与方法研究[D],吉林大学,2006
[10]陈小锋,城市交通信号动态优化控制技术研究[D],西北工业大学,2003
[11]尹宏宾,徐建闽,道路交通控制技术,广州,华南理工大学出版社,2000
[12]Allsop R.E,Some possibilities for using traffic control to influence t rip distribution and route choice[A],Proceeding 6th International Symposium Transportation and Traffic Theory[C],Sydney,1974
[13]Tan H.N,S.Gershwin,M.Athans.Hybrid optimization in urban traffic networks[R].Research and special programs administration[C],U.S Department of Transportation,1979
[14]刘虹秀,魏武,彭驰,基于Multi-agent的城市交通控制与诱导协调研究[J],公路与汽运,2007(5),42-44
[15]M.J.Smith and T.Van Vuren,Traffic equilibrium with responsive traffic control[J],Transportation Science,Vol.27,No.2,1993,118-132.
[16]Y.Sheffi.and W.B.Powell,Optimal signal settings over transportation networks[J],Journal of Transportation Engineering,Vol.109,No.6,1983,824-839.
[17]M.J.Smith,Traffic signals in assignment[J],Transportation Research,Vol.19B,No.2,1985,155-160
[18]Gartner,Nathan H,Stamatiadis,Integration of dynamic traffic assignment with real-time traffic adaptive control system[J],Transportation Research Record,1998,150-156
[19]M.J.Smith,T.Van Vuren,B.G.Heydecker and D.Van Vliet,The interactions between signal control policies and route choice[C],Proceedings of the Tenth International Symposium on Transportation and Traffic Theory,1987,319-338
[20]徐岩宇,冯蔚东,贺国光,VRGS与交通控制系统的一体化研究,公路交通科技,1997,9,24-2
[21]徐立群,城市交通流诱导与控制一体化理论和模型研究[D],吉林大学,2000,12-17
[22]李瑞敏,史其信,基于多智能体系统的城市交通控制与诱导集成化研究[J],公路交通科技,2004年05期,15-19
[23]魏连雨,李巧茹,城市交通系统的可协调发展[J],河北工业大学学报,1998年03期,55-62
[24]Wooldridge,M.,Muller,J.M.,and Tambe,M.Agents,theories,architectures and languages[J],(ATAL-95)Springer Verlag Lecture Notes in Artificial Intelligence(LNAI 1037)
[25]Russell,S.and Norvig.p.Artificial Intelligence[J],A Modem Approach Prentice Hall,New Jersey,1995
[26]孙晋文,基于Agent的智能交通控制策略与可视化动态仿真研究[D],中国农业大学,2001,5,10-25
[27]Kadar B,Monnstori L,Szelke E,An object-oriented framework for developing distributed manufacturing architecture[J],Journal of Intelligent Manufacturing,1998,9(2),173-179
[28]董宏斌,石纯一,移动Agent技术研究[J],计算机科学,2000,27(4),35-38
[29]李海刚,吴启迪多Agent系统综述[J],同济大学学报,2003,Vol.31(6),728-732
[30]贺建立,多智能体系统的开发[D],合肥,安徽大学,2004
[31]Nicholas R.Jennings,Michael J.Wooldinge,Agent Technology-Foundation[J],Application and Markets,Springer,1997
[32]H.Van Dyke PARUNAK,Practical and Industrial Applications of Agent-based Systems,http://www.cs.umbc.edu/agents,1999
[33]D.M.Lane,A.G.Mcfadzean,Distributed problem solving and real-time mechanisms in robot architectures[J],Engineering Application Intelligence,1994,7(2),105-117
[34]M.Ljunberg and A,Jucas.The OASIS air traffic management system[C],Proceeding of the 2nd Pacific Rim International Conference on AI,Seoul,Korea,1992
[35]B.Burmeister,A.Haddadi,G.Matylis,Applications of multi-agent systems in traffic and transportation[J],IEEE Trans on Software Engineering,1997,144(1),51-60
[36]C.V.Goldman,J.S.Rosenschein,Mutual supervised learning in multiagent systems[A],Proc of IJCAI'95 Workshop[C],Berlin,1996,85-96
[37]N.V.Findler,Distributed control of collaborating and learning expert systems for street traffic signals[A].IFA C Distributed Intelligence Systems[C],Pergam on Press,1991,125-130
[38]G.Adorni,A.Poggl,Route guidance as a just-in-time multi-agent task[J],Applied Artificial Intelligence 1996,10(2),95-120
[39]K.H.Funk,J.H.Lind,Agent-based pilot-vehicle inter-faces:Concept and prototype[J],IEEE Trans on Systems,Man and Cybernatics,1992,22(6),1309-1322
[40]G.Vernazza,R.Zunino.A,distributed intelligence methodology for railway traffic control[J],IEEE Trans on Vehicular Technology,1990,39(3),263-270
[41]Y.Jin,T Koyama,Z.J.Zhang,The marine traffic control systems as a distributed problem solving network[A],Proc IEEE Int Conf on Systems,Man and Cybernetics[C],USA
[42]Xiaoming Liu,Fei-yue Wang.Study of Intersection Traffic Flow Control On the Basis of Agents[J],Journal of Sytem Simulation,2004,16(4),853-855
[43]朱茵,唐祯敏,钱大琳,基于多智能体技术的交通管理系统的研究[J],中国公路学报,2002,15(3),80-83
[44]Gorry G A.,Scott Morton M S,A framework management information system,Sloan Management Review,1971,13(1),55-70
[45]陈文伟,决策支持系统教程[M],北京:清华大学出版社,2000
[46]Bonczek R H,et al.Foundations of Decision Support Systems[M],New York,Academic Press,1981
[47]Scott M S,Management Decision System,Computer Based Support for Decision Division of Research[R],Harvard University,Cambridge,Massachusetts,MA,1997
[48]冯仁德,孙在国编著.现代企业决策支持系统[M],成都:西南财经大学出版社,2003
[49]Sen A,Decision Support Systems:A Activity-oriented Design[J],Journal of Information Science,1983,7,23-30
[50]钱学森,一个科学新领域--开放的复杂巨系统及其方法论[M],自然杂志,1990,13(1),3-10
[51]于景元,钱学森关于开放的复杂巨系统的研究[J],系统工程理论与实践,1992,12(5),8-12
[52]刘大有,杨鲲,陈建中,Agent研究现状与发展趋势[J],软件学报,2000,11(3),315-321
[53]Shoham Y,Agent oriented programming[J],Artificial Intelligence,1993,60,51-92
[54]Michael W,Nicholas R J,Intelligent agents:theory and practice[J],Knowledge Engineering Review,1995,10(2),115-152
[55]李燕,樊玮,基于Agent的用户界面模型研究[J],计算机工程与应用,2002,38(8),26-28
[56]刘立,胡立臣,懂玉等,基于Agent的决策支持系统[J],计算机工程与科学,2002,24(3),70-74
[57]李英,刘豹,预测支持系统中的人机界面Agent及其机器学习[J],系统工程理论与实践,2000,12(6),459-467
[58]梁义芝,刘云飞,基于Multi-Agent技术的决策支持系统[J],计算机科学,1998,(8),1319
[59]刘金琨,王树青,基于Agent技术的人工智能决策支持系统研究[J],系统工程理论与实践,2000(2),5-20
[60]张宏斌,唐华俊,姜丽华等.基于Agent的智能决策支持系统开发与应用[J],计算机应用与工程,2004,25(40),190-192
[61]毛海军,唐焕文,基于Agent的智能决策支持系统研究[J],计算机工程与应用,2001,15(37),12-13
[62]李祯,倪天倪,基于Agent的智能决策支持系统模型的研究及应用[J],计算机工程,2002,5,5(28),120-122
[63]樊玮,陈增强,袁著扯,基于Agent的智能决策支持系统模型[J],计算机科学,2004,1.31(1),122-125
[64]邓贵仕,王众托,新一代决策支持系统(DSS)构成与软集成方法[J],大连理工大学学报,2000,9,613-617
[65]李秀,应维云等,第四方物流的体系结构和运作模式研究[J],计算机集成制造系统,2004,10,1233-1237
[66]况漠,物流决策支持系统开发研究[J],四川工业学院学报,2003,2(22),67-70
[67]王志远,耿祥义,决策支持物流管理信息系统的设计与开发[J],大连铁道学院学报,2004,2(25),46-49
[68]郝鹏飞,何世伟,陈艳青,基于Mufti-Agent的物流调度决策支持系统[J],物流科技,2006,1(29),64-66
[69]周宏,物流战略联盟的组建及运营管理[J],物流科技,2006,1(29),163-165
[70]Konstantinos G.Zografos,Konstantinos N.Androutsopoulos,George M.Vasilakis,A real-time decision support system for roadway network incident response logistics[J],Transportation Research Part C:Emerging Technologies,2002,10,1-18
[71]安实等,城市智能交通管理技术与应用[M],北京:科学出版社,2005
[72]刘志超,城市交通拥挤管理和控制的策略研究[D],南京,东南大学硕士论文,2002,9
[73]Kashani H R,Sardis G N,Intelligent control for urban traffic systems[J],Automatic,1983,19(2),191-197
[74]Krause B,Intelligent highway by fuzzy logic,congestion detection and traffic control on multi-lane roads with variable road signs[C],Proc of the 5th IEEE International conf,On Fuzzy systems,1996,3,1832-1837
[75]黄铮,邹群,邹国平,基于Multi-Agent的高速公路集成交通控制系统[J], 交通运输工程学报,2005,5(1),116-119
[76]Cai,Chang-Qing,Yang,Zhao-Sheng Study on urban traffic management based on multi-agent systems[C],Proceedings of the 2007 International Conference on Machine Learning and Cybernetics,ICMLC 2007,Aug 19-22 2007,Hong Kong,China,25-29
[77]仝秋红,郑永春,智能交通控制中的黑板系统结构[J],现代电子技术,2000(11),76-78
[78]徐丽群,基于多智能体技术的出行诱导管理系统研究[J],东南大学学报,2004,34(6),814-818
[79]保丽霞,基于信息集成的城市交通流诱导与交通控制协同的关键理论及技术研究[D],长春,吉林大学,2006,6
[80]镇海燕,王炜,基于GIS的城市交通安全管理研究[J],交通与计算机,2002,20(3),6-9
[81]李伯楠,沈志纲,“数字地球”体系下GIS-T技术的应用[J],信息技术,2000(1),30-33
[82]杨晓光,中国交通信息化与发展智能交通运输系统问题研究[J],交通运输系统工程与信息,2001年03期,25-35
[83]周剑峰,城市交通共用信息平台数据处理技术研究[D],长春,吉林大学,2005,6
[84]吉林大学交通学院,《深圳市智能交通系统调研报告》[R],2003.10,85-90
[85]九五国家重点科技攻关项目《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,2003.1.195-206
[86]卫金茂,黄道,知识发现中缺损数据的处理[J],华东理工大学学报,2000,25(5):521-524
[87]Jiawe Han,Micheline Kamber,Data Mining-concepts and techniques[M],Morgan Kaufman Publishers,2001,98-103
[88]姜桂艳,道路交通状态判别技术及应用[M],人民交通出版社,2004,9,第1版:103-105
[89]邵佩英,分布式数据库系统及其应用[M],北京:科学出版社,2000年6月
[90]Cai,Chang-Qing,Yang Zhao-Sheng,Xin Chen,Study on Application of D-S Evidential Theory in Traffic Volume Information collection[C],12th World Congress on ITS,2005.11
[91]俞峥,李建永,多智能体在交通控制系统中的应用[J],交通运输工程学报,Vol,No.1,2001,PP55-57
[92]Hai Yang,Traffic Assignment and Signal Control in Saturated Road Networks[J],Transpn.Res.-A.1995,Vol.29A,No.2.125-139,
[93]Halim Ceylan,Micheal G.H.Bell,Traffic signal timing optimisation based on genetic algorithm approach,including drivers' routing[J],Transportation Research Part B 38(2004),329-342
[94]荆便顺,道路交通控制工程[M],北京,人民交通出版社,1995,第1版,78-84.
[95]孙燕,服务于交通流诱导系统的动态路径选择模型与方法研究[D],东南大学,2003,6:70-72.
[96]马万经,杨晓光,信号控制交叉口实时延误计算与仿真研究[J],交通与计算机,2006,24(5):1-4
[97]万绪军,陆化普,线控系统中相位差优化模型的研究[J],中国公路学报,2001,14(2),99-102
[98]陈世福,陈兆乾,人工智能与知识工程[M],南京,南京大学出版社,1997
[99]Li Ning,Gao Yang,Lu Xin,Chen Shi-Fu,A learning agent based on reinforcement learning,Computer Research and Development,2001,38(9):1051-1056
[100]高阳,陈世福,陆鑫,强化学习研究综述[J],自动化学报,2004,1:Vol30,No1
[101]Singh S,Agents and reinforcement learning,San Mateo,CA,USA:Miller Freeman Publish Inc,1997
[102]Kaelbling L.P,Littman M.L,Moore A W,Reinforcement learning:a survey[J],Journal of Artificial Intelligence Research,1996,4:237-285
[103]Weiss G,Dillenbourg P,What is multi in multi-Agent learning? In:P Dillenbourg,eds,Collaborative learning[J],Cognitive and computational approaches,Amsterdam,Pergamon Press,1998,64-80
[104]M Bowling,Convergence and no-regret in multiAgent learning[M],In:Advances in Naural Information Processing Systems,2004
[105]王小平,曹立明,遗传算法理论、应用与软件实现[M],西安,西安交 通大学出版社,2002
[106]Goldberg D E,Genetic Algorithm in Search,Optimization,and Machine Learning[M],Addison-Wesley Publishing Co.,Reading,Massachusetts,1989
[107]玄光男,称润伟,遗传算法与工程设计[M],北京,科学出版社,2000
[108]杨煜普,欧海涛,基于再励学习与遗传算法的交通信号自组织控制[J],自动化学报,Vol.28,No.4,2002,PP564-568
[109]Joseph Giarratano,Gary Riley著,印鉴,刘星成,汤庸译.专家系统原理与编程[M],机械工业出版社,40-63.
[110]José Cuena,Josefa Hernández,Martín Molina,Knowledge-based models for adaptive traffic management systems[J],Transportation Research Part C:Emerging Technologies,Volume 3,Issue 5,October 1995,311-337
[111]Sascha Ossowski,Josefa Z.Hernández,María-Victoria Belmonte,Decision support for traffic management based on organisational and communicative multiagent abstractions[J],Transportation Research Part C:Emerging Technologies,Volume 13,Issue 4,August 2005,272-298
[112]Littman M L,Markov games as a framework for multi agent reinforcement learning[A],Proceedings of the Eleventh International Conference on Machine Learning[C],CA,1994,157-163
[113]C.J.C.H.Watkins,P.Dayan,Q-learning[J],Machine Learning,8(3-4),May,1992,279-292
[114]施锡锉,博弈论[M],上海,上海财经大学出版社,2000,5-17
[115]肖条军,博弈论及其应用[M],上海,上海三联书店,2004,1-173
[116]Flavien Balbo,Suzanne Pinson,Dynamic modeling of a disturbance in a multi-agent system for traffic regulation[J],Decision Support Systems,Volume 41,Issue 1,November 2005,131-146
[117]刘金琨,王树青,多智能体控制系统的设计与实现[J],控制理论与应用.Vol.16,No.4 Aug,1999,580-582.