基于车辆延误理论的多态交通流信号控制研究
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摘要
信号交叉口交通流受驾驶技术、交通组成、信号控制、交通管理、交通环境等诸多因素影响，导致交通流在时间和空间内通常具有多态特征。现有的信号控制方法因未能充分考虑交叉口交通流分布特征，造成交叉口成为道路网络交通延误增长的主要区域，影响整个路网的运营效益。为了解决现有的控制方法不能适应多态交通条件下交叉口信号控制问题，本文依托国家自然科学基金项目《混合交通环境下行驶工况的仿真及控制策略研究》（No.70771036）和《平面交叉口交通流片段仿真及管理措施实证研究》（No.71071044）课题，通过分析多态交通流特性，研究交叉口通行能力模型及交通流预测方法，进一步提出多态交通条件下交叉口信号控制方法及延误分析理论。本文研究工作主要表现在以下几个方面：
（1）通过分析多态交通流时间和空间分布多态特性，从人、车、路三个方面分析影响交通流多态特性影响因素；提出了多态交通流与相对稳态交通流的界定方法，标定了车头时距、交通流量两项统计指标的界限值。
（2）结合稠密特性函数可以任意精度逼近任何分布特征数据的特点，引入稠密性无限混合Gamma分布函数，提出了基于极大似然算法的交通流车头时距拟合方法，并通过实际调查数据证明了拟合方法的精确性。
（3）基于可穿越间隙理论，采用无限混合Erlang稠密性分布函数，分析了次要道路交通流可穿越主路交通流车头空隙的概率，建立了无信号交叉口通行能力模型。
（4）基于小波分析理论，采用RBF神经网络和Markov链预测方法，建立了短时多态交通流组合预测模型。
（5）根据多态交通流在日交通流量统计序列的变化特性，通过小波分析对日交通流量序列信号进行降噪处理，结合F.Webster-B.Cobber信号配时理论提出了多态交通特性信号交叉口控制时段划分方法。
（6）根据交叉口多态交通流分布特征和时空资源条件，分定时控制、感应控制和预测控制三种类型提出了信号控制方法：
①针对非对称多态交通流交叉口，根据交通流量通过调整交叉口空间资源，利用相位优化技术提出了交叉口信号伴随相位定时控制方法。
②针对交通流量多变、进口车道较少的小型交叉口，基于交叉口车辆排队理论建立了交叉口每相位主流向通行综合效益PI函数，依据交叉口流向冲突关系，提出了相位自组织优化的交叉口感应信号控制方法。
③针对进口车道较多的多态交通交叉口，通过在交叉口进口设置多功能进口车道，依据排队理论和多态交通流预测结果，建立以交叉口总运行延误最小为优化目标的多功能车道流向选取模型。在相位有效绿灯时间满足最大排队长度通行条件下，滚动优化每相位并逐步实施，提出了设置多功能车道的交叉口时空资源动态优化的预测控制方法。
（7）基于随机过程理论，提出了多态交通流量稠密特性函数概率统计方法；结合定时控制、感应控制与预测控制不同策略下信号相位变化状态，建立了多态交通流信号控制延误模型。
（8）使用Vissim及相关软件对多态交通流信号控制方法和延误分析理论进行仿真，证明了控制方法的有效性和延误分析理论的精确性。
The traffic flow in signalized intersection always embodies multi-state characteristic in terms oftime and space, influenced by a variety of factors, such as driving skill, traffic composition, signalcontrol, traffic management, traffic environment, etc. The existing signal control methods, whichhaven’t fully taken the distribution characteristic of traffic flow in intersection into account，causeintersections becoming the main areas of delay increasing in the road network and affect theoperation benefit of entire network. In order to solve the problem that the existing control methodcould not adapt to the intersection signal control in multi-state traffic conditions, the characteristic ofmulti-state traffic flow has been analyzed, traffic flow prediction method and capacity model ofintersection have been established, supported by the National Natural Science Foundation projects“Study on simulation and control strategy of driving cycle in mixed traffic environment”(No.70771036) and “The simulation of traffic flow segment and management measures empiricalresearch in plane intersection”(No.71071044), Furthermore, signal control methods and delayanalysis theory have been proposed. The research has been basically reflected in the followingaspects:
(1) By analyzing the multi-state characteristic of traffic flow in terms of time and space, theinfluences have been analyzed from three aspects: driver, vehicle and highway. Decision methods ofmulti-state traffic flow and relatively steady-state one have been put forward, and the thresholdvalues of headway and traffic flow have been calibrated.
(2) Combined with the feature that denseness function could approximate any distributioncharacteristic data with arbitrary degree of accuracy, dense infinite mixture Gamma distributionfunction has been introduced to establish the headway fitting method based on the maximumlikelihood algorithm, and then the accuracy has been verified by the investigation results.
(3) Based on gap acceptance theory, the capacity model of unsignalized intersection has beenestablished, from analyzing the probability that minor road traffic flow passes through the headspaces of major road one, by the infinite mixture Erlang denseness distribution function.
(4) Based on wavelet analysis theory, the combination forecasting model of short-timemulti-state traffic flow has been established with RBF neural network and Markov chains predictionmethods.
(5) According to the variation feature of multi-state traffic flow in the daily traffic statisticalseries, denoise process of daily traffic series signal has been implemented via wavelet analyzing. Combined with the signal timing theory by F.Webster-B.Cobber, the control time division method ofmulti-state signalized intersection has been proposed.
(6) According to the distribution characteristic of multi-state traffic flow and time-spaceresources in intersections, control methods have been established in three types: timing control,inductive control and predictive control.
①For unparallel multi-state traffic flow intersections, the adjoint phase timing control methodhas been set up after adjusting the space resources and using phase optimization technique.
②On account of small intersections with fewer entrance lanes and changeable traffic flow, thesynthesis benefit PI function with main traffic permitting in each phase has been established basedon the queuing theory. According to the conflict relationship of each direction in intersection,inductive signal control method with phase self-organization optimizing has been proposed.
③With regard to multi-state traffic intersection where there are many entrance lanes,multifunctional lanes should be installed. According to the queuing theory and predicting outcome ofmulti-state traffic flow, the selecting model of multifunctional lanes direction has been presented,which takes the minimum total operational delay as optimization objective. Under the condition thateffective green time could satisfy maximum queue length passing through, every phase has beenrolling optimized. And in this case, the predictive control method on space resources dynamicoptimization in intersection installing multifunctional lanes has been proposed.
(7) The denseness function probabilistic statistics of multi-state traffic flow has beenestablished on the foundation of random process theory. Combined with the phase changes ofdifferent strategies, such as timing control, inductive control and predictive control, the signalcontrol delay model of multi-state traffic flow has been proposed.
(8) The effectiveness of control strategy of multi-state traffic flow and the accuracy of delayanalysis theory have been confirmed by the simulation using VISSIM and related software.
（1）通过分析多态交通流时间和空间分布多态特性，从人、车、路三个方面分析影响交通流多态特性影响因素；提出了多态交通流与相对稳态交通流的界定方法，标定了车头时距、交通流量两项统计指标的界限值。
（2）结合稠密特性函数可以任意精度逼近任何分布特征数据的特点，引入稠密性无限混合Gamma分布函数，提出了基于极大似然算法的交通流车头时距拟合方法，并通过实际调查数据证明了拟合方法的精确性。
（3）基于可穿越间隙理论，采用无限混合Erlang稠密性分布函数，分析了次要道路交通流可穿越主路交通流车头空隙的概率，建立了无信号交叉口通行能力模型。
（4）基于小波分析理论，采用RBF神经网络和Markov链预测方法，建立了短时多态交通流组合预测模型。
（5）根据多态交通流在日交通流量统计序列的变化特性，通过小波分析对日交通流量序列信号进行降噪处理，结合F.Webster-B.Cobber信号配时理论提出了多态交通特性信号交叉口控制时段划分方法。
（6）根据交叉口多态交通流分布特征和时空资源条件，分定时控制、感应控制和预测控制三种类型提出了信号控制方法：
①针对非对称多态交通流交叉口，根据交通流量通过调整交叉口空间资源，利用相位优化技术提出了交叉口信号伴随相位定时控制方法。
②针对交通流量多变、进口车道较少的小型交叉口，基于交叉口车辆排队理论建立了交叉口每相位主流向通行综合效益PI函数，依据交叉口流向冲突关系，提出了相位自组织优化的交叉口感应信号控制方法。
③针对进口车道较多的多态交通交叉口，通过在交叉口进口设置多功能进口车道，依据排队理论和多态交通流预测结果，建立以交叉口总运行延误最小为优化目标的多功能车道流向选取模型。在相位有效绿灯时间满足最大排队长度通行条件下，滚动优化每相位并逐步实施，提出了设置多功能车道的交叉口时空资源动态优化的预测控制方法。
（7）基于随机过程理论，提出了多态交通流量稠密特性函数概率统计方法；结合定时控制、感应控制与预测控制不同策略下信号相位变化状态，建立了多态交通流信号控制延误模型。
（8）使用Vissim及相关软件对多态交通流信号控制方法和延误分析理论进行仿真，证明了控制方法的有效性和延误分析理论的精确性。
The traffic flow in signalized intersection always embodies multi-state characteristic in terms oftime and space, influenced by a variety of factors, such as driving skill, traffic composition, signalcontrol, traffic management, traffic environment, etc. The existing signal control methods, whichhaven’t fully taken the distribution characteristic of traffic flow in intersection into account，causeintersections becoming the main areas of delay increasing in the road network and affect theoperation benefit of entire network. In order to solve the problem that the existing control methodcould not adapt to the intersection signal control in multi-state traffic conditions, the characteristic ofmulti-state traffic flow has been analyzed, traffic flow prediction method and capacity model ofintersection have been established, supported by the National Natural Science Foundation projects“Study on simulation and control strategy of driving cycle in mixed traffic environment”(No.70771036) and “The simulation of traffic flow segment and management measures empiricalresearch in plane intersection”(No.71071044), Furthermore, signal control methods and delayanalysis theory have been proposed. The research has been basically reflected in the followingaspects:
(1) By analyzing the multi-state characteristic of traffic flow in terms of time and space, theinfluences have been analyzed from three aspects: driver, vehicle and highway. Decision methods ofmulti-state traffic flow and relatively steady-state one have been put forward, and the thresholdvalues of headway and traffic flow have been calibrated.
(2) Combined with the feature that denseness function could approximate any distributioncharacteristic data with arbitrary degree of accuracy, dense infinite mixture Gamma distributionfunction has been introduced to establish the headway fitting method based on the maximumlikelihood algorithm, and then the accuracy has been verified by the investigation results.
(3) Based on gap acceptance theory, the capacity model of unsignalized intersection has beenestablished, from analyzing the probability that minor road traffic flow passes through the headspaces of major road one, by the infinite mixture Erlang denseness distribution function.
(4) Based on wavelet analysis theory, the combination forecasting model of short-timemulti-state traffic flow has been established with RBF neural network and Markov chains predictionmethods.
(5) According to the variation feature of multi-state traffic flow in the daily traffic statisticalseries, denoise process of daily traffic series signal has been implemented via wavelet analyzing. Combined with the signal timing theory by F.Webster-B.Cobber, the control time division method ofmulti-state signalized intersection has been proposed.
(6) According to the distribution characteristic of multi-state traffic flow and time-spaceresources in intersections, control methods have been established in three types: timing control,inductive control and predictive control.
①For unparallel multi-state traffic flow intersections, the adjoint phase timing control methodhas been set up after adjusting the space resources and using phase optimization technique.
②On account of small intersections with fewer entrance lanes and changeable traffic flow, thesynthesis benefit PI function with main traffic permitting in each phase has been established basedon the queuing theory. According to the conflict relationship of each direction in intersection,inductive signal control method with phase self-organization optimizing has been proposed.
③With regard to multi-state traffic intersection where there are many entrance lanes,multifunctional lanes should be installed. According to the queuing theory and predicting outcome ofmulti-state traffic flow, the selecting model of multifunctional lanes direction has been presented,which takes the minimum total operational delay as optimization objective. Under the condition thateffective green time could satisfy maximum queue length passing through, every phase has beenrolling optimized. And in this case, the predictive control method on space resources dynamicoptimization in intersection installing multifunctional lanes has been proposed.
(7) The denseness function probabilistic statistics of multi-state traffic flow has beenestablished on the foundation of random process theory. Combined with the phase changes ofdifferent strategies, such as timing control, inductive control and predictive control, the signalcontrol delay model of multi-state traffic flow has been proposed.
(8) The effectiveness of control strategy of multi-state traffic flow and the accuracy of delayanalysis theory have been confirmed by the simulation using VISSIM and related software.
引文
[1]中国科学院.2010年城市蓝皮书[M].社会科学文献出版社.2010,7.
[2]郑州市规划局.郑州市城市总体规划（2010-2020年）[EB/OL].2010.10.
[3]合肥市规划设计研究院.合肥市城市总体规划(2006-2020)[EB/OL].2006.9
[4]天津市规划局.天津市城市总体规划(2005年-2020年)[EB/OL].2006.10.
[5]陈菲.2010年10月我国机动车保有量已达1.99亿辆[N].新华网.2010-10-7.
[6]刘茜;邱官升.天然气汽车市场推广的现存问题及对策[J].科技致富向导.2010,24:134-135.
[7]申文峰.中科院专家：中国15座城市因拥堵每天损失近10亿[N].四川新闻网.2010-10-8.
[8] Greenshields, B. D. The Photographic Method of Studying Traffic Behavior[C], HighwayResearch Board Proceedings,1934,13:382-399.
[9] Greenshields, B.D. A study of highway capacity. Proceedings highway Research Record,Washington.1935,14:448-477.
[10] Green shields B.D., Shapiro D., and Ericksen E.L. Traffic Performance at Urban streetintersections [C]. Bureau of Highway Traffic, Technical Report.1947, No.1.
[11] Kinzer J.P., Application of the Theory of Probability to Problems of Highway Traffic [J].Proc Inst Traffic Eng.,1934（5）:118-124.
[12] Adams, W. F. Road Traffic Considered as a Random Series [J], Journal of the Institution ofCivil Engineers,1936.4:121-130.
[13] Pipes, L. A. An Operational Analysis of Traffic Dynamics. J. Appl. Phys.,1953,24(3):274-281.
[14] Reuschel A. Fahrzeugbewegungen in der Kolonne[J]. Oesterreichisches Ingenieur-Aarchiv4,1950a(3/4):193-215.
[15] Reuschel, A.. Fahrzeugbewegungen in derKolonne bei gleichformig beschleunigtem oderverzogertem Leitfahrzeug[J]. Zeitschrift des Oesterreichischen Ingenieurund Architekten-Vereines95,1950b and1950c(7/859-62and9/10):73-77.
[16] Herman, R. Theory of Traffic Flow [M]. Elsevier Science Publishers.1961.
[17] Herman, R. Technology, Human Interaction, and Complexity: Reflections on VehicularTraffic Science [J]. Operations Research,1992,40(2):199-212.
[18] Chandler, R. E., R. Herman, and E. W. Montroll. Traffic Dynamics: Studies in Car Following[J], Opns. Res.,1958,6:165-183.
[19] Lighthill, M. J. and G. B. Whitham. On Kinematic Waves: II. A Theory of Traffic Flow onLong Crowded Roads[C]. Proceedings of the Royal Society. London,1955, A229:317-347.
[20] Edie, L. C. and R. S. Foote. Traffic Flow in Tunnels[C], Proc. Highway Research Board,1958,37:334-344.
[21] Wardrop, J. G. Some Theoretical Aspects of Road Traffic Research [C]. Proceedings of theInstitution of Civil Engineers,1952,1(2):325-378, U.K.
[22] Daniel L. Gerlough， Traffic Flow Theory， TRB，Special Report No.165，TransportationResearch Board[R]， National Research Council， Washington D.C.，1975
[23] A. D. May, Traffic Flow Fundamentals~Prentice Hall[R], Englewood Cliffs, NJ,1990
[24] Gerlough D.L. and Barnes F.C. The Poisson and other probability distributions in highwaytraffic [M]. Eye Foundation.1970.
[25] Whittlesey J.R.B. Incomplete gamma functions for evaluating Erlang process probabilities[J].Math.Comput.1963,17(81):11-17.
[26] Haight F.A. The generalized Poisson distribution. Annals of the Institute of StatisticalMathematics.1959，11(sup.1):101-105.
[27] Tsao HSJ, Hall RW and Chatterjee I. Analytical models for vehicle/gap distribution onautomated highway systems [J]. TRANSPORTATION SCIENCE.1997,31(1):18-33.
[28] Neubert, L; Santen, L; Schadschneider, A; Schreckenberg, M. Single-vehicle data of highwaytraffic: a statistical analysis [J]. Phys Rev E Stat Phys Plasmas Fluids Relat InterdiscipTopics.1999,60(6):6480-6490.
[29] Krbalek, M.; Seba, P.; Wagner, P. Headways in traffic flow: Remarks from a physicalperspective [J]. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics).2001,64(6):66-119.
[30] Abul-Magd, A Y. Modeling highway-traffic headway distributions using super statistics [J].Phys Rev E Stat Nonlin Soft Matter Phys.2007,76(5):57-101.
[31] Sheu JB. A stochastic modeling approach to real-time prediction of queue overflows [J].TRANSPORTATION SCIENCE.2003,37(1):97-117.
[32] Cohen SL. Application of car-following systems to queue discharge problem at signalizedintersections [J]. TRANSPORTATION RESEARCH RECORD.2002,1802:205-213.
[33] Albanese M, Camus R, Longo G. Capacity and queue modeling for on-ramp-freewayjunctions [J]. Transportation Research Record.2003,1852:256-264.
[34] Jin WL. A kinematic wave theory of lane-changing traffic flow [J]. TRANSPORTATIONRESEARCH PART B-METHODOLOGICAL.2010,44(8-9):1001-1021.
[35] Ossen S, Hoogendoorn SP. Heterogeneity in car-following behavior: Theory and empirics [J]TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES.2011,19(2):182-195.
[36] Esser J, Schreckenberg M. Microscopic simulation of urban traffic based on cellular automata[J]. International Journal of Modern Physics C,1997,8(5):1025.
[37] Helbing D, Huberman B. A. Coherent moving states in highway traffic [J].NATURE.1998,396:738-740.
[38] Helbing D, Treiber M.Traffic theory-Jams, waves, and clusters [J]. SCIENCE.1998，282:2001-2003.
[39] Helbing D. Traffic and related self-driven systems [J]. Rev. Mod. Phys.2001,73(4):1067-1141.
[40] B. S. Kerner1and H. Rehborn. Experimental Properties of Phase Transitions in Traffic Flow[J]. PHYSICAL REVIEW LETTERS.1997,79(20):4030-4033.
[41] Kerner BS, Klenov SL, Wolf DE. Cellular automata approach to three-phase traffic theory [J].JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL.2002,35(47):9971-10013.
[42] Schonhof M, Helbing D. Criticism of three-phase traffic theory [J]. Transportation ResearchPart B-Methodological.2009,43(7):784-797.
[43] Putcha, C.S.,Tadi, R.R.,Charoensuphong, M., Kreiner J.H. Development of improved andefficient traffic flow model [J]. International Journal of Modelling and Simulation,2007,27(1):89-93.
[44] Tanaka, Katsunori, Nagatani, Takashi, Masukura, Shuichi. Fundamental diagram in trafficflow of mixed vehicles on multi-lane highway [J]. Physica A: Statistical Mechanics and itsApplications,2008,387(22):5583-5596.
[45] Logghe, S., Immers, L.H. Multi-class kinematic wave theory of traffic flow[J].TransportationResearch Part B: Methodological,2008,42(6):523-541.
[46] Ngoduy, D. Macroscopic effects of multianticipative driving behavior on traffic flowcharacteristics [J].Transportation Research Record,2009,2124:103-112.
[47] Giglio, D., Minciardi, R. Optimization of traffic flows in congested metropolitan areas［C］.IEEE Conference on Intelligent Transportation Systems,2008ITSC:116-121.
[48] Bassan Shy, Ceder Avishai. Calibrated time-dependent two-regime traffic flow models [J].Traffic Engineering and Control,2007,48(5):228-234.
[49] Brockwell, P J, Davies R A. Time Series: Theory and Methods [M], New York:Springer-Verlag.1987.
[50] C.T.Haan, Statistical Methods in Hydrology [M], the Iowa State University Press, Iowa,1977.
[51] R.L.Bras and I.Rodrigues-Iturbe, Random Functions and Hydrology [M], Addison-Wesley，London，1985.
[52] Patricia Sholl, R.Kenneth Wolfe. The Kalman filter as an adaptive forecasting procedure foruse with Box-Jenkins arima models [J]. Computers&Industrial Engineering.1985,9(3):247-262.
[53] Vythoulkas P C. Alternative approaches to short term traffic forecasting for use in driverinformation systems [A]//Proceedings of the12th International Symposium on the Theoryof traffic flow and transportation: transportation and traffic theory. Amsterdam: ElsevierScience,1993:485-506.
[54] Smith B L, Demetsky M J. Short-term traffic flow prediction: Neural network approach [J].Transportation Research Record,1994(1453):98-104.
[55] Yasdi, R. Prediction of road traffic using a neural network approach [J]. Neural Computing&Applications.1999,8(2):135-142.
[56] Messai N., Thomas P.，Lefebvre D.，EI Moudni A. A neural network approach for freewaytraffic flow prediction[C]. Proceedings of the2002IEEE International Conference onControl Applications.2002,1:984-989.
[57] Florio, L.; Mussone, L. Neural-network models for classification and forecasting of freewaytraffic flow stability [J]. Control Engineering Practice.1996,4(2):153-164.
[58] Chen S., Cowan C.F.N., Grant P.M. Orthogonal least squares learning algorithms for radialbasis function network[C]. IEEE Transactions on Neural Networks.1991,2(2):302-309.
[59] Hongbin Yin, Wong, S.C., Jianmin Xu. Urban traffic flow prediction using a fuzzy-neuralapproach [J].Transportation Research Part C (Emerging Technologies).2002,10C (2):85-98.
[60] Davis G A, Nihan N L. Nonparametric regression and short term freeway traffic forecasting[J]. Journal of Transportation Engineering.1991,117(4):178-188.
[61] Bates J M, Granger C. The combination of forecast [J]. Operation Research Quarterly,1969,20:451-468.
[62] Nie Peilin, Yu Zhi, He Zhaocheng. Short term traffic flow forecasting based on artificialneural network combined predictor[C]. International Conference on TransportationEngineering2007.2007:1481-1486.
[63] Stathopoulos Antony, Dimitriou Loukas, Tsekeris Theodore. Fuzzy modeling approach forcombined forecasting of urban traffic flow [J]. Computer-Aided Civil and InfrastructureEngineering.2008,23(7):521-535.
[64] Stathopoulos Antony, Karlaftis Matthew G., Dimitriou Loukas. Fuzzy rule-based systemapproach to combining traffic count forecasts [J]. Transportation Research Record.2010,2183:120-128.
[65]李群祖,夏清国,巴明春,潘万鹏.城市交通信号控制系统现状与发展[J].科学技术与工程.2009,9(24):7436-7442.
[66] Webster, F. V.1958. Traffic signal settings [M]. Department of Scientific and IndustrialResearch, Road Research Technical Paper, No.39, H. M. Stationery Office, London, U.K.
[67] Webster, F.V., B.M. Cobbe. Techn. Rep. No.56: Traffic signals [M], London: Road ResearchLaboratory,1966.
[68] Akcelik R. Traffic Signals: Capacity and timing analysis, Research Report ARR NO.123[R].Australian: Australian Road Research Board,1981.
[69] Dunne, M. C., and Potts, R. B. Algorithm for traffic control [J]. Operations Research,1964,12(6):870-881.
[70] McNeil, D. R. A solution to the fixed-cycle traffic light problem for compound poissonarrivals [J]. Journal of Applied Probability,1968,5(3):624–635.
[71] Allsop, R. E., Charlesworth, J.A. Traffic in a signal controlled network: An example ofdifferent signal timings inducing different routing [J]. Traffic Engineering and Control,1977,18:262-264.
[72] Robertson, D. I. TRANSYT: A traffic network study tool [R]. RRL Report, No. LR253,Transport and Road Research Laboratory, Grow Thorne, UK.1969.
[73] Frank L. Dolan PE. Adaptive Traffic Signal Systems Detectors&Communication Devices[R]TRB, New York.2001,7.
[74] Morris, R.W.J., Pak-Poy, P.G. Intersection control by vehicle-actuated signals. In: VehicularTraffic Science[C]. Proceedings of the Third International Symposium on the Theory ofTraffic Flow. New York,1965:300–301.
[75] FHWA. Traffic Control Systems Handbook [R]. Federal Highway Administration, ReportFHWA-IP-85-12, Washington, DC,1985,401-456.
[76] Sims, A. G. and Dobinson, K. W. The Sydney Coordinated Adaptive Traffic (SCAT) SystemPhilosophy and Benefits [J]. IEEE Transactions on Vehicular Technology.1980,29:130–137.
[77] Hunt P.B., Robertson, D. I., Bretherton, R. D., and Winton, R. I. SCOOT—A trafficresponsive method of coordinating signals, Rep. LR1014[R]. Transport and Road ResearchLaboratory, Crow Thorne, England.1981.
[78] MIRCHANDANI P, Head L. A real-time traffic control system: Architecture, algorithms andanalysis [J]. Transportation Research Part C,2001,9(6):415–432.
[79] ZHENG X, RECKER W, CHU L Y. Optimization of Control Parameters for AdaptiveTraffic-Actuated Signal Control [J]. Journal of Intelligent Transportation Systems,2010,14(2):95–108.
[80] Pappis, C.P., Mamdani, E.H. A fuzzy logic controller for a traffic junction [J]. IEEETransactions on Systems, Man and Cybernetics,1977,7(10):707–717.
[81] Janecek, J.J., Zargham, M.R. A fuzzy logic controller for a traffic signal [C]. SPIE.1995,2622:687–691.
[82] Niittymaki, X., Jarkko, P. Isolated traffic signals-vehicle dynamics and fuzzy control [R].Ph.D. Thesis, Helsinki University of Technology, Civil and Environmental Engineering.1997.
[83] Murat Y S, Gedizlioglu E. A fuzzy logic multi-phased signal control model for isolatedjunctions [J]. Transp. Res. Part C: Emerging Technologies,2005,13(1):19–36.
[84] Ledoux C. An urban traffic flow model integrating neural networks [J], TransportationResearch Part C,1997,5(5):287-300.
[85] CEYLAN H, BELL M G H. Traffic signal timing optimization based on genetic algorithmapproach, including drivers’ routing [J]. Transportation Research Part B,2004,38(4):329-34.
[86] GHASSAN A, CHEN H, RAHIM F B. Modeling Traffic Output for Design of DynamicMulti-Cycle Control in Congested Conditions [J]. Journal of Intelligent TransportationSystems,2007,11(1):25–40.
[87] Radwan, E., and Elahi, M. Knowledge-based systems applications for traffic signal control[R]. Paper presented at the Workshop on Knowledge-Based Expert Systems inTransportation. Espoo, Finland.1990.
[88] Chiou Y, Lan L W. Adaptive traffic signal control with iterative genetic fuzzy logic controller(GFLC)[C]. In: Proc.,2004IEEE Int. Conf. Networking, Sensing, and Control, Taipei,Taiwan,2004,287–292.
[89] Bingham E. Reinforcement learning in neurofuzzy traffic signal control [J]. Euro. J.Operational Res.2001,131(2):232–241.
[90] Iisakki K. Multi-agent fuzzy signal control based on real-time simulation [J]. TransportationResearch Part C.2003,11:389–403.
[91] McNeil D.R. A solution to the fixed cycle traffic light problem for compound Poisson arrivals[J]. Journal of Applied Probability.1968,5:624-635.
[92] ALLSOP R E. Delay at a fixed time traffic signal: theoretical analysis [J]. TransportationScience,1972,6(3):260-285.
[93] Ak elik R. Time-Dependent Expressions for Delay, Stop Rate and Queue Length at TrafficSignals [R]. Australian Road Research Board, Internal Report,1980:367-371.
[94] Van Leeuwaarden JSH. Delay Analysis for the Fixed-Cycle Traffic-Light Queue [J].TRANSPORTATION SCIENCE.2006,40(2):189-199.
[95]吴正．低速混合型城市交通的流体力学模型[J].力学学报，1994，26(2)：149-157．
[96]王明祺．交通流理论的研究进展[J]．力学进展.1995，25(3)：343-355.
[97]李进平，张乐文.交通流的流体动力学模型研究[J].武汉理工大学学报(交通科学与工程版).2002,12（26）：748-751.
[98]冯苏苇.交通流的动力学模型与数值模拟[J].上海大学学报(自然科学版).1997,8（3）：443－453.
[99]吴正.关于交通流动力学模型与交通状态指数研究[J].水动力学研究与进展.2003,7（4）：403-407.
[100]贺国光,崔岩,王桂珠.单个交叉路口到车服从泊松分布条件下控制动态响应的仿真研究[J].系统工程，2002,9：65-71.
[101]徐伟民，熊烈强.与车辆跟驰理论统一的一维交通流动力模型研究[J].交通运输系统工程与信息.2002,2(1):42-45.
[102] Cao Hongcui, Huang Haijun, Xu Wei, Chen Deying, Yu Jiong, Li Jun, Li Lanjuan. Fecalmetabolome profiling of liver cirrhosis and hepatocellular carcinoma patients by ultraperformance liquid chromatography-mass spectrometry [J]. Analytica Chimica Acta.2011,691(1-2):68-75.
[103] Jiang R, Wu Q S, Jia B, Intermittent unstable structures induced by incessant constantdisturbances in the full velocity difference car-following model, PHYSICA D.2008,237(4),467-474.
[104] Jiang R, Wang R L, Hu M B, et al. Spontaneous symmetry breaking in a two-lane systemwith parallel update, JOURNAL OF PHYSICS A.2007,40(31):9213-9225.
[105] Jiang R, Wu Q S, Cellular automata models for synchronized traffic flow, JOURNAL OFPHYSICS A.2003,36(2):381-390.
[106]姜紫峰，荆便顺.人工神经网络在交通领域中的应用[J].公路交通科技.1997,14(4):18-23.
[107]尹宏宾，徐建闽，周其节.遗传算法在交通量预测中的应用[J].中国公路学报.1998,11(2):69-73.
[108] Li, Ruimin, Lu Huapu. Combined Neural Network Approach for Short-Term Urban FreewayTraffic Flow Prediction[C]//6th International Symposium on Neural Networks. Wuhan,Germany: Springer Verlag,2009:26-29.
[109] Li Qiangwei. Short-time Traffic Flow Volume Prediction Based on Support Vector Machinewith Time-dependent Structure [C]//I2MTC2009-International Instrumentation andMeasurement Technology Conference. Singapore, USA: IEEE Computer Society,2009:5-7.
[110]傅惠,胡刚,徐建闽,许伦辉.基于神经网络的城市关联交叉口交通流预测控制方法[J].中国公路学报,2008,21(5):91-95.
[111]董超俊,刘智勇.多层混沌神经网络及其在交通量预测中的应用[J].系统仿真学报，2007,19（19）:4450-4453.
[112]夏英，梁中军，王国胤.基于时空分析的短时交通流量预测模型[J].南京大学学报（自然科学版）.2010,46(5):552-560.
[113]邓聚龙.灰色系统理论教程[M].武汉：华中理工大学出版社，1990.
[114]刘思峰.灰色系统理论及其应用[M].北京：科学出版社，2010.
[115]刘国林,高放,李俊芬.最优组合预测及其在短时交通流预测中的应用[J].山东科技大学学报（自然科学版）,2009,28(1):10-12.
[116] Li Qingfu, Hu Qunfang, Zhang Peng. Application of Grey-Markov Model in PredictingTraffic Volume [C]//Proceedings of2007IEEE International Conference on Grey Systemsand Intelligent Services. Nanjing, USA: Computer Society,2007:18-20.
[117]万玉成，何亚群，盛昭瀚.基于灰色系统与神经网络的航材消耗广义加权函数平均组合预测模型研究[J].系统工程理论与实践.2003，7:80-87.
[118]郑为中，史其信.基于贝叶斯组合模型的短期交通量预测研究[J].中国公路学报.2005，18(1):85-89.
[119]王京元.信号交叉口时空资源优化实用方法研究[D].东南大学博士学位论文.2006:7-8.
[120]李水友,刘智勇.城市交通感应控制综述[J].城市交通.2006,4(6):64-67.
[121]张卫华，陆化普，石琴，刘强.公交优先的信号交叉口配时优化方法[J].交通运输工程学报，2004,4(3):49-53.
[122]张卫华,王炜.基于公交优先通行的交叉口预信号设置方法研究[J].公路交通科技.2004,21(6):101-104.
[123]陈淑燕,陈森发,吴明赞.单路口交通的多相位实时模糊控制[J].系统工程理论与实践,2003,23(1):110-115.
[124]沈国江,孙优贤.基于相序优化的多相位模糊交通控制J].控制与决策，2002,17(增刊):654-658.
[125]高海军，李灵犀，陈龙.交通路口可变相位信号控[J].交通运输工程学报，2003，3(3)：79-83.
[126]赵忠杰,刘小强,谢光秋.单交通路口变相位变周期信号控制[J].长安大学学报（自然科学版），2005,25(6):70-72.
[127]赵冬斌，刘德荣，易建强.基于自适应动态规划的城市交通信号优化控制方法综述[J].自动化学报.2009,35(6):676-681.
[128]朱文兴,贾磊.单路口信号灯模糊—遗传算法优化配时研究[J].系统仿真学报,2004,16(6):1173-1178.
[129]刘智勇,李水友.城市交通信号的ANN自校正预测控制[J].控制理论与应用，2003,20(6):933-937.
[130]李丽丽，曲昭伟，陈永恒，王殿海.可变车道的控制方法[J].吉林大学学报（工学版），2009,39(1):98-103.
[131]庄焰,曾文佳.信号交叉口延误计算模型研究[J].深圳大学学报理工版.2006,23(4):309-313.
[132]裴玉龙,刘广萍.自适应信号控制下交叉口延误计算方法研究[J].公路交通科技.2005,22(7):110-114.
[133]潘全如,朱翼隽.信号交叉口车辆的延误分析[J].系统科学与数学.2009,29(6):728-734.
[134]许伦辉,傅惠.交通信息智能预测理论与方法[M].北京:科学出版社,2009:29-39.
[135]张玉梅,曲仕茹,温凯歌.交通流量的混沌特性分析及预测模型研究[J].土木工程学报,2009,42（1）:119-123.
[136]李洪萍，裴玉龙.基于混沌理论的交通流短时预测模型[J].昆明理工大学学报(理工版),2006,31(5):95-99.
[137]丁恒，郑小燕，张卫华.基于伴随相位的非对称交叉口交通信号改善研究[J].合肥工业大学（自然科学版），2008,31(3)356-359.
[138] Heng DING, Xiao-yan ZHENG, Xiao MA, Wu-wei CHEN. Multi-state Flow InductiveControl Method at Intersection with Multi-function Entrance Lane[C]. InternationalConference of Chinese Transportation Professionals2010,8:1377-1383.
[139]王炜,过秀成.交通工程学[M].南京：东南大学出版社,2000.
[140] Zaman M R, Roy M K, Akhter N. Chi-square Mixture of Gamma Distribution [J]. Journal ofApplied Science.2005,5(9):1632-1635.
[141] Zaart A E, Ziou D, Wang S R, Jiang Q S. SAR Images Segmentation Using Mixture ofGamma Distribution[C]//Vision Interface. Trois-Riveieres,1999:125-130.
[142]黄卓,潘晓,林波,郭波.有限混合Gamma分布的拓扑稠密性证明[J].数学的实践与认识.2007,37(12):103-108.
[143]黄卓,李苏军,郭波.基于混合Gamma分布的通用可靠性寿命数据拟合方法[J].航空学报.2008,29(2):379-386.
[144] Wang H X, Hu Z L. On EM Estimation for Mixture of Multivariate-Distributions [J]. NeuralProcess Lett,2009,30(10):243–256.
[145]常玉林,王炜,曹洪.主车流服从Erlang分布下支路通行能力研究[J].东南大学学报,1998,28(3):98-102.
[146]吴兵，李晔.交通管理与控制[M].北京:人民交通出版社,2009.
[147] Ingrid Daubechies.Ten Lectures on Wavelets [M]. New Jersey: Bibliometrics,2004.
[148]毛中亚,郭其一.城市轨道交通系统谐波的小波降噪仿真分析[J].系统仿真学报，2006,18(增刊2)：939-941.
[149]汪金菊，徐小红，朱功勤，朱琇珺，傅建伟.混沌信号的马尔可夫模型降噪[J].系统仿真学报，2009，21（8）:2299-2302.
[150]王丽艳，赵建辉，李帆.基于RBF神经网络的井眼方位角误差建模及补偿[J].系统仿真学报,2007,19(17):4097-1400.
[151] Ding Heng, Zheng Xiao-yan, Huang Wen-juan, Chen Wu-wei. Intersection Traffic SignalTime Division Method Based on Wavelet Analysis.2009International Conference onIntelligent Computation Technology and Automation，2009,10:641-643
[152]郑小燕，丁恒，刘拥辉，程林结.基于Monte-Carlo算法的交通流仿真.合肥工业大学（自然科学版）2009，（9）：1374-1377.
[153]徐钟济.蒙特卡罗方法[M].上海：上海科学技术出版社,1985.
[154]丁柏群,张鹏.蒙特卡罗模拟信号交叉口的服务水平[J].森林工程,2006,22(3):35～37.
[155]邝先验,邹小洪,王晓燕.微观交通仿真车辆随机生成模型分析与设计[J].江西理工大学学报,2006,27(3):20-23.
[156]罗霞，刘澜.交通管理与控制[M].北京：人民交通出版社，2008.
[2]郑州市规划局.郑州市城市总体规划（2010-2020年）[EB/OL].2010.10.
[3]合肥市规划设计研究院.合肥市城市总体规划(2006-2020)[EB/OL].2006.9
[4]天津市规划局.天津市城市总体规划(2005年-2020年)[EB/OL].2006.10.
[5]陈菲.2010年10月我国机动车保有量已达1.99亿辆[N].新华网.2010-10-7.
[6]刘茜;邱官升.天然气汽车市场推广的现存问题及对策[J].科技致富向导.2010,24:134-135.
[7]申文峰.中科院专家：中国15座城市因拥堵每天损失近10亿[N].四川新闻网.2010-10-8.
[8] Greenshields, B. D. The Photographic Method of Studying Traffic Behavior[C], HighwayResearch Board Proceedings,1934,13:382-399.
[9] Greenshields, B.D. A study of highway capacity. Proceedings highway Research Record,Washington.1935,14:448-477.
[10] Green shields B.D., Shapiro D., and Ericksen E.L. Traffic Performance at Urban streetintersections [C]. Bureau of Highway Traffic, Technical Report.1947, No.1.
[11] Kinzer J.P., Application of the Theory of Probability to Problems of Highway Traffic [J].Proc Inst Traffic Eng.,1934（5）:118-124.
[12] Adams, W. F. Road Traffic Considered as a Random Series [J], Journal of the Institution ofCivil Engineers,1936.4:121-130.
[13] Pipes, L. A. An Operational Analysis of Traffic Dynamics. J. Appl. Phys.,1953,24(3):274-281.
[14] Reuschel A. Fahrzeugbewegungen in der Kolonne[J]. Oesterreichisches Ingenieur-Aarchiv4,1950a(3/4):193-215.
[15] Reuschel, A.. Fahrzeugbewegungen in derKolonne bei gleichformig beschleunigtem oderverzogertem Leitfahrzeug[J]. Zeitschrift des Oesterreichischen Ingenieurund Architekten-Vereines95,1950b and1950c(7/859-62and9/10):73-77.
[16] Herman, R. Theory of Traffic Flow [M]. Elsevier Science Publishers.1961.
[17] Herman, R. Technology, Human Interaction, and Complexity: Reflections on VehicularTraffic Science [J]. Operations Research,1992,40(2):199-212.
[18] Chandler, R. E., R. Herman, and E. W. Montroll. Traffic Dynamics: Studies in Car Following[J], Opns. Res.,1958,6:165-183.
[19] Lighthill, M. J. and G. B. Whitham. On Kinematic Waves: II. A Theory of Traffic Flow onLong Crowded Roads[C]. Proceedings of the Royal Society. London,1955, A229:317-347.
[20] Edie, L. C. and R. S. Foote. Traffic Flow in Tunnels[C], Proc. Highway Research Board,1958,37:334-344.
[21] Wardrop, J. G. Some Theoretical Aspects of Road Traffic Research [C]. Proceedings of theInstitution of Civil Engineers,1952,1(2):325-378, U.K.
[22] Daniel L. Gerlough， Traffic Flow Theory， TRB，Special Report No.165，TransportationResearch Board[R]， National Research Council， Washington D.C.，1975
[23] A. D. May, Traffic Flow Fundamentals~Prentice Hall[R], Englewood Cliffs, NJ,1990
[24] Gerlough D.L. and Barnes F.C. The Poisson and other probability distributions in highwaytraffic [M]. Eye Foundation.1970.
[25] Whittlesey J.R.B. Incomplete gamma functions for evaluating Erlang process probabilities[J].Math.Comput.1963,17(81):11-17.
[26] Haight F.A. The generalized Poisson distribution. Annals of the Institute of StatisticalMathematics.1959，11(sup.1):101-105.
[27] Tsao HSJ, Hall RW and Chatterjee I. Analytical models for vehicle/gap distribution onautomated highway systems [J]. TRANSPORTATION SCIENCE.1997,31(1):18-33.
[28] Neubert, L; Santen, L; Schadschneider, A; Schreckenberg, M. Single-vehicle data of highwaytraffic: a statistical analysis [J]. Phys Rev E Stat Phys Plasmas Fluids Relat InterdiscipTopics.1999,60(6):6480-6490.
[29] Krbalek, M.; Seba, P.; Wagner, P. Headways in traffic flow: Remarks from a physicalperspective [J]. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics).2001,64(6):66-119.
[30] Abul-Magd, A Y. Modeling highway-traffic headway distributions using super statistics [J].Phys Rev E Stat Nonlin Soft Matter Phys.2007,76(5):57-101.
[31] Sheu JB. A stochastic modeling approach to real-time prediction of queue overflows [J].TRANSPORTATION SCIENCE.2003,37(1):97-117.
[32] Cohen SL. Application of car-following systems to queue discharge problem at signalizedintersections [J]. TRANSPORTATION RESEARCH RECORD.2002,1802:205-213.
[33] Albanese M, Camus R, Longo G. Capacity and queue modeling for on-ramp-freewayjunctions [J]. Transportation Research Record.2003,1852:256-264.
[34] Jin WL. A kinematic wave theory of lane-changing traffic flow [J]. TRANSPORTATIONRESEARCH PART B-METHODOLOGICAL.2010,44(8-9):1001-1021.
[35] Ossen S, Hoogendoorn SP. Heterogeneity in car-following behavior: Theory and empirics [J]TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES.2011,19(2):182-195.
[36] Esser J, Schreckenberg M. Microscopic simulation of urban traffic based on cellular automata[J]. International Journal of Modern Physics C,1997,8(5):1025.
[37] Helbing D, Huberman B. A. Coherent moving states in highway traffic [J].NATURE.1998,396:738-740.
[38] Helbing D, Treiber M.Traffic theory-Jams, waves, and clusters [J]. SCIENCE.1998，282:2001-2003.
[39] Helbing D. Traffic and related self-driven systems [J]. Rev. Mod. Phys.2001,73(4):1067-1141.
[40] B. S. Kerner1and H. Rehborn. Experimental Properties of Phase Transitions in Traffic Flow[J]. PHYSICAL REVIEW LETTERS.1997,79(20):4030-4033.
[41] Kerner BS, Klenov SL, Wolf DE. Cellular automata approach to three-phase traffic theory [J].JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL.2002,35(47):9971-10013.
[42] Schonhof M, Helbing D. Criticism of three-phase traffic theory [J]. Transportation ResearchPart B-Methodological.2009,43(7):784-797.
[43] Putcha, C.S.,Tadi, R.R.,Charoensuphong, M., Kreiner J.H. Development of improved andefficient traffic flow model [J]. International Journal of Modelling and Simulation,2007,27(1):89-93.
[44] Tanaka, Katsunori, Nagatani, Takashi, Masukura, Shuichi. Fundamental diagram in trafficflow of mixed vehicles on multi-lane highway [J]. Physica A: Statistical Mechanics and itsApplications,2008,387(22):5583-5596.
[45] Logghe, S., Immers, L.H. Multi-class kinematic wave theory of traffic flow[J].TransportationResearch Part B: Methodological,2008,42(6):523-541.
[46] Ngoduy, D. Macroscopic effects of multianticipative driving behavior on traffic flowcharacteristics [J].Transportation Research Record,2009,2124:103-112.
[47] Giglio, D., Minciardi, R. Optimization of traffic flows in congested metropolitan areas［C］.IEEE Conference on Intelligent Transportation Systems,2008ITSC:116-121.
[48] Bassan Shy, Ceder Avishai. Calibrated time-dependent two-regime traffic flow models [J].Traffic Engineering and Control,2007,48(5):228-234.
[49] Brockwell, P J, Davies R A. Time Series: Theory and Methods [M], New York:Springer-Verlag.1987.
[50] C.T.Haan, Statistical Methods in Hydrology [M], the Iowa State University Press, Iowa,1977.
[51] R.L.Bras and I.Rodrigues-Iturbe, Random Functions and Hydrology [M], Addison-Wesley，London，1985.
[52] Patricia Sholl, R.Kenneth Wolfe. The Kalman filter as an adaptive forecasting procedure foruse with Box-Jenkins arima models [J]. Computers&Industrial Engineering.1985,9(3):247-262.
[53] Vythoulkas P C. Alternative approaches to short term traffic forecasting for use in driverinformation systems [A]//Proceedings of the12th International Symposium on the Theoryof traffic flow and transportation: transportation and traffic theory. Amsterdam: ElsevierScience,1993:485-506.
[54] Smith B L, Demetsky M J. Short-term traffic flow prediction: Neural network approach [J].Transportation Research Record,1994(1453):98-104.
[55] Yasdi, R. Prediction of road traffic using a neural network approach [J]. Neural Computing&Applications.1999,8(2):135-142.
[56] Messai N., Thomas P.，Lefebvre D.，EI Moudni A. A neural network approach for freewaytraffic flow prediction[C]. Proceedings of the2002IEEE International Conference onControl Applications.2002,1:984-989.
[57] Florio, L.; Mussone, L. Neural-network models for classification and forecasting of freewaytraffic flow stability [J]. Control Engineering Practice.1996,4(2):153-164.
[58] Chen S., Cowan C.F.N., Grant P.M. Orthogonal least squares learning algorithms for radialbasis function network[C]. IEEE Transactions on Neural Networks.1991,2(2):302-309.
[59] Hongbin Yin, Wong, S.C., Jianmin Xu. Urban traffic flow prediction using a fuzzy-neuralapproach [J].Transportation Research Part C (Emerging Technologies).2002,10C (2):85-98.
[60] Davis G A, Nihan N L. Nonparametric regression and short term freeway traffic forecasting[J]. Journal of Transportation Engineering.1991,117(4):178-188.
[61] Bates J M, Granger C. The combination of forecast [J]. Operation Research Quarterly,1969,20:451-468.
[62] Nie Peilin, Yu Zhi, He Zhaocheng. Short term traffic flow forecasting based on artificialneural network combined predictor[C]. International Conference on TransportationEngineering2007.2007:1481-1486.
[63] Stathopoulos Antony, Dimitriou Loukas, Tsekeris Theodore. Fuzzy modeling approach forcombined forecasting of urban traffic flow [J]. Computer-Aided Civil and InfrastructureEngineering.2008,23(7):521-535.
[64] Stathopoulos Antony, Karlaftis Matthew G., Dimitriou Loukas. Fuzzy rule-based systemapproach to combining traffic count forecasts [J]. Transportation Research Record.2010,2183:120-128.
[65]李群祖,夏清国,巴明春,潘万鹏.城市交通信号控制系统现状与发展[J].科学技术与工程.2009,9(24):7436-7442.
[66] Webster, F. V.1958. Traffic signal settings [M]. Department of Scientific and IndustrialResearch, Road Research Technical Paper, No.39, H. M. Stationery Office, London, U.K.
[67] Webster, F.V., B.M. Cobbe. Techn. Rep. No.56: Traffic signals [M], London: Road ResearchLaboratory,1966.
[68] Akcelik R. Traffic Signals: Capacity and timing analysis, Research Report ARR NO.123[R].Australian: Australian Road Research Board,1981.
[69] Dunne, M. C., and Potts, R. B. Algorithm for traffic control [J]. Operations Research,1964,12(6):870-881.
[70] McNeil, D. R. A solution to the fixed-cycle traffic light problem for compound poissonarrivals [J]. Journal of Applied Probability,1968,5(3):624–635.
[71] Allsop, R. E., Charlesworth, J.A. Traffic in a signal controlled network: An example ofdifferent signal timings inducing different routing [J]. Traffic Engineering and Control,1977,18:262-264.
[72] Robertson, D. I. TRANSYT: A traffic network study tool [R]. RRL Report, No. LR253,Transport and Road Research Laboratory, Grow Thorne, UK.1969.
[73] Frank L. Dolan PE. Adaptive Traffic Signal Systems Detectors&Communication Devices[R]TRB, New York.2001,7.
[74] Morris, R.W.J., Pak-Poy, P.G. Intersection control by vehicle-actuated signals. In: VehicularTraffic Science[C]. Proceedings of the Third International Symposium on the Theory ofTraffic Flow. New York,1965:300–301.
[75] FHWA. Traffic Control Systems Handbook [R]. Federal Highway Administration, ReportFHWA-IP-85-12, Washington, DC,1985,401-456.
[76] Sims, A. G. and Dobinson, K. W. The Sydney Coordinated Adaptive Traffic (SCAT) SystemPhilosophy and Benefits [J]. IEEE Transactions on Vehicular Technology.1980,29:130–137.
[77] Hunt P.B., Robertson, D. I., Bretherton, R. D., and Winton, R. I. SCOOT—A trafficresponsive method of coordinating signals, Rep. LR1014[R]. Transport and Road ResearchLaboratory, Crow Thorne, England.1981.
[78] MIRCHANDANI P, Head L. A real-time traffic control system: Architecture, algorithms andanalysis [J]. Transportation Research Part C,2001,9(6):415–432.
[79] ZHENG X, RECKER W, CHU L Y. Optimization of Control Parameters for AdaptiveTraffic-Actuated Signal Control [J]. Journal of Intelligent Transportation Systems,2010,14(2):95–108.
[80] Pappis, C.P., Mamdani, E.H. A fuzzy logic controller for a traffic junction [J]. IEEETransactions on Systems, Man and Cybernetics,1977,7(10):707–717.
[81] Janecek, J.J., Zargham, M.R. A fuzzy logic controller for a traffic signal [C]. SPIE.1995,2622:687–691.
[82] Niittymaki, X., Jarkko, P. Isolated traffic signals-vehicle dynamics and fuzzy control [R].Ph.D. Thesis, Helsinki University of Technology, Civil and Environmental Engineering.1997.
[83] Murat Y S, Gedizlioglu E. A fuzzy logic multi-phased signal control model for isolatedjunctions [J]. Transp. Res. Part C: Emerging Technologies,2005,13(1):19–36.
[84] Ledoux C. An urban traffic flow model integrating neural networks [J], TransportationResearch Part C,1997,5(5):287-300.
[85] CEYLAN H, BELL M G H. Traffic signal timing optimization based on genetic algorithmapproach, including drivers’ routing [J]. Transportation Research Part B,2004,38(4):329-34.
[86] GHASSAN A, CHEN H, RAHIM F B. Modeling Traffic Output for Design of DynamicMulti-Cycle Control in Congested Conditions [J]. Journal of Intelligent TransportationSystems,2007,11(1):25–40.
[87] Radwan, E., and Elahi, M. Knowledge-based systems applications for traffic signal control[R]. Paper presented at the Workshop on Knowledge-Based Expert Systems inTransportation. Espoo, Finland.1990.
[88] Chiou Y, Lan L W. Adaptive traffic signal control with iterative genetic fuzzy logic controller(GFLC)[C]. In: Proc.,2004IEEE Int. Conf. Networking, Sensing, and Control, Taipei,Taiwan,2004,287–292.
[89] Bingham E. Reinforcement learning in neurofuzzy traffic signal control [J]. Euro. J.Operational Res.2001,131(2):232–241.
[90] Iisakki K. Multi-agent fuzzy signal control based on real-time simulation [J]. TransportationResearch Part C.2003,11:389–403.
[91] McNeil D.R. A solution to the fixed cycle traffic light problem for compound Poisson arrivals[J]. Journal of Applied Probability.1968,5:624-635.
[92] ALLSOP R E. Delay at a fixed time traffic signal: theoretical analysis [J]. TransportationScience,1972,6(3):260-285.
[93] Ak elik R. Time-Dependent Expressions for Delay, Stop Rate and Queue Length at TrafficSignals [R]. Australian Road Research Board, Internal Report,1980:367-371.
[94] Van Leeuwaarden JSH. Delay Analysis for the Fixed-Cycle Traffic-Light Queue [J].TRANSPORTATION SCIENCE.2006,40(2):189-199.
[95]吴正．低速混合型城市交通的流体力学模型[J].力学学报，1994，26(2)：149-157．
[96]王明祺．交通流理论的研究进展[J]．力学进展.1995，25(3)：343-355.
[97]李进平，张乐文.交通流的流体动力学模型研究[J].武汉理工大学学报(交通科学与工程版).2002,12（26）：748-751.
[98]冯苏苇.交通流的动力学模型与数值模拟[J].上海大学学报(自然科学版).1997,8（3）：443－453.
[99]吴正.关于交通流动力学模型与交通状态指数研究[J].水动力学研究与进展.2003,7（4）：403-407.
[100]贺国光,崔岩,王桂珠.单个交叉路口到车服从泊松分布条件下控制动态响应的仿真研究[J].系统工程，2002,9：65-71.
[101]徐伟民，熊烈强.与车辆跟驰理论统一的一维交通流动力模型研究[J].交通运输系统工程与信息.2002,2(1):42-45.
[102] Cao Hongcui, Huang Haijun, Xu Wei, Chen Deying, Yu Jiong, Li Jun, Li Lanjuan. Fecalmetabolome profiling of liver cirrhosis and hepatocellular carcinoma patients by ultraperformance liquid chromatography-mass spectrometry [J]. Analytica Chimica Acta.2011,691(1-2):68-75.
[103] Jiang R, Wu Q S, Jia B, Intermittent unstable structures induced by incessant constantdisturbances in the full velocity difference car-following model, PHYSICA D.2008,237(4),467-474.
[104] Jiang R, Wang R L, Hu M B, et al. Spontaneous symmetry breaking in a two-lane systemwith parallel update, JOURNAL OF PHYSICS A.2007,40(31):9213-9225.
[105] Jiang R, Wu Q S, Cellular automata models for synchronized traffic flow, JOURNAL OFPHYSICS A.2003,36(2):381-390.
[106]姜紫峰，荆便顺.人工神经网络在交通领域中的应用[J].公路交通科技.1997,14(4):18-23.
[107]尹宏宾，徐建闽，周其节.遗传算法在交通量预测中的应用[J].中国公路学报.1998,11(2):69-73.
[108] Li, Ruimin, Lu Huapu. Combined Neural Network Approach for Short-Term Urban FreewayTraffic Flow Prediction[C]//6th International Symposium on Neural Networks. Wuhan,Germany: Springer Verlag,2009:26-29.
[109] Li Qiangwei. Short-time Traffic Flow Volume Prediction Based on Support Vector Machinewith Time-dependent Structure [C]//I2MTC2009-International Instrumentation andMeasurement Technology Conference. Singapore, USA: IEEE Computer Society,2009:5-7.
[110]傅惠,胡刚,徐建闽,许伦辉.基于神经网络的城市关联交叉口交通流预测控制方法[J].中国公路学报,2008,21(5):91-95.
[111]董超俊,刘智勇.多层混沌神经网络及其在交通量预测中的应用[J].系统仿真学报，2007,19（19）:4450-4453.
[112]夏英，梁中军，王国胤.基于时空分析的短时交通流量预测模型[J].南京大学学报（自然科学版）.2010,46(5):552-560.
[113]邓聚龙.灰色系统理论教程[M].武汉：华中理工大学出版社，1990.
[114]刘思峰.灰色系统理论及其应用[M].北京：科学出版社，2010.
[115]刘国林,高放,李俊芬.最优组合预测及其在短时交通流预测中的应用[J].山东科技大学学报（自然科学版）,2009,28(1):10-12.
[116] Li Qingfu, Hu Qunfang, Zhang Peng. Application of Grey-Markov Model in PredictingTraffic Volume [C]//Proceedings of2007IEEE International Conference on Grey Systemsand Intelligent Services. Nanjing, USA: Computer Society,2007:18-20.
[117]万玉成，何亚群，盛昭瀚.基于灰色系统与神经网络的航材消耗广义加权函数平均组合预测模型研究[J].系统工程理论与实践.2003，7:80-87.
[118]郑为中，史其信.基于贝叶斯组合模型的短期交通量预测研究[J].中国公路学报.2005，18(1):85-89.
[119]王京元.信号交叉口时空资源优化实用方法研究[D].东南大学博士学位论文.2006:7-8.
[120]李水友,刘智勇.城市交通感应控制综述[J].城市交通.2006,4(6):64-67.
[121]张卫华，陆化普，石琴，刘强.公交优先的信号交叉口配时优化方法[J].交通运输工程学报，2004,4(3):49-53.
[122]张卫华,王炜.基于公交优先通行的交叉口预信号设置方法研究[J].公路交通科技.2004,21(6):101-104.
[123]陈淑燕,陈森发,吴明赞.单路口交通的多相位实时模糊控制[J].系统工程理论与实践,2003,23(1):110-115.
[124]沈国江,孙优贤.基于相序优化的多相位模糊交通控制J].控制与决策，2002,17(增刊):654-658.
[125]高海军，李灵犀，陈龙.交通路口可变相位信号控[J].交通运输工程学报，2003，3(3)：79-83.
[126]赵忠杰,刘小强,谢光秋.单交通路口变相位变周期信号控制[J].长安大学学报（自然科学版），2005,25(6):70-72.
[127]赵冬斌，刘德荣，易建强.基于自适应动态规划的城市交通信号优化控制方法综述[J].自动化学报.2009,35(6):676-681.
[128]朱文兴,贾磊.单路口信号灯模糊—遗传算法优化配时研究[J].系统仿真学报,2004,16(6):1173-1178.
[129]刘智勇,李水友.城市交通信号的ANN自校正预测控制[J].控制理论与应用，2003,20(6):933-937.
[130]李丽丽，曲昭伟，陈永恒，王殿海.可变车道的控制方法[J].吉林大学学报（工学版），2009,39(1):98-103.
[131]庄焰,曾文佳.信号交叉口延误计算模型研究[J].深圳大学学报理工版.2006,23(4):309-313.
[132]裴玉龙,刘广萍.自适应信号控制下交叉口延误计算方法研究[J].公路交通科技.2005,22(7):110-114.
[133]潘全如,朱翼隽.信号交叉口车辆的延误分析[J].系统科学与数学.2009,29(6):728-734.
[134]许伦辉,傅惠.交通信息智能预测理论与方法[M].北京:科学出版社,2009:29-39.
[135]张玉梅,曲仕茹,温凯歌.交通流量的混沌特性分析及预测模型研究[J].土木工程学报,2009,42（1）:119-123.
[136]李洪萍，裴玉龙.基于混沌理论的交通流短时预测模型[J].昆明理工大学学报(理工版),2006,31(5):95-99.
[137]丁恒，郑小燕，张卫华.基于伴随相位的非对称交叉口交通信号改善研究[J].合肥工业大学（自然科学版），2008,31(3)356-359.
[138] Heng DING, Xiao-yan ZHENG, Xiao MA, Wu-wei CHEN. Multi-state Flow InductiveControl Method at Intersection with Multi-function Entrance Lane[C]. InternationalConference of Chinese Transportation Professionals2010,8:1377-1383.
[139]王炜,过秀成.交通工程学[M].南京：东南大学出版社,2000.
[140] Zaman M R, Roy M K, Akhter N. Chi-square Mixture of Gamma Distribution [J]. Journal ofApplied Science.2005,5(9):1632-1635.
[141] Zaart A E, Ziou D, Wang S R, Jiang Q S. SAR Images Segmentation Using Mixture ofGamma Distribution[C]//Vision Interface. Trois-Riveieres,1999:125-130.
[142]黄卓,潘晓,林波,郭波.有限混合Gamma分布的拓扑稠密性证明[J].数学的实践与认识.2007,37(12):103-108.
[143]黄卓,李苏军,郭波.基于混合Gamma分布的通用可靠性寿命数据拟合方法[J].航空学报.2008,29(2):379-386.
[144] Wang H X, Hu Z L. On EM Estimation for Mixture of Multivariate-Distributions [J]. NeuralProcess Lett,2009,30(10):243–256.
[145]常玉林,王炜,曹洪.主车流服从Erlang分布下支路通行能力研究[J].东南大学学报,1998,28(3):98-102.
[146]吴兵，李晔.交通管理与控制[M].北京:人民交通出版社,2009.
[147] Ingrid Daubechies.Ten Lectures on Wavelets [M]. New Jersey: Bibliometrics,2004.
[148]毛中亚,郭其一.城市轨道交通系统谐波的小波降噪仿真分析[J].系统仿真学报，2006,18(增刊2)：939-941.
[149]汪金菊，徐小红，朱功勤，朱琇珺，傅建伟.混沌信号的马尔可夫模型降噪[J].系统仿真学报，2009，21（8）:2299-2302.
[150]王丽艳，赵建辉，李帆.基于RBF神经网络的井眼方位角误差建模及补偿[J].系统仿真学报,2007,19(17):4097-1400.
[151] Ding Heng, Zheng Xiao-yan, Huang Wen-juan, Chen Wu-wei. Intersection Traffic SignalTime Division Method Based on Wavelet Analysis.2009International Conference onIntelligent Computation Technology and Automation，2009,10:641-643
[152]郑小燕，丁恒，刘拥辉，程林结.基于Monte-Carlo算法的交通流仿真.合肥工业大学（自然科学版）2009，（9）：1374-1377.
[153]徐钟济.蒙特卡罗方法[M].上海：上海科学技术出版社,1985.
[154]丁柏群,张鹏.蒙特卡罗模拟信号交叉口的服务水平[J].森林工程,2006,22(3):35～37.
[155]邝先验,邹小洪,王晓燕.微观交通仿真车辆随机生成模型分析与设计[J].江西理工大学学报,2006,27(3):20-23.
[156]罗霞，刘澜.交通管理与控制[M].北京：人民交通出版社，2008.
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