基于智能计算的城市交通信号控制系统研究
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摘要
随着社会经济的发展和城市化进程的加快,城市人口数量以及机动车保有量呈现逐年上升的趋势。城市道路交通拥挤和交通阻塞已经成为世界大中城市普遍存在的问题,并且由此带来了一系列的社会问题,比如交通事故剧增、环境污染、能源紧缺等。大力发展城市交通智能控制系统是解决现代社会交通需求与供给矛盾的重要途径之一。实现城市交通系统的智能控制不仅有利于提高交通运输效率,增加道路交通运营安全,而且还关系到土地资源与能源的合理利用、城市环境的改善,乃至国民经济的持续发展和社会效益的提高。
基于智能计算方法的城市交通信号控制是城市交通智能控制的重要内容之一,对提高城市道路网络通行能力,减少车辆行车延误具有重要意义。目前,我国大中城市的交叉口多为信号控制交叉口,并且大部分信号控制机采用定时信号控制策略,路网部分交叉口布设了视频检测器或者感应线圈用以检测交通流量。本论文立足我国城市交通基础设施建设实际,尝试构建一个基于交通模式识别的交通信号控制系统：如果控制区域内所有交叉口均布设车流检测设备,则根据车流检测设备实时采集的交通流信息,进行交叉口交通模式识别,进而调用与交通模式相适应的信号控制方案；如果控制区域内部分交叉口未布设车流检测设备,则根据车流的时间分布规律对交叉口交通模式进行识别,将控制时域划分为若干时间段,针对不同的时间段调用相应的信号控制方案。
该交通信号控制系统的方案库是预先给出的,信号配时优化算法将干线协调控制与单点定时鲁棒控制相结合,在保证信号控制系统效率的前提下,有效提高了信号控制方案对于流量波动的稳定性。对于布设车流检测设备的区域,该系统采用方案选择式信号控制策略,能够充分利用现有硬件设备,提高信号控制方案的效率以及稳定性；对于未布设车流检测设备的区域,该系统采用定时信号控制策略,对交通信号机的要求低,实施和维护费用低,能够以最少的经济投入,提高交叉口通行能力,最大限度地提高信号控制路网车辆运行效率,减少车辆延误及停车次数,进而降低车辆燃油消耗以及尾气排放。
论文针对构建该系统的关键技术问题展开研究,主要包括了以下六个方面：
(1)在总结和评述国内外学者对于交通流模型研究成果的基础上,基于元胞自动机原理,提出了一个更具普适性的信号控制路网交通流模型。通过改进开放性边界条件,利用一维元胞自动机模型模拟协调控制主干路交通流状况。该模型采用差分方程的形式描述车辆动态行为,解除了信号灯等间距布设的限制,每个交叉口的信号灯可以根据交通流变化自由选择绿信比,相邻交叉口采用绿波控制方式调整相位差。论文利用Matlab软件对模型进行仿真,分析了驶入主干路流量以及干、支路的转弯流量对于主干路平均速度、平均密度和平均流量的影响。在仿真结果的基础上,提出了相应的控制措施改善主干路交通状况,为城市交通信号配时方案库的建立提供了理论依据。
(2)结合我国城市道路信号控制机以及车流检测器的布设现状,提出了一个基于交通模式识别的城市交通信号控制系统框架,分析了该系统的工作原理以及功能模块的构成,并对构建该系统所需的智能计算方法(粗糙集理论、模糊神经网络、遗传算法、元胞传输理论)的概念、原理以及适用性进行了阐述和分析。
(3)基于粗糙集理论、模糊逻辑以及人工神经网络等智能计算方法,构建了用于交叉口交通模式识别的粗模糊神经网络模型。该模型由两个阶段构成：第一阶段,基于粗糙集理论的交通参数约简,得到能够表征交叉口交通特征的最小属性集合；第二阶段,利用第一阶段得到的最小属性集合,建立基于模糊神经网络的交叉口交通模式识别模型。该模型为交通参数数据采集、分析以及处理提供了必要的理论依据,为实现交通模式识别提供了技术支持,是建立交通信号控制系统的前提与基础。
(4)针对单点交叉口定时信号控制无法适应交通流变化的缺陷,提出了单点交叉口定时信号鲁棒控制的思想。在信号控制方案效率优化的基础上,加入了改善控制方案鲁棒性的子目标。论文选择车辆平均延误标准差最小这一指标衡量信号控制方案的稳定性。并通过试算实验,得到了鲁棒控制目标与效率控制目标的权重系数,建立了多目标规划模型,对交叉口的周期以及绿信比两个参数进行优化。
(5)建立了基于元胞传输模型的干线协调控制模型,以优化路网相邻交叉口的相位差。该模型利用元胞传输理论模拟信号控制主干线交通流,并以此为平台分析车辆延误、停车次数、车辆通过量等评价指标。以主干线车辆总延误最小,停车总次数最少以及单位周期内通过车辆数最多为目标函数,以相位差约束为约束条件建立了协调控制优化模型。并采用遗传算法对模型进行求解。求解结果表明,基于元胞传输模型的城市主干线协调控制优化模型有效降低了车辆延误,减少了车辆停车次数,提高了主干线通行能力。
(6)设计交通信号控制系统算法,构建城市交通信号控制方案库,建立基于交通模式识别的交通信号控制系统。将单点交通信号鲁棒控制模型与干线协调控制模型有机结合为一个整体,用以优化交叉口交通信号配时参数(周期、绿信比以及相位差)。该模型充分考虑了城市主干线的交通流特性、上游交叉口排队长度对下游交叉口信号配时的影响以及信号控制对交通流的调节作用,为区域信号控制优化提供了思路和借鉴。最后,将此模型推广到区域范畴,并结合道路网络实际,进行了计算机仿真分析。仿真结果表明,该控制系统在不同流量状态下均能够有效地控制车辆延误、减少车辆的停车次数以及提高车辆通过量,控制方案的稳定性较好。
最后,对论文的创新点以及不足进行了总结分析,提出了未来的研究重点和方向。
With the development of economy and advancement of urbanization, urban population and vehicle number is increasing year by year. Traffic congestion and jam has become a common problem to be faced by large and medium-sized cities in the world. And a series of social problems brought by traffic congestion also trouble them, such as traffic accidents, environment pollution, energy crisis and so on. Developing urban intelligent traffic control system is one of the important ways to solve contradictions between traffic demand and supply. Implementing intelligent control of urban traffic system is not only beneficial to improve transportation efficiency and enhance road traffic safety, but also relates to making full use of land source and energy, improving urban environment, developing national economy and social benefit.
Urban traffic signal control based on intelligent computation is one of the important contents of urban traffic intelligent control, which is of significance to improve traffic capacity of urban road network and reduce delay of vehicles. Recently, most intersections in larger and major Chinese cities are signalized, and signal controllers mostly adopt fixed time signal control strategy. Otherwise, video detectors and induction detectors are placed on the mass of intersecitons on arterial roads to detect traffic flow volume. Based on the construction situations of urban transportation infrastructure facilities, the paper tries to establish a signal control system based on traffic pattern recognition. If traffic flow detectors are equipped for all of the intersections of control area, suitable signal timing plans can be called according to traffic pattern recognition based on traffic flow information detected by detectors. If detectors are not installed in part of intersections of control area, control time domain will be divided into a series of sections according to traffic pattern recognition based on temporal distribution of traffic flow, and corresponding signal timing plans will be invoked at different time.
Signal timing plan library is prepared beforehand, and optimal algorithm combines arterial coordinated control with robust optimization of signal timing at single-point intersections. For the control area without detectors, the system employs fixed time signal control strategy with low implement and maintain cost of traffic controllers. Besides, the system has ability of enhancing capacity of intersections, improving efficiency of vehicles on signalized network, reducing delay and stopping rate, lowering energy consumption and exhaust emission. For the control area with detectors, the system utilizes plan-selection signal control system, which can make full use of hardware equipments and has ability of improving efficiency and stability of control plans.
The main contents of this thesis are listed as follows:
(1) Based on summarizing and reviewing research fruits on traffic flow model of urban signalized road network, a traffic flow model of signalized road network with wide applications is proposed based on cellular automaton theory. A one-dimensional cellular automaton model with improved open boundary conditions is used to simulate the traffic flow on arterial roads with coordinated control system. The model employs difference equations to describe dynamic behaviors of vehicles. The restriction on regularly spaced distribution of traffic signal lamps can be eliminated. Furthermore, the split on every intersection can be chosen according to traffic flow fluctuations. The offset between adjacent intersections can be adjusted by green wave control. Matlab is employed to simulate this model to analyze the impacts on mean velocity, density and volume of arterial traffic flow by the flow volume on the artery and the turning flow volumes from branches. Based on the results of simulation, a series of proposals for improving the arterial traffic situations are put forward, which is a prerequisite for constructing urban traffic signal timing library.
(2) In consideration of the placement situations of urban signal controllers and detectors, a framework of economical and effective urban traffic signal control system is proposed. The paper analyzes basic principle, structure and functional modules of the system, and then explains concepts, principle and applied scope of intelligent computation methods such as rough set theory, fuzzy neural network, genetic algorithm, and cell transmission model, which are used to construct the system.
(3) Based on rough set theory and fuzzy neural network, a rough fuzzy neural network model is proposed to realize traffic pattern recognition of intersections. The model is comprised of two stages. At the first stage, traffic parameters are reduced based on rough set theory to obtain the least reduction of attribute set which can describe traffic characteristics. At the second stage, traffic pattern recognition model is built based on fuzzy neural network using reduction parameters above. The model provides necessary theoretical basis for data collection, analysis and processing of traffic parameters, and it provides technical support for traffic pattern recognition, and it is prerequisite to establish traffic signal control system.
(4) The idea of robust control for intersections is proposed to remedy the disadvantage of fixed-time signal control that could not be suitable for large fluctuations of traffic flow. The sub-objective function is added to traditional optimal objective function. The robust objective function which strengths stability of signal control is to minimize standard deviation of vehicle delay. Based on simulation and analysis of intersections under various traffic conditions, the study establishes the relationship between sub-target weights and flow fluctuating ranges. Then the paper builds a multi-objective optimization model to optimize cycle length and splits of single-point intersections.
(5) Coordinated control system of arterial roads is constructed to optimize offset between adjacent intersections. The model simulates traffic flow on urban signalized arterial road by cell transmission model, and constructs mathematic models of delay, stopping rate and traffic volume based on the platform. The paper proposes a optimization model to optimize the offset between adjacent intersections of coordinated control system. Its objective function is to minimize total delay and stopping rate and to maximize traffic capacity of arterial road. And its constraint condition is offset constraint. Genetic algorithm is executed by Matlab to solve the model. And the experiment results show that the model effectively reduces the delay and stopping rate of vehicles running on arterial road and largely improves traffic capacity of artery.
(6) Combing single-point signal robust control model with coordinated control model of arterial roads, traffic signal control library is built to optimize signal timing of intersections, including cycle length, split and offset. The model provides thoughts for area signal control, considering traffic flow characteristics and the effect of queue length of upstream intersections on signal timing of downstream intersections and adjustment effect of signal control to traffic flow. Finally, the model is applied to area signal control. And combined with real road network, computerized simulation is carried out. The results show that the model not only effectively reduces average delay and stopping rate of vehicles running on arterial road and largely improves traffic capacity of arterial road, but also reduces the sensitivity of signal control for flow volatility.
Finally, the paper summarizes innovations and shortcomings, and proposes future research priorities and orientations.
基于智能计算方法的城市交通信号控制是城市交通智能控制的重要内容之一,对提高城市道路网络通行能力,减少车辆行车延误具有重要意义。目前,我国大中城市的交叉口多为信号控制交叉口,并且大部分信号控制机采用定时信号控制策略,路网部分交叉口布设了视频检测器或者感应线圈用以检测交通流量。本论文立足我国城市交通基础设施建设实际,尝试构建一个基于交通模式识别的交通信号控制系统：如果控制区域内所有交叉口均布设车流检测设备,则根据车流检测设备实时采集的交通流信息,进行交叉口交通模式识别,进而调用与交通模式相适应的信号控制方案；如果控制区域内部分交叉口未布设车流检测设备,则根据车流的时间分布规律对交叉口交通模式进行识别,将控制时域划分为若干时间段,针对不同的时间段调用相应的信号控制方案。
该交通信号控制系统的方案库是预先给出的,信号配时优化算法将干线协调控制与单点定时鲁棒控制相结合,在保证信号控制系统效率的前提下,有效提高了信号控制方案对于流量波动的稳定性。对于布设车流检测设备的区域,该系统采用方案选择式信号控制策略,能够充分利用现有硬件设备,提高信号控制方案的效率以及稳定性；对于未布设车流检测设备的区域,该系统采用定时信号控制策略,对交通信号机的要求低,实施和维护费用低,能够以最少的经济投入,提高交叉口通行能力,最大限度地提高信号控制路网车辆运行效率,减少车辆延误及停车次数,进而降低车辆燃油消耗以及尾气排放。
论文针对构建该系统的关键技术问题展开研究,主要包括了以下六个方面：
(1)在总结和评述国内外学者对于交通流模型研究成果的基础上,基于元胞自动机原理,提出了一个更具普适性的信号控制路网交通流模型。通过改进开放性边界条件,利用一维元胞自动机模型模拟协调控制主干路交通流状况。该模型采用差分方程的形式描述车辆动态行为,解除了信号灯等间距布设的限制,每个交叉口的信号灯可以根据交通流变化自由选择绿信比,相邻交叉口采用绿波控制方式调整相位差。论文利用Matlab软件对模型进行仿真,分析了驶入主干路流量以及干、支路的转弯流量对于主干路平均速度、平均密度和平均流量的影响。在仿真结果的基础上,提出了相应的控制措施改善主干路交通状况,为城市交通信号配时方案库的建立提供了理论依据。
(2)结合我国城市道路信号控制机以及车流检测器的布设现状,提出了一个基于交通模式识别的城市交通信号控制系统框架,分析了该系统的工作原理以及功能模块的构成,并对构建该系统所需的智能计算方法(粗糙集理论、模糊神经网络、遗传算法、元胞传输理论)的概念、原理以及适用性进行了阐述和分析。
(3)基于粗糙集理论、模糊逻辑以及人工神经网络等智能计算方法,构建了用于交叉口交通模式识别的粗模糊神经网络模型。该模型由两个阶段构成：第一阶段,基于粗糙集理论的交通参数约简,得到能够表征交叉口交通特征的最小属性集合；第二阶段,利用第一阶段得到的最小属性集合,建立基于模糊神经网络的交叉口交通模式识别模型。该模型为交通参数数据采集、分析以及处理提供了必要的理论依据,为实现交通模式识别提供了技术支持,是建立交通信号控制系统的前提与基础。
(4)针对单点交叉口定时信号控制无法适应交通流变化的缺陷,提出了单点交叉口定时信号鲁棒控制的思想。在信号控制方案效率优化的基础上,加入了改善控制方案鲁棒性的子目标。论文选择车辆平均延误标准差最小这一指标衡量信号控制方案的稳定性。并通过试算实验,得到了鲁棒控制目标与效率控制目标的权重系数,建立了多目标规划模型,对交叉口的周期以及绿信比两个参数进行优化。
(5)建立了基于元胞传输模型的干线协调控制模型,以优化路网相邻交叉口的相位差。该模型利用元胞传输理论模拟信号控制主干线交通流,并以此为平台分析车辆延误、停车次数、车辆通过量等评价指标。以主干线车辆总延误最小,停车总次数最少以及单位周期内通过车辆数最多为目标函数,以相位差约束为约束条件建立了协调控制优化模型。并采用遗传算法对模型进行求解。求解结果表明,基于元胞传输模型的城市主干线协调控制优化模型有效降低了车辆延误,减少了车辆停车次数,提高了主干线通行能力。
(6)设计交通信号控制系统算法,构建城市交通信号控制方案库,建立基于交通模式识别的交通信号控制系统。将单点交通信号鲁棒控制模型与干线协调控制模型有机结合为一个整体,用以优化交叉口交通信号配时参数(周期、绿信比以及相位差)。该模型充分考虑了城市主干线的交通流特性、上游交叉口排队长度对下游交叉口信号配时的影响以及信号控制对交通流的调节作用,为区域信号控制优化提供了思路和借鉴。最后,将此模型推广到区域范畴,并结合道路网络实际,进行了计算机仿真分析。仿真结果表明,该控制系统在不同流量状态下均能够有效地控制车辆延误、减少车辆的停车次数以及提高车辆通过量,控制方案的稳定性较好。
最后,对论文的创新点以及不足进行了总结分析,提出了未来的研究重点和方向。
With the development of economy and advancement of urbanization, urban population and vehicle number is increasing year by year. Traffic congestion and jam has become a common problem to be faced by large and medium-sized cities in the world. And a series of social problems brought by traffic congestion also trouble them, such as traffic accidents, environment pollution, energy crisis and so on. Developing urban intelligent traffic control system is one of the important ways to solve contradictions between traffic demand and supply. Implementing intelligent control of urban traffic system is not only beneficial to improve transportation efficiency and enhance road traffic safety, but also relates to making full use of land source and energy, improving urban environment, developing national economy and social benefit.
Urban traffic signal control based on intelligent computation is one of the important contents of urban traffic intelligent control, which is of significance to improve traffic capacity of urban road network and reduce delay of vehicles. Recently, most intersections in larger and major Chinese cities are signalized, and signal controllers mostly adopt fixed time signal control strategy. Otherwise, video detectors and induction detectors are placed on the mass of intersecitons on arterial roads to detect traffic flow volume. Based on the construction situations of urban transportation infrastructure facilities, the paper tries to establish a signal control system based on traffic pattern recognition. If traffic flow detectors are equipped for all of the intersections of control area, suitable signal timing plans can be called according to traffic pattern recognition based on traffic flow information detected by detectors. If detectors are not installed in part of intersections of control area, control time domain will be divided into a series of sections according to traffic pattern recognition based on temporal distribution of traffic flow, and corresponding signal timing plans will be invoked at different time.
Signal timing plan library is prepared beforehand, and optimal algorithm combines arterial coordinated control with robust optimization of signal timing at single-point intersections. For the control area without detectors, the system employs fixed time signal control strategy with low implement and maintain cost of traffic controllers. Besides, the system has ability of enhancing capacity of intersections, improving efficiency of vehicles on signalized network, reducing delay and stopping rate, lowering energy consumption and exhaust emission. For the control area with detectors, the system utilizes plan-selection signal control system, which can make full use of hardware equipments and has ability of improving efficiency and stability of control plans.
The main contents of this thesis are listed as follows:
(1) Based on summarizing and reviewing research fruits on traffic flow model of urban signalized road network, a traffic flow model of signalized road network with wide applications is proposed based on cellular automaton theory. A one-dimensional cellular automaton model with improved open boundary conditions is used to simulate the traffic flow on arterial roads with coordinated control system. The model employs difference equations to describe dynamic behaviors of vehicles. The restriction on regularly spaced distribution of traffic signal lamps can be eliminated. Furthermore, the split on every intersection can be chosen according to traffic flow fluctuations. The offset between adjacent intersections can be adjusted by green wave control. Matlab is employed to simulate this model to analyze the impacts on mean velocity, density and volume of arterial traffic flow by the flow volume on the artery and the turning flow volumes from branches. Based on the results of simulation, a series of proposals for improving the arterial traffic situations are put forward, which is a prerequisite for constructing urban traffic signal timing library.
(2) In consideration of the placement situations of urban signal controllers and detectors, a framework of economical and effective urban traffic signal control system is proposed. The paper analyzes basic principle, structure and functional modules of the system, and then explains concepts, principle and applied scope of intelligent computation methods such as rough set theory, fuzzy neural network, genetic algorithm, and cell transmission model, which are used to construct the system.
(3) Based on rough set theory and fuzzy neural network, a rough fuzzy neural network model is proposed to realize traffic pattern recognition of intersections. The model is comprised of two stages. At the first stage, traffic parameters are reduced based on rough set theory to obtain the least reduction of attribute set which can describe traffic characteristics. At the second stage, traffic pattern recognition model is built based on fuzzy neural network using reduction parameters above. The model provides necessary theoretical basis for data collection, analysis and processing of traffic parameters, and it provides technical support for traffic pattern recognition, and it is prerequisite to establish traffic signal control system.
(4) The idea of robust control for intersections is proposed to remedy the disadvantage of fixed-time signal control that could not be suitable for large fluctuations of traffic flow. The sub-objective function is added to traditional optimal objective function. The robust objective function which strengths stability of signal control is to minimize standard deviation of vehicle delay. Based on simulation and analysis of intersections under various traffic conditions, the study establishes the relationship between sub-target weights and flow fluctuating ranges. Then the paper builds a multi-objective optimization model to optimize cycle length and splits of single-point intersections.
(5) Coordinated control system of arterial roads is constructed to optimize offset between adjacent intersections. The model simulates traffic flow on urban signalized arterial road by cell transmission model, and constructs mathematic models of delay, stopping rate and traffic volume based on the platform. The paper proposes a optimization model to optimize the offset between adjacent intersections of coordinated control system. Its objective function is to minimize total delay and stopping rate and to maximize traffic capacity of arterial road. And its constraint condition is offset constraint. Genetic algorithm is executed by Matlab to solve the model. And the experiment results show that the model effectively reduces the delay and stopping rate of vehicles running on arterial road and largely improves traffic capacity of artery.
(6) Combing single-point signal robust control model with coordinated control model of arterial roads, traffic signal control library is built to optimize signal timing of intersections, including cycle length, split and offset. The model provides thoughts for area signal control, considering traffic flow characteristics and the effect of queue length of upstream intersections on signal timing of downstream intersections and adjustment effect of signal control to traffic flow. Finally, the model is applied to area signal control. And combined with real road network, computerized simulation is carried out. The results show that the model not only effectively reduces average delay and stopping rate of vehicles running on arterial road and largely improves traffic capacity of arterial road, but also reduces the sensitivity of signal control for flow volatility.
Finally, the paper summarizes innovations and shortcomings, and proposes future research priorities and orientations.
引文
[1]杨佩昆,吴兵.交通管理与控制[M].北京：人民交通出版社,2005：89-152.
[2]Chard B M and LineX CJ. TRANSYT:The latest development[J].Traffic Engineering and control,1987,28:32-35.
[3]刘智勇.智能交通控制理论及其应用[M].北京：科学出版社,2003：2-20.
[4]全永燊.城市交通控制[M].北京：人民交通出版社,1989：157-198、226-266.
[5]Hunt. P. Betal. The SCOOT online traffic signal optimization technique[J]. Traffic Engineering & Control,1982,23:190-192.
[6]Martin PT and Hockaday S L M. SCOOT-An update[J].ITE Journal,1995, 65(1):44-48.
[7]Hunt. P. Betal, Robertson D I, Bretherton R D.et. SCOOT-A traffic responsive method of coordinating signals[R]. TRRL Report I.R 1014, Crowthorne1981.
[8]Lowrie P R. SCATS principles, methodologies, algorithm[C]. IEE Conference on Road Traffic Signal.London:IEE Publication,1982:67-70.
[9]全永燊.城市交通控制[M].北京：人民交通出版社,1989：3-4.
[10]彭维.城市交通信号智能控制方法研究[D].长春：吉林大学,2007：27-45.
[11]蔡蕾.城市平面交叉路口交通信号优化控制[D].长春：吉林大学,2007：18-40.
[12]Burmeister B, Haddadi A, Matylis G. Application of multi_agent system in traffic and transportation [J]. IEEE Proceeding_Software Engineering,1997,144 (1):51-60.
[13]Laichour H, Maouche S, Mandiau R. Traffic control assistance in connection nodes:Muti-agent application in urban transport systems [C]. Ukraine:International Workshop on Intelligent Data Application and Advanced Computing System: Technology and Application,2001:133-137.
[14]Bingham E. Reinforcement learning in neuro-fuzzy traffic signal control [J]. European Journal of Operational Research,2001,131(2):232-241.
[15]魏武,张起森,王明俊,黄中祥.一种基于模糊逻辑的城市交叉口交通信号控制方法[J].交通运输工程学报,2001(2)：99-102.
[16]陈阳舟,张辉,杨玉珍,胡全连.基于Q学习的Agent在单路口交通控制中的应用[J].公路交通科技,2007(5)：117-120.
[17]万伟,陈峰.基于遗传算法的单交叉口信号优化控制[J].计算机工程,2007,33(16)：217-219.
[18]王秋平,谭学龙,张生瑞.城市单点交叉口信号配时优化.交通运输工程学报,2006(2)：60-64.
[19]赵伟,高凤玲,张毅.道路交叉口交通控制模拟系统[J].交通科技.2004(3)：98-100.
[20]吴义虎,喻丹,何霞,郭文莲.单路口低饱和交通流的多相位混沌控制仿真研究[J].系统仿真技术,2007,3(2)：63-67.
[21]贺国光,崔岩,王桂珠.单路口交通流控制动态响应的仿真研究[J].系统工程学报,2005,20(3)：323-329.
[22]Halim Ceylan, Michael G.H.Bell. Traffic signal timing optimization based on genetic algorithm approach, including drivers'routing [J].Transportation Research Part B,2004,38 (2):329-342.
[23]靳文舟,黄一峰,荣利利,李俊辉,刘颖杰.基于粒子群理论的干道协调控制优化研究[J].交通与计算机,2008,26(1)：31-35.
[24]吴恩,杨晓光,吴震,常云涛.基于遗传算法的干线协调控制参数共同优化[J].同济大学学报(自然科学版),2008,36(7)：921-926.
[25]陈娟,徐立鸿,袁长亮.分层控制算法在过饱和交通干线控制中的应用[J].系统仿真学报,2008,20(15)：4122-4137,4131.
[26]赵晓华,李振龙,于泉,荣建.基于Q学习算法的两交叉口信号灯博弈协调控制[J].系统仿真学报,2007,19(18)：4253-4256.
[27]朱文兴,贾磊.城市主干路交通流多目标优化控制[J].山东大学学报(工学版),2004,34(4)：72-78.
[28]Gartner N H, Stamatiadis C. Arterial-based control of traffic flow in urban grid networks [J]. Mathematics and Computer Modelling,2002,35(5):657-671.
[29]杨晓芳.基于模糊控制的城市交通信号控制系统的研究[D].西安：长安大学,2003：18-40.
[30]Nathan H. Gartner, John D.C. Little, Henry Gabbay. Optimization of traffic signal settings by mixed integer linear programming Part I:the network coordination problem[J]. Transportation Science,1975,9(4):321-343.
[31]Gartner, N.H., Little, J.D.C., Gabbay, H.. Optimization of traffic signal settings by mixed integer linear programming. Part Ⅱ:the network synchronization problem[J]. Transportation Science,1975,9(4):344-363.
[32]Little, J.D.C., Kelson, M.D., Gartner, N.H..MAXBAND:a program for setting signals on arterials and triangular networks[J]. Transportation Research Record,1981,795:40-46.
[33]Cohen, S.L., Liu, C.C.. The bandwidth-constrained TRANSYT signal optimization program[J].Transportation Research Record,1986,1057:1-7.
[34]Gartner, N.H., Assmann, S.F., Lasaga, F.L., Hou, D.L... A multi-band approach to arterial traffic signal optimization[J]. Transportation Research Part B,1991,25 (1):55-74.
[35]Chaudhary, N.A., Messer, C.J.. PASSER-Ⅳ:a program for optimizing signal timing in grid networks[C].Washington,D.C.:72nd Annual Meeting of the Transportation Research Board,1993:82-93.
[36]Allsop R. E.. Some possibilities for using traffic control to influence trip destinations and route choice[C]. Sydney, Australia:Proceedings of the Sixth International Symposium on Transportation and Traffic Theory,1974:345-374.
[37]Nathan H. Gartner. Area traffic control and network equilibrium[R]. Boston: Massachusetts Institute of Technology,1975:1-26.
[38]Hai Yang. Traffic assignment and signal control in saturated road networks[J]. Transportion Research A,1995,29(2):125-139.
[39]Wann-Ming Wey. Model formulation and solution algorithm of traffic signal control in an urban network[J]. Computers, Environment and Urban Systems,2000,24,(4):355-378.
[40]Hong K. Lo, Elbert Chang, Yiu Cho Chan. Dynamic network traffic control[J].Transportation Research Part A:Policy and Practice,2001,35(8):721-744.
[41]Ernesto Cipriani, Gaetano Fusco. Combined signal setting design and traffic assignment problem[J]. European Journal of operation research. 2004,155(3):569-583.
[42]Mariagrazia Dotoli, Maria Pia Fanti, Carlo Meloni. A signal timing plan formulation for urban traffic control [J]. Control Engineering Practice, 2006,14(11):1297-1311.
[43]石建军,于泉,任福田.大城市交通信号控制系统层次与区域动态划分的研究[J].道路交通与安全,2004,(4)：7-9.
[44]高海军,俞国军,李振龙.基于agent的城市交通信号控制[J].控制与决策,2004,19(7)：737-740.
[45]傅惠,徐建闽,卢凯.基于粒子群优化的关联交叉口群信号控制策略研究[J].交通与计算机,2007,25(3)：23-26.
[46]Hua Jiuyi, Ardeshir Faghri. Dynamic Traffic Pattern Classification Using Artificial Neural Networks[J]. Transportation Research Record,1993,1399:14-19.
[47]高云峰,胡华,陈红洁,杨晓光.交叉口群交通控制实时评价模型仿真研究[J].系统仿真学报,2007,19(24)：5607-5612.
[48]徐建闽,许伦辉,撒元功.交叉口有交通信号控制时用户最优动态配流模型[J].控制理论与应用,2000,17(1)：117-120.
[49]徐丽群,杨兆升,贾正锐.信号控制对动态路线选择的影响研究[J].中国公路学报,2000,13(2)：99-101.
[50]李润梅,汤淑明.饱和路网中动态交通分配与路口控制一体化建模研究[J].系统仿真学报,2007：19(8)：1811-1815.
[51]连爱萍.城市动态网络交通流分配及相关问题的研究[D].北京：北京交通大学,2007：91-109.
[52]王浩.城市交通动态协调控制优化方法及实用技术研究[D].上海：同济大学,2008：37-65.
[53]郭金,黄崇超.交通信号控制的二层规矩模型与算法研究[J].数学杂志,2008.28(5)：559-564.
[54]卢守峰.基于元胞自动机的交通信号控制与路径诱导的协同系统[D].长春：吉林大学,2006：45-67.
[55]龙建成,高自友,任华玲.城市网络交通动态信号控制方法[J].中国公路学报,2009,22(4)：108-114,121.
[56]徐吉谦,过秀成.交通工程学基础[M].南京：东南大学出版社,1994：224-249.
[57]王殿海.交通流理论[M].北京：人民交通出版社,2002：7-28,60-77.
[58]丹尼尔L.鸠洛夫,马休J.休伯.交通流理论[M].北京：人民交通出版 社,1983：86-115,184-224.
[59]K. Gulik, L.P. Hurd, S. Yu. Computation theoretic aspects of cellular automata[J]. Physica D,1990,45:357-378.
[60]贾斌,高自友,李克平,李新刚.基于元胞自动机机地交通系统建模与模拟[M].北京：科学出版社,2007：15-30.
[61]PACEY G M. The progress of a bunch of vehicles released from a traffic signal, Report 2665[R]. Road Research Laboratory,1956.
[62]ROBERTSON D I. Transyt:a traffic network study tool, Report 253[R]. Road Research Laboratory,1969.
[63]王殿海,李凤,宋现敏.一种新的车队离散模及其应用[J].吉林大学学报(工学版),2009,39(4)：891-894.
[64]Lo, H. A cell-based traffic control formulation:strategies and benefits of dynamic timing plans[J]. Transportation Science,2001,35(2):148-164.
[65]Andy H.F. Chow, Hong K. Lo. Sensitivity analysis of signal control with physical queuing:Delay derivatives and an application[J]. Transportation Research Part B,2007,41(3):462-477.
[66]谭惠丽,黄乒花,李华兵,刘慕仁,孔令江.交通灯控制下主干路的交通流研究[J].物理学报,2003,52(5)：1127-1131.
[67]彭麟,谭惠丽,吴大艳,刘慕仁,孔令江.交通灯控制下城市主干路双车道多速元胞自动机交通流模型研究[J].物理学报,2004,53(9)：2899-2904.
[68]Makoto S. Wantanabe. Dynamical behavior of a two-dimensional cellular automaton with signal processing[J]. Physica A,2003,324(1-2):707-716.
[69]黄乒花,谭惠丽,孔令江,刘慕仁.开放边界条件下二维可转向主干路交通流模型的研究[J].物理学报,2005,54(7)：3044-3050.
[70]顾国庆,许伯铭,王秉宏,戴世强.随机化交通灯的二维元胞自动机交通模型[J].应用数学与力学,1998,19(9)：753-758.
[71]钱新建,许彦冰,顾国庆.二维元胞自动机交通流的绿波模型与交通灯效应[J].上海理工大学学报.2000,22(3)：207-210.
[72]Fukui M,Ishibashi Y.Traffic flow in 1D cellular automaton model including cars moving with high speed [J]. Journal of the Physical Society of Japan (Japan),1996,65 (6):1868-1870.
[73]Takashi Nagatani. Traffic state and fundamental diagram in cellular automaton model of vehicular traffic controlled by signals[J]. Physica A, 2009,388(8):1673-1681.
[74]张文修.粗糙集理论与方法[M].北京：科学出版社,2001：25-40.
[75]满江虹.基于粗糙集的分类知识发现方法及其应用研究[D].南京：东南大学,2005：13-17.
[76]黄德双.神经网络模式识别系统理论[M].北京：电子工业出版社,1996：254-276.
[77]Lee S C, Lee E T. Fuzzy Sets and Neural Networks[J].Journal of Cybernetics, 1974,4 (1):83-103.
[78]Amit Konar, Uday K. Chakraborty, Paul P. Wang. Supervised learning on a fuzzy Petri net[J].Information Sciences.2005,172:397-416.
[79]Pads A. Mastorocostas, John B.Theocharis. An Orthogonal Least-Squares Method for Recurrent Fuzzy-Neural Modeling[J].Fuzzy Sets and Systems,2003, 140(2):285-300.
[80]孙海蓉.模糊神经网络的研究及其应用[D].北京：华北电力大学,2006：17-30.
[81]Takagi T., Sugeno M.. Fuzzy identification of systems and its applications to modeling and control [J]. IEEE Trans System Man and Cybernetics,1985,15(1):116-132.
[82]张文修,梁怡.遗传算法的数学基础[M].西安交通大学出版社.2000.
[83]李敏强,寇纪淞,林丹等.遗传算法的基本理论与应用[M].科学出版社.2002.
[84]周明,孙树栋.遗传算法原理及应用[M].北京：国防工业出版社,1999：15-30.
[85]Lo, H. A cell-based traffic control formulation:strategies and benefits of dynamic timing plans[J]. Transportation Science,2001,35:148-164.
[86]汪湛.信号控制交叉口服务水平研究[D].上海：同济大学,2009：15-18.
[87]包振强,王宁生,李斌.专家知识库粗集建模中基于熵的数据离散化[J].数学的实践与认识,2003,33(8)：60-65.
[88]运士伟,张永胜.置换矩阵算法在粗糙集属性约简中的应用[J].计算机工程与应用,2009,45(13)：45-47.
[89]赵军,王国胤,吴中福,李华.基于粗集理论的数据离散化新算法[J].重庆大学学报(自然科学版),2002,25(3)：18-21.
[90]刘财辉.一种基于Rough集的数据约简方法[J].宁波大学学报(理工版),2007,20(3)：350-353.
[91]张红梅,王勇,王行愚.基于粗糙集理论的网络型入侵检测系统.计算机工程,2006,32(19)：29-30,33.
[92]Aleksander (?)hrn. ROSETTA Technical Reference Manual[R].Trondheim, Norway:Norwegian University of Science and Technology,2001:22-23.
[93]Staal Vinterbo, Aleksander (?)hrn.Minimal approximate hitting sets and rule templates[J]. International Journal of Approximate Reasoning,2000,25(2):123-143.
[94]孙即祥等.现代模式识别[M].长沙：国防科技大学出版社,2002：31-35.
[95]徐扬,秦克云,刘军,宋振明,吴建乐.模糊模式识别及其应用[M].成都：西南交通大学出版社,1999：104.
[96]田盈,赵阳.基于模糊模式识别的学生综合素质评价方法[J].重庆师范学院学报,2001(9)：92～94.
[97]陈祥光,裴旭东.人工神经网络技术及应用[M].北京：中国电力出版社,2003：3-20.
[98]黄德双.神经网络模式识别系统理论[M].北京：电子工业出版社,1996：3-10.
[99]赵喜林,赵喜玲,江祥奎.模式识别方法及其比较分析[J].信阳农业高等专科学校学报,2004(9)：37-40.
[100]TRB.Highway Capacity Manual[M]. America:Transportation Research Board,2000:35-38.
[101]马京辉.采用HCM2000新方法计算城市道路服务水平浅析[J].城市道桥与防洪,2009,7(7)：34-38.
[102]朱中,杨兆升.实时交通流量人工神经网络预测模型[J].中国公路学报,1998(10)：89～92.
[103]翟彦景.具有BP算法的模糊、神经网络在非线性动态系统辨别中的应用[D].曲阜：曲阜师范大学,2007：10-12.
[104]全永燊.城市交通控制[M].北京：人民交通出版社,1989：95-125.
[105]杨兆生,张树升.交通管理与控制[M].北京：人民交通出版 社,1997：101-122.
[106]Francois Dion, Hesham Pakha, Youn-Soo Kang. Comparison of delay estimates at under-saturated and over-saturated pre-timed signalized intersections [J]. Transportation Research Part B,2004,(38):99-122.
[107]赵雨旸,冯雨芹,杨忠良.信号交叉口Webster法延误计算修正模型[J].黑龙江工程学院学报(自然科学版),2010,24(2)：8-10,17.
[108]胡尧,韦纬,王登梅,田玲珲.信号交叉口随机控制延误模型研究[J].数学的实践与认识,2010,40(18)：153-158.
[109]张惠玲,李克平,孙剑.信号控制交叉口延误参数提取研究[J].合肥工业大学学报(自然科学版),2010,33(12)：1770-1774.
[110]Webster F.V., Cobbe B.M. Traffic signals[M]. London:Her Majesty's stationery office,1966.
[111]AKCELIK, R., ROUPHAIL, N.M.. Estimation of Delays at Traffic Signals for Variable Demand Conditions [J]. Transportation Research, Part B.1993,27(2): 109-131.
[112]Transportation Research Board.Highway capacity manual 2000 [M].4th ed. Washington D C:National Research Council,2000:16-19.
[113]王殿海,祁宏生,徐程,陈松.信号交叉口停车次数[J].吉林大学学报,2009,39(s2)：140-145.
[114]孙秀娟,刘希玉,李丽丽.基于自动识别交叉算子和自适应变异算子的遗传算法研究[J].信息技术与信息化,2008(1)：55-57.
[115]全永燊.城市交通控制[M].北京：人民交通出版社,1989：129-157.
[116]马楠,邵春福,赵熠.基于双向绿波带宽最大化的交叉口信号协调控制优化[J].吉林大学学报(工学版),2009,39(S2)：19-24.
[117]Zhang L H, Yin Y F. Robust synchronization of actuated signals on arterials[J]. Journal of Transportation Research Board,2008,2080(13):111-119.
[118]Natale P, Gartano F. Maximal bandwidth problems:a new algorithm based on the properties of periodicity of the system[J]. Transportation Research B, 1998,32(4):277-288.
[119]Little J.The synchronization of traffic signal by mixed-integer linear programming [J]. Operation research,1966,14(4):568-594.
[120]马永光.城市交通干线信号优化控制方法的研究[D].天津：天津大 学,2007.
[121]徐建伟.基于免疫算法的城市干线交通信号协调控制研究[D].湘潭：湘潭大学,2008.
[122]马强.城市干线协调控制与Petri网建模仿真[D].武汉：华中科技大学,,2007.
[123]马健.城市交通干线交叉口间递阶模糊神经控制算法的仿真研究[D].南京：河海大学,2006.
[124]沈国江,许卫明.交通干线动态双向绿波带控制技术研究[J].浙江大学学报(工学版),2008,42(9)：1625-1630.
[125]龙建成,高自友,任华玲.城市网络交通动态信号控制方法[J].中国公路学报,2009,22(4)：108-114.
[126]Chang T H, Sun G Y. Modeling and optimization of an oversaturated signalized network[J]. Transportation Research Part B,2004,38:687-707.
[127]吴震.基于仿真的干线协调控制分析指标[J].武汉理工大学学报(交通科学与工程版),2009,22(2)：349-352.
[2]Chard B M and LineX CJ. TRANSYT:The latest development[J].Traffic Engineering and control,1987,28:32-35.
[3]刘智勇.智能交通控制理论及其应用[M].北京：科学出版社,2003：2-20.
[4]全永燊.城市交通控制[M].北京：人民交通出版社,1989：157-198、226-266.
[5]Hunt. P. Betal. The SCOOT online traffic signal optimization technique[J]. Traffic Engineering & Control,1982,23:190-192.
[6]Martin PT and Hockaday S L M. SCOOT-An update[J].ITE Journal,1995, 65(1):44-48.
[7]Hunt. P. Betal, Robertson D I, Bretherton R D.et. SCOOT-A traffic responsive method of coordinating signals[R]. TRRL Report I.R 1014, Crowthorne1981.
[8]Lowrie P R. SCATS principles, methodologies, algorithm[C]. IEE Conference on Road Traffic Signal.London:IEE Publication,1982:67-70.
[9]全永燊.城市交通控制[M].北京：人民交通出版社,1989：3-4.
[10]彭维.城市交通信号智能控制方法研究[D].长春：吉林大学,2007：27-45.
[11]蔡蕾.城市平面交叉路口交通信号优化控制[D].长春：吉林大学,2007：18-40.
[12]Burmeister B, Haddadi A, Matylis G. Application of multi_agent system in traffic and transportation [J]. IEEE Proceeding_Software Engineering,1997,144 (1):51-60.
[13]Laichour H, Maouche S, Mandiau R. Traffic control assistance in connection nodes:Muti-agent application in urban transport systems [C]. Ukraine:International Workshop on Intelligent Data Application and Advanced Computing System: Technology and Application,2001:133-137.
[14]Bingham E. Reinforcement learning in neuro-fuzzy traffic signal control [J]. European Journal of Operational Research,2001,131(2):232-241.
[15]魏武,张起森,王明俊,黄中祥.一种基于模糊逻辑的城市交叉口交通信号控制方法[J].交通运输工程学报,2001(2)：99-102.
[16]陈阳舟,张辉,杨玉珍,胡全连.基于Q学习的Agent在单路口交通控制中的应用[J].公路交通科技,2007(5)：117-120.
[17]万伟,陈峰.基于遗传算法的单交叉口信号优化控制[J].计算机工程,2007,33(16)：217-219.
[18]王秋平,谭学龙,张生瑞.城市单点交叉口信号配时优化.交通运输工程学报,2006(2)：60-64.
[19]赵伟,高凤玲,张毅.道路交叉口交通控制模拟系统[J].交通科技.2004(3)：98-100.
[20]吴义虎,喻丹,何霞,郭文莲.单路口低饱和交通流的多相位混沌控制仿真研究[J].系统仿真技术,2007,3(2)：63-67.
[21]贺国光,崔岩,王桂珠.单路口交通流控制动态响应的仿真研究[J].系统工程学报,2005,20(3)：323-329.
[22]Halim Ceylan, Michael G.H.Bell. Traffic signal timing optimization based on genetic algorithm approach, including drivers'routing [J].Transportation Research Part B,2004,38 (2):329-342.
[23]靳文舟,黄一峰,荣利利,李俊辉,刘颖杰.基于粒子群理论的干道协调控制优化研究[J].交通与计算机,2008,26(1)：31-35.
[24]吴恩,杨晓光,吴震,常云涛.基于遗传算法的干线协调控制参数共同优化[J].同济大学学报(自然科学版),2008,36(7)：921-926.
[25]陈娟,徐立鸿,袁长亮.分层控制算法在过饱和交通干线控制中的应用[J].系统仿真学报,2008,20(15)：4122-4137,4131.
[26]赵晓华,李振龙,于泉,荣建.基于Q学习算法的两交叉口信号灯博弈协调控制[J].系统仿真学报,2007,19(18)：4253-4256.
[27]朱文兴,贾磊.城市主干路交通流多目标优化控制[J].山东大学学报(工学版),2004,34(4)：72-78.
[28]Gartner N H, Stamatiadis C. Arterial-based control of traffic flow in urban grid networks [J]. Mathematics and Computer Modelling,2002,35(5):657-671.
[29]杨晓芳.基于模糊控制的城市交通信号控制系统的研究[D].西安：长安大学,2003：18-40.
[30]Nathan H. Gartner, John D.C. Little, Henry Gabbay. Optimization of traffic signal settings by mixed integer linear programming Part I:the network coordination problem[J]. Transportation Science,1975,9(4):321-343.
[31]Gartner, N.H., Little, J.D.C., Gabbay, H.. Optimization of traffic signal settings by mixed integer linear programming. Part Ⅱ:the network synchronization problem[J]. Transportation Science,1975,9(4):344-363.
[32]Little, J.D.C., Kelson, M.D., Gartner, N.H..MAXBAND:a program for setting signals on arterials and triangular networks[J]. Transportation Research Record,1981,795:40-46.
[33]Cohen, S.L., Liu, C.C.. The bandwidth-constrained TRANSYT signal optimization program[J].Transportation Research Record,1986,1057:1-7.
[34]Gartner, N.H., Assmann, S.F., Lasaga, F.L., Hou, D.L... A multi-band approach to arterial traffic signal optimization[J]. Transportation Research Part B,1991,25 (1):55-74.
[35]Chaudhary, N.A., Messer, C.J.. PASSER-Ⅳ:a program for optimizing signal timing in grid networks[C].Washington,D.C.:72nd Annual Meeting of the Transportation Research Board,1993:82-93.
[36]Allsop R. E.. Some possibilities for using traffic control to influence trip destinations and route choice[C]. Sydney, Australia:Proceedings of the Sixth International Symposium on Transportation and Traffic Theory,1974:345-374.
[37]Nathan H. Gartner. Area traffic control and network equilibrium[R]. Boston: Massachusetts Institute of Technology,1975:1-26.
[38]Hai Yang. Traffic assignment and signal control in saturated road networks[J]. Transportion Research A,1995,29(2):125-139.
[39]Wann-Ming Wey. Model formulation and solution algorithm of traffic signal control in an urban network[J]. Computers, Environment and Urban Systems,2000,24,(4):355-378.
[40]Hong K. Lo, Elbert Chang, Yiu Cho Chan. Dynamic network traffic control[J].Transportation Research Part A:Policy and Practice,2001,35(8):721-744.
[41]Ernesto Cipriani, Gaetano Fusco. Combined signal setting design and traffic assignment problem[J]. European Journal of operation research. 2004,155(3):569-583.
[42]Mariagrazia Dotoli, Maria Pia Fanti, Carlo Meloni. A signal timing plan formulation for urban traffic control [J]. Control Engineering Practice, 2006,14(11):1297-1311.
[43]石建军,于泉,任福田.大城市交通信号控制系统层次与区域动态划分的研究[J].道路交通与安全,2004,(4)：7-9.
[44]高海军,俞国军,李振龙.基于agent的城市交通信号控制[J].控制与决策,2004,19(7)：737-740.
[45]傅惠,徐建闽,卢凯.基于粒子群优化的关联交叉口群信号控制策略研究[J].交通与计算机,2007,25(3)：23-26.
[46]Hua Jiuyi, Ardeshir Faghri. Dynamic Traffic Pattern Classification Using Artificial Neural Networks[J]. Transportation Research Record,1993,1399:14-19.
[47]高云峰,胡华,陈红洁,杨晓光.交叉口群交通控制实时评价模型仿真研究[J].系统仿真学报,2007,19(24)：5607-5612.
[48]徐建闽,许伦辉,撒元功.交叉口有交通信号控制时用户最优动态配流模型[J].控制理论与应用,2000,17(1)：117-120.
[49]徐丽群,杨兆升,贾正锐.信号控制对动态路线选择的影响研究[J].中国公路学报,2000,13(2)：99-101.
[50]李润梅,汤淑明.饱和路网中动态交通分配与路口控制一体化建模研究[J].系统仿真学报,2007：19(8)：1811-1815.
[51]连爱萍.城市动态网络交通流分配及相关问题的研究[D].北京：北京交通大学,2007：91-109.
[52]王浩.城市交通动态协调控制优化方法及实用技术研究[D].上海：同济大学,2008：37-65.
[53]郭金,黄崇超.交通信号控制的二层规矩模型与算法研究[J].数学杂志,2008.28(5)：559-564.
[54]卢守峰.基于元胞自动机的交通信号控制与路径诱导的协同系统[D].长春：吉林大学,2006：45-67.
[55]龙建成,高自友,任华玲.城市网络交通动态信号控制方法[J].中国公路学报,2009,22(4)：108-114,121.
[56]徐吉谦,过秀成.交通工程学基础[M].南京：东南大学出版社,1994：224-249.
[57]王殿海.交通流理论[M].北京：人民交通出版社,2002：7-28,60-77.
[58]丹尼尔L.鸠洛夫,马休J.休伯.交通流理论[M].北京：人民交通出版 社,1983：86-115,184-224.
[59]K. Gulik, L.P. Hurd, S. Yu. Computation theoretic aspects of cellular automata[J]. Physica D,1990,45:357-378.
[60]贾斌,高自友,李克平,李新刚.基于元胞自动机机地交通系统建模与模拟[M].北京：科学出版社,2007：15-30.
[61]PACEY G M. The progress of a bunch of vehicles released from a traffic signal, Report 2665[R]. Road Research Laboratory,1956.
[62]ROBERTSON D I. Transyt:a traffic network study tool, Report 253[R]. Road Research Laboratory,1969.
[63]王殿海,李凤,宋现敏.一种新的车队离散模及其应用[J].吉林大学学报(工学版),2009,39(4)：891-894.
[64]Lo, H. A cell-based traffic control formulation:strategies and benefits of dynamic timing plans[J]. Transportation Science,2001,35(2):148-164.
[65]Andy H.F. Chow, Hong K. Lo. Sensitivity analysis of signal control with physical queuing:Delay derivatives and an application[J]. Transportation Research Part B,2007,41(3):462-477.
[66]谭惠丽,黄乒花,李华兵,刘慕仁,孔令江.交通灯控制下主干路的交通流研究[J].物理学报,2003,52(5)：1127-1131.
[67]彭麟,谭惠丽,吴大艳,刘慕仁,孔令江.交通灯控制下城市主干路双车道多速元胞自动机交通流模型研究[J].物理学报,2004,53(9)：2899-2904.
[68]Makoto S. Wantanabe. Dynamical behavior of a two-dimensional cellular automaton with signal processing[J]. Physica A,2003,324(1-2):707-716.
[69]黄乒花,谭惠丽,孔令江,刘慕仁.开放边界条件下二维可转向主干路交通流模型的研究[J].物理学报,2005,54(7)：3044-3050.
[70]顾国庆,许伯铭,王秉宏,戴世强.随机化交通灯的二维元胞自动机交通模型[J].应用数学与力学,1998,19(9)：753-758.
[71]钱新建,许彦冰,顾国庆.二维元胞自动机交通流的绿波模型与交通灯效应[J].上海理工大学学报.2000,22(3)：207-210.
[72]Fukui M,Ishibashi Y.Traffic flow in 1D cellular automaton model including cars moving with high speed [J]. Journal of the Physical Society of Japan (Japan),1996,65 (6):1868-1870.
[73]Takashi Nagatani. Traffic state and fundamental diagram in cellular automaton model of vehicular traffic controlled by signals[J]. Physica A, 2009,388(8):1673-1681.
[74]张文修.粗糙集理论与方法[M].北京：科学出版社,2001：25-40.
[75]满江虹.基于粗糙集的分类知识发现方法及其应用研究[D].南京：东南大学,2005：13-17.
[76]黄德双.神经网络模式识别系统理论[M].北京：电子工业出版社,1996：254-276.
[77]Lee S C, Lee E T. Fuzzy Sets and Neural Networks[J].Journal of Cybernetics, 1974,4 (1):83-103.
[78]Amit Konar, Uday K. Chakraborty, Paul P. Wang. Supervised learning on a fuzzy Petri net[J].Information Sciences.2005,172:397-416.
[79]Pads A. Mastorocostas, John B.Theocharis. An Orthogonal Least-Squares Method for Recurrent Fuzzy-Neural Modeling[J].Fuzzy Sets and Systems,2003, 140(2):285-300.
[80]孙海蓉.模糊神经网络的研究及其应用[D].北京：华北电力大学,2006：17-30.
[81]Takagi T., Sugeno M.. Fuzzy identification of systems and its applications to modeling and control [J]. IEEE Trans System Man and Cybernetics,1985,15(1):116-132.
[82]张文修,梁怡.遗传算法的数学基础[M].西安交通大学出版社.2000.
[83]李敏强,寇纪淞,林丹等.遗传算法的基本理论与应用[M].科学出版社.2002.
[84]周明,孙树栋.遗传算法原理及应用[M].北京：国防工业出版社,1999：15-30.
[85]Lo, H. A cell-based traffic control formulation:strategies and benefits of dynamic timing plans[J]. Transportation Science,2001,35:148-164.
[86]汪湛.信号控制交叉口服务水平研究[D].上海：同济大学,2009：15-18.
[87]包振强,王宁生,李斌.专家知识库粗集建模中基于熵的数据离散化[J].数学的实践与认识,2003,33(8)：60-65.
[88]运士伟,张永胜.置换矩阵算法在粗糙集属性约简中的应用[J].计算机工程与应用,2009,45(13)：45-47.
[89]赵军,王国胤,吴中福,李华.基于粗集理论的数据离散化新算法[J].重庆大学学报(自然科学版),2002,25(3)：18-21.
[90]刘财辉.一种基于Rough集的数据约简方法[J].宁波大学学报(理工版),2007,20(3)：350-353.
[91]张红梅,王勇,王行愚.基于粗糙集理论的网络型入侵检测系统.计算机工程,2006,32(19)：29-30,33.
[92]Aleksander (?)hrn. ROSETTA Technical Reference Manual[R].Trondheim, Norway:Norwegian University of Science and Technology,2001:22-23.
[93]Staal Vinterbo, Aleksander (?)hrn.Minimal approximate hitting sets and rule templates[J]. International Journal of Approximate Reasoning,2000,25(2):123-143.
[94]孙即祥等.现代模式识别[M].长沙：国防科技大学出版社,2002：31-35.
[95]徐扬,秦克云,刘军,宋振明,吴建乐.模糊模式识别及其应用[M].成都：西南交通大学出版社,1999：104.
[96]田盈,赵阳.基于模糊模式识别的学生综合素质评价方法[J].重庆师范学院学报,2001(9)：92～94.
[97]陈祥光,裴旭东.人工神经网络技术及应用[M].北京：中国电力出版社,2003：3-20.
[98]黄德双.神经网络模式识别系统理论[M].北京：电子工业出版社,1996：3-10.
[99]赵喜林,赵喜玲,江祥奎.模式识别方法及其比较分析[J].信阳农业高等专科学校学报,2004(9)：37-40.
[100]TRB.Highway Capacity Manual[M]. America:Transportation Research Board,2000:35-38.
[101]马京辉.采用HCM2000新方法计算城市道路服务水平浅析[J].城市道桥与防洪,2009,7(7)：34-38.
[102]朱中,杨兆升.实时交通流量人工神经网络预测模型[J].中国公路学报,1998(10)：89～92.
[103]翟彦景.具有BP算法的模糊、神经网络在非线性动态系统辨别中的应用[D].曲阜：曲阜师范大学,2007：10-12.
[104]全永燊.城市交通控制[M].北京：人民交通出版社,1989：95-125.
[105]杨兆生,张树升.交通管理与控制[M].北京：人民交通出版 社,1997：101-122.
[106]Francois Dion, Hesham Pakha, Youn-Soo Kang. Comparison of delay estimates at under-saturated and over-saturated pre-timed signalized intersections [J]. Transportation Research Part B,2004,(38):99-122.
[107]赵雨旸,冯雨芹,杨忠良.信号交叉口Webster法延误计算修正模型[J].黑龙江工程学院学报(自然科学版),2010,24(2)：8-10,17.
[108]胡尧,韦纬,王登梅,田玲珲.信号交叉口随机控制延误模型研究[J].数学的实践与认识,2010,40(18)：153-158.
[109]张惠玲,李克平,孙剑.信号控制交叉口延误参数提取研究[J].合肥工业大学学报(自然科学版),2010,33(12)：1770-1774.
[110]Webster F.V., Cobbe B.M. Traffic signals[M]. London:Her Majesty's stationery office,1966.
[111]AKCELIK, R., ROUPHAIL, N.M.. Estimation of Delays at Traffic Signals for Variable Demand Conditions [J]. Transportation Research, Part B.1993,27(2): 109-131.
[112]Transportation Research Board.Highway capacity manual 2000 [M].4th ed. Washington D C:National Research Council,2000:16-19.
[113]王殿海,祁宏生,徐程,陈松.信号交叉口停车次数[J].吉林大学学报,2009,39(s2)：140-145.
[114]孙秀娟,刘希玉,李丽丽.基于自动识别交叉算子和自适应变异算子的遗传算法研究[J].信息技术与信息化,2008(1)：55-57.
[115]全永燊.城市交通控制[M].北京：人民交通出版社,1989：129-157.
[116]马楠,邵春福,赵熠.基于双向绿波带宽最大化的交叉口信号协调控制优化[J].吉林大学学报(工学版),2009,39(S2)：19-24.
[117]Zhang L H, Yin Y F. Robust synchronization of actuated signals on arterials[J]. Journal of Transportation Research Board,2008,2080(13):111-119.
[118]Natale P, Gartano F. Maximal bandwidth problems:a new algorithm based on the properties of periodicity of the system[J]. Transportation Research B, 1998,32(4):277-288.
[119]Little J.The synchronization of traffic signal by mixed-integer linear programming [J]. Operation research,1966,14(4):568-594.
[120]马永光.城市交通干线信号优化控制方法的研究[D].天津：天津大 学,2007.
[121]徐建伟.基于免疫算法的城市干线交通信号协调控制研究[D].湘潭：湘潭大学,2008.
[122]马强.城市干线协调控制与Petri网建模仿真[D].武汉：华中科技大学,,2007.
[123]马健.城市交通干线交叉口间递阶模糊神经控制算法的仿真研究[D].南京：河海大学,2006.
[124]沈国江,许卫明.交通干线动态双向绿波带控制技术研究[J].浙江大学学报(工学版),2008,42(9)：1625-1630.
[125]龙建成,高自友,任华玲.城市网络交通动态信号控制方法[J].中国公路学报,2009,22(4)：108-114.
[126]Chang T H, Sun G Y. Modeling and optimization of an oversaturated signalized network[J]. Transportation Research Part B,2004,38:687-707.
[127]吴震.基于仿真的干线协调控制分析指标[J].武汉理工大学学报(交通科学与工程版),2009,22(2)：349-352.