考虑排放因素的城市交叉口交通信号控制策略的研究
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摘要
交通是现代社会的基础,是人类社会经济的命脉,人们的社会行为与交通息息相关。在我国,由于机动车保有量迅速增加、交通设施建设滞后以及管理措施不够完善等原因,交叉路口的交通堵塞现象日趋严重,从而影响到城市路网通行能力的发挥。车辆在交叉路口处反复地分流、合流及交叉,交通状况复杂,使得交叉路口已经成为制约城市道路交通功能的瓶颈。维持城市交通正常运转,需要加强交通控制与管理,积极开展对交叉路口的研究,努力提高交叉路口的通行能力。交通信号控制是运输网络的一种基本管理手段,随着智能运输系统(Intelligent Transponation System,ITS)的发展,交通信号控制的智能化研究越来越受到重视。到目前为止,已经研究了许多交通信号控制模型。但是目前这些研究建立的模型大多数都是针对交通流的,没有将其他因素考虑在内。
随着城市交通和社会经济的日益发展,汽车的保有量不断创下新高,大量机动车的出现在造成严重的交通拥挤和堵塞的同时,也引起了严重的空气污染和噪声污染。据权威统计分析,主要大城市大气污染物中机动车排放分担率呈明显上升趋势。如何在发展经济的同时,建立一个人、车、路、环境和谐发展的交通体系,这个问题已经被认为是全球最严重也是最具挑战性的课题之一。因此建立一个同时兼顾交通流情况和机动车尾气排放情况的交通信号控制模型具有重要的理论意义和实用价值。
论文总结了国内外城市交通信号控制的研究进展,分析了该领域研究的发展趋势。在此基础上,针对城市交通信号优化控制问题进行了智能优化的理论分析和应用方法的研究。提出了考虑排放因素的城市交通信号优化控制系统的架构、建模方法和求解算法。通过对城市信号控制路口的交通流分析,从单点两相位、单点多相位和干线协调控制的不同层面,建立了城市交通信号优化控制的双层多目标规划模型。采用遗传算法和遗传蚁群融合算法对模型进行了仿真试验,通过与传统仿真试验结果进行比较,证明了这两种优化算法的优越性,其中,遗传蚁群融合算法优化性能更好。
本论文的主要研究工作总结如下:
(1)设计了考虑排放因素的交通信号优化控制系统框架。
通过对城市交叉口交通信号控制发展现状和趋势的分析,将双层规划模型的研究方法与考虑排放因素的交通信号配时控制联系起来进行研究,设计了交通信号优化控制系统的架构,指出信号优化模块是交通信号优化控制系统的核心,其实质是交通信号控制模型及其求解算法。
(2)从单点控制和协调控制的角度,建立了城市交通信号控制的双层多目标规划模型。
以双层规划模型为工具建立了交通信号控制模型,分别对上下层模型进行详细的问题描述和数学表达。其中,路网中的机动车尾气排放量作为一个优化目标,嵌入上层模型的目标函数中。模型以路网中的车辆延误和尾气排放总量为性能指标,通过改变信号控制策略,在保证交通基本畅通的前提下,将城市机动车尾气排放量限制在一定范围内。
(3)提出并实现了求解函数优化问题的遗传蚁群融合算法。
遗传蚁群融合算法的基本思路是首先采用遗传算法产生有关问题的初始信息素分布;然后采用蚂蚁算法,在有一定初始信息素分布的情况下,充分利用蚂蚁算法的并行性、正反馈机制以及求解效率高等特性,高速高效地得到函数的优化解。本文以Camel函数作为测试函数,同时还以单点两相位信号优化控制模型的求解为例,通过与传统优化方法的仿真试验进行比较,采用遗传蚁群融合算法可获得最优性能指标,证明该算法是一种时间效率和求解效率都比较好的求解函数优化问题的有效算法。
(4)设计并实现了基于启发式遗传算法的模型求解方法。
在利用惩罚策略处理双层多目标规划模型约束条件的基础上,针对具体建立的交通信号控制模型,设计了基于启发式遗传算法的模型求解方法,并运用MATLAB软件对所设计的城市单交叉口交通信号控制的双层多目标规划模型进行实时仿真,仿真表明能达到良好的控制效果。
(5)完成典型城市路网交通流情况的仿真。
利用VISSIM交通仿真软件对典型城市路网的交通流情况进行仿真运行,得出了在固定信号配时方案下各项模拟输出的结果。其结果一方面可以作为参数代入典型城市路网交通信号控制的双层多目标规划模型,另一方面可以作为比较值验证以上两种优化算法的优化性能。
Traffic is the foundation and the economic lifelines of the modern society. In China, the quantity of vehicles increases rapidly, at the same time, traffic infrastructure and management methods have some shortages. As a result, the traffic congestions in intersections become more and more serious. It makes intersections have become bottlenecks of the urban traffic. Traffic signal is an essential element to manage the transportation network. Nevertheless, it is widely accepted that the benefits of traffic control signal systems are not being fully practiced. Along with the development of Intelligent Transportation System (ITS), the research on traffic signal control is by no means complete and traffic signal control remains one of the most heavily funded research and development items. A number of models of the traffic signal control have been developed in the past. However, those models mainly focus on the traffic flow and don't take other factors into account.
With the development of urban transportation, large numbers of vehicles cause serious air pollution and noise pollution. The authoritative statistics shows that the share rate of vehicles exhaust emission to atmosphere has an obvious increasing tendency. This problem, how to establish an harmonious traffic systems of human, vehicles, roads and environment, has been recognized as one of the most critical, yet challenging problems for the world. Therefore, it has practical significance to establish a new traffic signal control model based on the condition of the traffic flow and vehicle exhaust emissions.
In this dissertation, the research of theoretical analysis and application methods are given on base of summarizing the domestic and overseas research progress and analyzing the development trends of urban traffic signal control. The principle of the traffic signal control by intelligent optimization, mainly the basic optimal theories and the methods of Genetic Algorithm as well as Ant Colony Algorithm are proposed firstly. Through the study and the analysis of traffic streams in an intersection, a bi-level multi-object optimization model of traffic signal control in an intersection is established. The simulation tests are conducted using Genetic Algorithm and The Fusion Algorithm of Genetic and Ant Colony. The simulation results show that the optimal algorithms are superior to the traditional methods, and the fusion algorithm is most suitable for determining the green split in a single intersection.
The main achievements of the dissertation are as follow:
(1) A bi-level multi-object model is established for optimizing the signal cycle length and green time by considering the constraint of automotive exhaust emission. The performance index function for optimization is defined to improve traffic quality and reduce emission at intersections. The research tries to limit the range of vehicle exhaust emissions on the premise of unimpeded transport by changing the traffic signal control strategy.
(2) The heuristic genetic algorithm is designed to solve the problem, simplifying the model by means of penalty strategy. Subsequently MATLAB program is given to simulate the solution process. The simulation results show that very nice effects are obtained.
(3) The fusion algorithm of genetic and Ant Colony applied in solving the function optimization problem is presented. By comparing with other solution methods, the fusion algorithm has the best performance.
(4) The typical topological structure of urban road network is designed by combining the features of traffic signal control with the study perspective of this research.
(5) Traffic simulation system (VISSIM) is used to simulate the situation of traffic flow in the typical urban road network and gains the results on the condition of fix signal timing plan. The function of the results is substituting into the model as parameters and verifying the optimization effect of above algorithms.
随着城市交通和社会经济的日益发展,汽车的保有量不断创下新高,大量机动车的出现在造成严重的交通拥挤和堵塞的同时,也引起了严重的空气污染和噪声污染。据权威统计分析,主要大城市大气污染物中机动车排放分担率呈明显上升趋势。如何在发展经济的同时,建立一个人、车、路、环境和谐发展的交通体系,这个问题已经被认为是全球最严重也是最具挑战性的课题之一。因此建立一个同时兼顾交通流情况和机动车尾气排放情况的交通信号控制模型具有重要的理论意义和实用价值。
论文总结了国内外城市交通信号控制的研究进展,分析了该领域研究的发展趋势。在此基础上,针对城市交通信号优化控制问题进行了智能优化的理论分析和应用方法的研究。提出了考虑排放因素的城市交通信号优化控制系统的架构、建模方法和求解算法。通过对城市信号控制路口的交通流分析,从单点两相位、单点多相位和干线协调控制的不同层面,建立了城市交通信号优化控制的双层多目标规划模型。采用遗传算法和遗传蚁群融合算法对模型进行了仿真试验,通过与传统仿真试验结果进行比较,证明了这两种优化算法的优越性,其中,遗传蚁群融合算法优化性能更好。
本论文的主要研究工作总结如下:
(1)设计了考虑排放因素的交通信号优化控制系统框架。
通过对城市交叉口交通信号控制发展现状和趋势的分析,将双层规划模型的研究方法与考虑排放因素的交通信号配时控制联系起来进行研究,设计了交通信号优化控制系统的架构,指出信号优化模块是交通信号优化控制系统的核心,其实质是交通信号控制模型及其求解算法。
(2)从单点控制和协调控制的角度,建立了城市交通信号控制的双层多目标规划模型。
以双层规划模型为工具建立了交通信号控制模型,分别对上下层模型进行详细的问题描述和数学表达。其中,路网中的机动车尾气排放量作为一个优化目标,嵌入上层模型的目标函数中。模型以路网中的车辆延误和尾气排放总量为性能指标,通过改变信号控制策略,在保证交通基本畅通的前提下,将城市机动车尾气排放量限制在一定范围内。
(3)提出并实现了求解函数优化问题的遗传蚁群融合算法。
遗传蚁群融合算法的基本思路是首先采用遗传算法产生有关问题的初始信息素分布;然后采用蚂蚁算法,在有一定初始信息素分布的情况下,充分利用蚂蚁算法的并行性、正反馈机制以及求解效率高等特性,高速高效地得到函数的优化解。本文以Camel函数作为测试函数,同时还以单点两相位信号优化控制模型的求解为例,通过与传统优化方法的仿真试验进行比较,采用遗传蚁群融合算法可获得最优性能指标,证明该算法是一种时间效率和求解效率都比较好的求解函数优化问题的有效算法。
(4)设计并实现了基于启发式遗传算法的模型求解方法。
在利用惩罚策略处理双层多目标规划模型约束条件的基础上,针对具体建立的交通信号控制模型,设计了基于启发式遗传算法的模型求解方法,并运用MATLAB软件对所设计的城市单交叉口交通信号控制的双层多目标规划模型进行实时仿真,仿真表明能达到良好的控制效果。
(5)完成典型城市路网交通流情况的仿真。
利用VISSIM交通仿真软件对典型城市路网的交通流情况进行仿真运行,得出了在固定信号配时方案下各项模拟输出的结果。其结果一方面可以作为参数代入典型城市路网交通信号控制的双层多目标规划模型,另一方面可以作为比较值验证以上两种优化算法的优化性能。
Traffic is the foundation and the economic lifelines of the modern society. In China, the quantity of vehicles increases rapidly, at the same time, traffic infrastructure and management methods have some shortages. As a result, the traffic congestions in intersections become more and more serious. It makes intersections have become bottlenecks of the urban traffic. Traffic signal is an essential element to manage the transportation network. Nevertheless, it is widely accepted that the benefits of traffic control signal systems are not being fully practiced. Along with the development of Intelligent Transportation System (ITS), the research on traffic signal control is by no means complete and traffic signal control remains one of the most heavily funded research and development items. A number of models of the traffic signal control have been developed in the past. However, those models mainly focus on the traffic flow and don't take other factors into account.
With the development of urban transportation, large numbers of vehicles cause serious air pollution and noise pollution. The authoritative statistics shows that the share rate of vehicles exhaust emission to atmosphere has an obvious increasing tendency. This problem, how to establish an harmonious traffic systems of human, vehicles, roads and environment, has been recognized as one of the most critical, yet challenging problems for the world. Therefore, it has practical significance to establish a new traffic signal control model based on the condition of the traffic flow and vehicle exhaust emissions.
In this dissertation, the research of theoretical analysis and application methods are given on base of summarizing the domestic and overseas research progress and analyzing the development trends of urban traffic signal control. The principle of the traffic signal control by intelligent optimization, mainly the basic optimal theories and the methods of Genetic Algorithm as well as Ant Colony Algorithm are proposed firstly. Through the study and the analysis of traffic streams in an intersection, a bi-level multi-object optimization model of traffic signal control in an intersection is established. The simulation tests are conducted using Genetic Algorithm and The Fusion Algorithm of Genetic and Ant Colony. The simulation results show that the optimal algorithms are superior to the traditional methods, and the fusion algorithm is most suitable for determining the green split in a single intersection.
The main achievements of the dissertation are as follow:
(1) A bi-level multi-object model is established for optimizing the signal cycle length and green time by considering the constraint of automotive exhaust emission. The performance index function for optimization is defined to improve traffic quality and reduce emission at intersections. The research tries to limit the range of vehicle exhaust emissions on the premise of unimpeded transport by changing the traffic signal control strategy.
(2) The heuristic genetic algorithm is designed to solve the problem, simplifying the model by means of penalty strategy. Subsequently MATLAB program is given to simulate the solution process. The simulation results show that very nice effects are obtained.
(3) The fusion algorithm of genetic and Ant Colony applied in solving the function optimization problem is presented. By comparing with other solution methods, the fusion algorithm has the best performance.
(4) The typical topological structure of urban road network is designed by combining the features of traffic signal control with the study perspective of this research.
(5) Traffic simulation system (VISSIM) is used to simulate the situation of traffic flow in the typical urban road network and gains the results on the condition of fix signal timing plan. The function of the results is substituting into the model as parameters and verifying the optimization effect of above algorithms.
引文
[1]Wootton J.R.,Garcia-Ortiz A..Intelligent transportation system:a global perspective[J].Math.Comput.Modeling,1995,22(4):259-268.
[2]毛保华,杨肇夏,陈海波.道路交通仿真技术与系统研究[J].北方交通大学学报,2002,26(5):37-46.
[3]陆化普.城市交通现代化管理[M].北京:人民交通出版社,1999.
[4]上海市环境科学研究院.上海交通现状与机动车污染状况及污染暴露-反应关系[R].2005.
[5]贺国光.发展智能交通促进一系列产业的发展[J].信息系统工程,1999(6):7-8.
[6]尹宏宾,徐建闽.道路交通控制技术[M].广州:华南理工大学出版社,2000.
[7]钟国文,朱劲.单交叉路口的模糊智能交通控制系统[J].广西大学学报:自然科学版,2004(9):175-180.
[8]许伦辉,衷路生,徐建闽.基于神经网络实现的交叉口多相位模糊逻辑控制[J].系统工程理论与实践,2004(7):135-140.
[9]承向军,杨肇夏.一种交通信号自学习控制方法及仿真实现[J].系统仿真学报,2004,16(7):1519-1524.
[10]Findler N U.Multiagent coordination and cooperation in a distributed dynamic environment with limited resources[J].Artificial Intelligent in Engineering,1995,9:229-238.
[11]France John,Ghorbani Ali A.A multiagent system for optimizing urban traffic[C].Proceeding of the IEEE/W IC International Conference on Intelligent Agent Technology,2003.
[12]陆小芳,郑应平,王令群.交通信号控制系统的多agent协调研究[J].计算机工程与应用,2006(2):188-190.
[13]邱凌云,陈锋,何兵兵.基于Agent的驾驶员-车辆建模研究与实现[J].计算机仿真,2005(11):222-225.
[14]Ferrari P.Road pricing and network equilibrium[J].Transportation Research,1995(29B):357-372.
[15]Yang H,Bell M G H.Traffic restraint,road pricing and network equilibrium[J].Transportation Research,1997(31B):303-314.
[16]Gao Z Y,Song Y F.A reserve capacity model of optimal signal control with user-equilibrium route choice[J].Transportation Research,2002(36B):313-323.
[17]Rilett L R,Benedek C M.Traffic assignment under environmental and equity objectives[J].Transportation Research,1994(36B):176-190.
[18]杨文国,高自友.考虑环境因素的广义用户平衡和广义系统最优配流模型[J].中国公 路学报,2003,16(4):72-76.
[19]赵彤,高自友.环境污染限制及最优信号控制条件下的综合离散网络设计问题[J].土木工程学报,2006,39(2):102-106.
[20]Hallmark S.L.,Fomunung I.,Guensler R.,et al.Assessing impacts of improved signal timing and transportation control measure using an activity-specific modeling approach[J].Transportation Research Record,2000(1738):49-55.
[21]Li X.,Li G.,Pang,S.,Yang X.,et al.Signal timing of intersections using integrated optimization of traffic quality,emissions and fuel consumption[J].Transportation Research,2004(9D):401-407.
[22]Allsop R E.Some possibilities for using traffic control to influence trip destinations and route choice[C].Proceedings of 6th International Symposium on Transportation and Traffic Theory.Sydney,Australia:1974:345-374.
[23]Ma Shoufeng,He Guoguang,Wang Shitong.A hierarchical coordination model for control-guidance integrated systems in ITS[C].Proceedings of IEEE International Conference on Intelligent Transportation System.Singapore:IEEE,2002:522-527.
[24]杨兆升,陈昕,王媛,等.城市交通控制与诱导系统智能协作[J].交通运输系统工程与信息,2005,5(6):43-46.
[25]Fisk C S.Game theory and transportation systems modeling[J].Transportation Research,1984(18B):310-313.
[26]Chen O J W,Ben-Akiva M E.Game-theoretic formulation of interaction between dynamic traffic control and dynamic traffic assignment[J].Transportation Research(Record1617),1998:179-188.
[27]尹宏宾,徐建闽.道路交通控制技术[M].广州:华南理工大学出版社,2000.
[28]王锡禄.具有拓扑结构的双层规划及应用[D].大连:大连理工大学,2000.
[29]LeBlanc L J,Boyce D E.A bilevel programming algorithm for exact solution of the network design problem with user-optimal flows[J].Transportaion Research,1981(20B):259-265.
[30]Ben-Ayed O.,Bouce D.E.,Blair C.E.A general bilevel linear programming formulation of the network design problem[J].Transportation Research,1988(22B):311-318.
[31]Migadalas A,Paradalos P M,Varbrand P.Multilevel optimization algorithms and applications[M].Boston:Kluwer Academic Publishers,1998.
[32]Yang Hai,Yagar Sam.Traffic assignment and traffic control in general free way-arterial corridor systems[J].Transportaion Research,1994(28B):463-486
[33]Yin Yafeng,Lu Huapu.Dynamic network traffic signal setting model[J].Gonglu Jiaotong Keji,1997,14(3):11-16.
[34]Allsop,R.E.Some possibilities for using traffic control to influence trip destinations and route choice[C].Proceedings of the Sixth International Symposium on Transportation and Traffic Theory.Elsevier,Amsterdam,1974:345-374.
[35]Abdulaal M.,LeBlanc L.J.Continuous equilibrium network design models[J].Transportation Research,1979(13B):19-32.
[36]Yang H,Lam W H K.Optimal road tolls under conditions of queuing and congestion[J].Transportation Research,1996(30A):319-332.[37]Margarida C.Coelho.Impact of speed control traffic signals on pollutant emissions[J].Transportation Research,2005(10D):323-340.
[38]Wang S.C.,Yang H.Reserve Capacity of a signal-controlled road network[J].Transportation Research,1997(31B):397-402.
[39]吕智林,范炳全,刘娟娟,等.城市快速路网匝道与污染控制双层多目标规划模型[J].控制与决策,2006,21(1):64-67.
[40]Yang Wenguo,Guo Tiande,Gao Ziyou,et al.A bi-level programming model for the optimal velocity problems under environmental objective[J].Graduate School of the Chinese Academy of Sciences,2005,22(2):129-134.
[41]Bagley J D.The behavior of adaptive system which employ genetic and correlation algorithm[M].Dissertation Abstracts International,1967.
[42]Rosonberg R S.Simulation of genetic population with biochemical properties[D].U.S.A:University of Michigan,1967.
[43]Holland J H.Adaptation in nature and artificial system[M].MA.:MIT Press,1992.
[44]De Jong K A.An analysis of the behavior of a class adaptive systems[D].U.S.A:University of Michigan,1975.
[45]Goldberg D E.Genetic algorithm in search,optimization and machine learning[M].U.S.A.:Addison-Wesley,1989.
[46]Davis L D.Handbook of Genetic Algorithm[M].London:Van Nostrand Reinhold,1991.
[47]娄信明,盛戈皋,罗毅,等.改进遗传算法在水电站自动电压控制中的应用研究[J].电力系统自动化,2000(12):41-44.
[48]邢焕来,潘炜,邹喜华.一种解决组合优化问题的改进型量子遗传算法[J].电子学报,2007,35(10):1999-2002.
[49]柯碧辉,毛卫宁.基于遗传算法的空时综合定位方法及其在数字信号处理器上的实现[J].声学学报,2003,28(2):167-170.
[50]郑春红,焦李成.基于遗传算法的Stewart并联机器人位置正解分析[J].西安电子科技大学学报:自然科学版,2003,30(2):165-168.
[51]T.H.Back,H.P.Schwefel.Application of Genetic Algorithms[R].ftp://lumpi.Informatik.uni-dortmund.de/pub/EA/paper/ea-app.ps.gz.
[52]Forrest S.Emergent computation:self-organizing,collective,and cooperative phenomena in natural and artificial computing networks[M].North-Holland:Amerserdama,1990.
[53]Glover E,Kelly J.P.,Laguna M.Genetic algorithms and tabu search:hybrids for optimization[J].Computers and Operations Research.,1995,22(1):111-134.
[54]Poths J.C.,Giddens T.D.,Yadaw S.B..The development and evaluation for an improved genetic algorithm based on migration and artificial selection[J].IEEE Transactions on Systems,Man and Cybernetics,1997,24(1):73-56.
[55]Kazarli s S.A.,Papadakis S.E.,Theocharis J.B..Microgenetie algorithms as generalized hill-climblng operators for GA optimization[J].IEEE Transactions on Evolutionary Compuation,2001,5(3):204-217.
[56]张讲社,徐宗本,梁怡.整体退火遗传算法及其收敛充要条件[J].中国科学:E辑,1997,27(2):154-164.
[57]Prasad K.,Ranjan R.,Sahoo N.C.,et al.Optimal reconfiguration of radial distribution systems using a fuzzy mutated genetic algorithm[J].IEEE Transactions on power Delivery,2005,20(2):1211-1213.
[58]Zhang Y.,Yang Xiuxia.Design for beam-balanced system controller based on chaos genetic algorithm[C].Proceedings of International Conference on Information Acquisition,2004,448-451.
[59]唐家福,汪定伟,高振,等.面向非线性规划问题的混合式遗传算法[J].自动化学报,2000,26(3):401-404.
[60]向丽,顾培亮.一种快速收敛的混合遗传算法[J].控制与决策,2002,17(1):19-23.
[61]Tsai J.T.,Chou J.H.,Liu T.K..Tuning the structure and parameters of a neural network by using hybrid taguchi-genetic algorithm[J].IEEE Transactions on Neural Networks,2006,17(1):69-80.
[62]Leung Yiuwing,Wang Yuping.An orthogonal genetic algorithm with quantization for global numverical optimization[J].IEEE Transactions on Evolutionary Computation,2001,5(1):53-62.
[63]焦李成,王磊.免疫全局优化计算[J].西安电子科技大学学报,2000(2):68-79.
[64]Tsutsui S.,Forking Y.E Genetic algorithms:gas with search space division schemes[J].Evolutionary Computation,1997,5(1):61-80.
[65]Back T..Self-adaptation in genetic algorithms[C].Proceedings of the First European Conference on Artifical Life,Cambridge,MA:MIT Press,1991.
[66]Peter J.Angeline and K.E.Kinnear.Evolving Programmers:The Co-Evolution of Intelligent Recombination Operators.Advances in Genetic Programming 2[M].Cambridge,MA:MIT Press,1996.
[67]Goldberg D.E.,Deb K.,Korb B.Messy genetic algorithm revisited:studies in mixed size and scale[J].Complex Systems,1990(4):415-444.
[68]Goldberg D.E.Real-coded genetic algorithms,virtual alphabets,and blocking[J].Complex Systems,1991(5):139-167.
[69]Sechraudolph N.N.,Belew R.K.Dynamic parameter encoding for genetic algorithms[J].Machine Learning,1992,9(1):9-22.
[70]Whitley D.,Mathias K.,Fitzhorn P.Delta-coding:an iterative search strategy for genetic algorithms[C].Proceedings of the Fourth International Conference on Genetic Algorithms,.San Mateo,CA:Morgan Kaufmann.1991:77-84.
[71]Dorigo M.,Maniezzo V.,Colorai A..The ant system:optimization by a colony of cooperating agents[J].IEEE Transactions on System,Man and Cybernetics,Part B.1996,26(1):29-41.
[72]Dorigo M.,Maniezzo V.,Colorni A..Positive feedback as a search strategy[D].Polotecnico di Milano,IT,1991.
[73]Dorigo M..Optimization,learning and natural algorithm(in Italian)[D].Politecnico di Milano,IT,1992.
[74]Bonabeau E.,Dorigo M..Inspiration for optimization from social insect behavior[J].Nature,2000,406(6):39-42.
[75]Michael J.B.K.,Bernard B.J.,Laurent K.Ant-like task and recruitment in cooperative robots[J].Nature,2000,406(31):992-995.
[76]Jackson D.E.,Holcombe M.,W.Ratnieks F.L..Trail geometry gives polarity to ant foraging networks[J].Nature,2004,432(70):907-909.
[77]Dorigo M.,Stützle T.Ant colony optimization[M].MA:MIT Press,2004.
[78]Gambardel]a L.M.,Dorigo M..Ant-Q:a reinforcement learning approach to the traveling salesman problem[C].Proceedings of the 12~(th) International Conference on Machine Learning,1995:252-260.
[79]Dorig o M.,Gambardella L.M.Ant colony system:a cooperative learning approach to the traveling salesman problem[J].IEEE Transactions on Evolutionary Computation,1997,1(1):53-66.
[80]StUtzle T,Hoos H..Improvements on the ant system:introducing max-min ant system[C].Proceedings of the International Conference on artificial Neural Networks and Genetic Algorithms,Wien:Spirng Verlag,1997:245-249.
[81]Bullnheimer B.,Hartl R.F.,Strauss C.A new rank-based version of the ant system:a computational study[J].Central European Journal for Operations Research and Economics,1999,7(1):25-38.
[82]Cordon O..A new ACO model intergrating evolutionary computation concepts:the best-worst ant system[C].Proceedings of ANTS2000-From Ant Colonies to Artificial Ants:A Series of International Workshops on Ant Algorithms,University of Librede Bruxells,2000:22-29.
[83]Dorigo M.,Caro G.D.,Gambardella L.M..Ant algorithms for discrete optimization[J].Artificial Life,1999,5(2):137-172.
[84]Dorigo M.,Caro G.D.The ant colony optimization meta-heuristic[M].New Ideas in Optimization.UK:Mcgraw Hill,1999:11-32.
[85]Gutjahr W.J..A graph-based ant system and its convergence[J].Futrue Gener.comput.Syst.,2000,16(8):873-888.
[86]Gutjahr W.J..ACO algorithm with guaranteed convergence to the optimal solution[J].Info.Processing Lett.,2002,82(3):145-153.
[87]吴庆洪,张纪会,徐心和.具有变异特征的蚁群算法[J].计算机研究与发展,1999,36(10):1240-1245.
[88]覃刚力,杨家本.自适应调整信息素的蚁群算法[J].信息与控制,2002,31(3):198-210.
[89]王颖,谢剑英.一种自适应蚁群算法及其仿真研究[J].系统仿真学报,2002,14(1):31-33.
[90]吴斌,史忠植.一种基于蚁群算法的TSP问题分段求解算法[J].计算机学报,2001,24(12):1328-1333.
[91]熊伟清,余舜杰,赵杰煌.具有分工的蚁群算法及其应用[J].模式识别与人工智能,2003,16(3):328-332.
[92]张徐亮,张晋斌.基于协同学习的蚁群电缆敷设系统[J].计算机工程与应用,2000(5):181-182.
[93]庄昌文,范明饪,李春辉,等.基于协同工作方式的一种蚁群布线系统[J].半导体学报.1999,20(5):400-406.
[94]Dorigo Marco,Gambardella,Luca Maria.Ant colonies for the traveling salesman problem[M].Belgium:Biosystems,1997,43(2):73-81
[95]Maniezzo V,Dorigo M,Colornia.The ant system applied to the quadratic assignment problem[R].Belgium:Universite de Bruxelles,1994.
[96]Colornia,Dorigo M,Maniezzo V,et al.Ant system for job-shop scheduling[J].Belgian Journal of Operations Research,Statistics and Computer Science,1994,34(1):39-53.
[97]胡小兵,黄席樾.蚁群优化算法及其应用[J].计算机仿真,2004,24(5):81-85.
[98]张素兵,刘泽民.基于蚂蚁算法的分级QoS路由调度方法[J].北京邮电大学学报,2000,23(4):12-15.
[99]张素兵,刘泽民.基于蚂蚁算法的时延受限分布式多播路由研究[J].通信学报,2001,22(3):71-74.
[100]王颖,谢剑英.一种基于蚁群算法的多媒体网络多播路由算法[J].上海交通大学学报,2002,36(4):526-531.
[101]Caro G.D.,Dorig o M.Ant net:A mobile agents approach to adaptive routing[R].Belgium:Universit Libre de Bruxelles,,1997.
[102]程紫润,傅大放.信号交叉口汽车尾气附加排放量分析[J].公路交通科技,1993, 10(3):67-71.
[103]Willam R,McShane,Roger P Roess.Traffic engineering[M].New Jersey:American Prentice-Hall company,1990.
[104]Jeroslow R G.The polynomial hierarchy and a simple model for competitive analysis]J].Mathematical Programming,1985,32(2):146-164.
[105]Bard J F.Some properties of the bilevel programming problem]J].Optimization Theory and Applications,1991,68(2):371-378.
[106]Hansen P,Lammard B,Saward G.New branch-and-bound rules for linear bilevel programming]J].SIAM Journal of scientific and Statistical Computing,1992,13(5):1194-1217.
[107]Vicente L N,Savavd G,Judice J J.Descent approaches for quadratic bilevel programming [J].Optimization Theory and Applications,1994,81(2):379-399.
[108]Edmunds T.A.,Bard J.F.Algorithms for nonlinear mathematical bilevel programs[J].IEEE Transprotation Systems,Man,and Cybernetics,1991,21(1):83-89.
[109]Jeroslow R G.The polynomial hierarchy and a simple model for competitive analysis]J].Mathematical Programming,1985,32(2):146-164.
[110]高自友,宋一凡,四兵锋.城市交通连续平衡网络设计--理论与方法[M].北京:中国铁道出版社,2000.
[111]Bard J.R.Practical bilevel optimization:algorithms and applications]M].Boston:Kluwer Academic Publishers,1998.
[112]Bard J F,Moore J T.A branch and bound algorithm for the bilevel programming problem]J].SIAM Journal of Scientific and statistical Computing,1990,11(2):281-292.
[113]Shimizu K,Lu M.A global optimization method for the Stackelberg problem with convex functions via problem transformations and concave programming]J].IEEE Trans.Systems,Man,and Cybernetics,1995,25(12):1635-640.
[114]Amat J.F.,McCal B.A representation and economic interpretation of two level programming problem]J].Operational Research Society,1981,32(9):783-792.
[115]Yin Yafeng.Genetic-algorithms-based approach for bilevel programming models]J].Transportation Engineering,2000,126(2):115-120.
[116]周明,孙树栋.遗传算法原理及应用[M].国防工业出版社,1999.
[117]Schaffer,J.D.,Caruana R.A,Eshelman L.J.A study of control Parameters affecting online performance of genetic algorithms for function optimization]C].Proceedings of the third international conference on Genetic algorithms table of contents,Los Altos(USA):Morgan Kaufmann Publish,1989:51-60.
[118]李敏强,徐博艺,寇纪淞.遗传算法与神经网络的结合[J].系统工程理论与实践,1999,19(2):65-69.
[119]吴庆洪,张纪会,徐心和.具有变异特征的蚁群算法[J].计算机研究与发展,1999,36(10):1240-1245.
[120]Quinlan J.Combining instance-based and model-based learning[C].Proceedings of the 10~(th)International Conference on Machine Learning,MA:Morgan Kaufmann Prublishers,1993:236-243.
[121]Transportation Research Board.Highway Capacity ManuaI(HCM)[M].Special Report 209,Washington D C:TRB,National Research Council,1985.
[122]Transportation Research Board.Highway Capacity Manual(HCM)[M].Special Report 209,Washington D C:TRB,National Research Council,2000.
[123]孙剑,杨晓光.微观交通仿真模型系统参数校正研究--以VISSIM的应用为例[J].交通与计算机,2004,22(3):3-6.
[124]减利林,贾磊,罗永刚.交通干线相邻交叉口动态协调控制研究[J].公路交通科技,2007,24(7):103-106.
[125]万绪军,陆化普.线控系统中相位差优化模型的研究[J].中国公路学报,2001,14(2):99-102.
[126]杨晓光,曾松,杭明升.中国城市道路交通实时自适应控制与管理系统研究[J].交通运输工程学报,2001(2):74-77.
[127]臧利林.城市交通信号优化控制算法研究[D].济南:山东大学,2007.
[128]雷英杰,张善文,李续武,等.MATLAB遗传算法工具箱及应用[M].西安:西安电子科技大学出版社,2005.
[129]罗隆福.基因控制遗传算法的理论与应用研究[D].长沙:湖南大学,2001.
[130]姜昌华.遗传算法在物流系统优化中的应用研究[D].上海:华东师范大学,2007.
[2]毛保华,杨肇夏,陈海波.道路交通仿真技术与系统研究[J].北方交通大学学报,2002,26(5):37-46.
[3]陆化普.城市交通现代化管理[M].北京:人民交通出版社,1999.
[4]上海市环境科学研究院.上海交通现状与机动车污染状况及污染暴露-反应关系[R].2005.
[5]贺国光.发展智能交通促进一系列产业的发展[J].信息系统工程,1999(6):7-8.
[6]尹宏宾,徐建闽.道路交通控制技术[M].广州:华南理工大学出版社,2000.
[7]钟国文,朱劲.单交叉路口的模糊智能交通控制系统[J].广西大学学报:自然科学版,2004(9):175-180.
[8]许伦辉,衷路生,徐建闽.基于神经网络实现的交叉口多相位模糊逻辑控制[J].系统工程理论与实践,2004(7):135-140.
[9]承向军,杨肇夏.一种交通信号自学习控制方法及仿真实现[J].系统仿真学报,2004,16(7):1519-1524.
[10]Findler N U.Multiagent coordination and cooperation in a distributed dynamic environment with limited resources[J].Artificial Intelligent in Engineering,1995,9:229-238.
[11]France John,Ghorbani Ali A.A multiagent system for optimizing urban traffic[C].Proceeding of the IEEE/W IC International Conference on Intelligent Agent Technology,2003.
[12]陆小芳,郑应平,王令群.交通信号控制系统的多agent协调研究[J].计算机工程与应用,2006(2):188-190.
[13]邱凌云,陈锋,何兵兵.基于Agent的驾驶员-车辆建模研究与实现[J].计算机仿真,2005(11):222-225.
[14]Ferrari P.Road pricing and network equilibrium[J].Transportation Research,1995(29B):357-372.
[15]Yang H,Bell M G H.Traffic restraint,road pricing and network equilibrium[J].Transportation Research,1997(31B):303-314.
[16]Gao Z Y,Song Y F.A reserve capacity model of optimal signal control with user-equilibrium route choice[J].Transportation Research,2002(36B):313-323.
[17]Rilett L R,Benedek C M.Traffic assignment under environmental and equity objectives[J].Transportation Research,1994(36B):176-190.
[18]杨文国,高自友.考虑环境因素的广义用户平衡和广义系统最优配流模型[J].中国公 路学报,2003,16(4):72-76.
[19]赵彤,高自友.环境污染限制及最优信号控制条件下的综合离散网络设计问题[J].土木工程学报,2006,39(2):102-106.
[20]Hallmark S.L.,Fomunung I.,Guensler R.,et al.Assessing impacts of improved signal timing and transportation control measure using an activity-specific modeling approach[J].Transportation Research Record,2000(1738):49-55.
[21]Li X.,Li G.,Pang,S.,Yang X.,et al.Signal timing of intersections using integrated optimization of traffic quality,emissions and fuel consumption[J].Transportation Research,2004(9D):401-407.
[22]Allsop R E.Some possibilities for using traffic control to influence trip destinations and route choice[C].Proceedings of 6th International Symposium on Transportation and Traffic Theory.Sydney,Australia:1974:345-374.
[23]Ma Shoufeng,He Guoguang,Wang Shitong.A hierarchical coordination model for control-guidance integrated systems in ITS[C].Proceedings of IEEE International Conference on Intelligent Transportation System.Singapore:IEEE,2002:522-527.
[24]杨兆升,陈昕,王媛,等.城市交通控制与诱导系统智能协作[J].交通运输系统工程与信息,2005,5(6):43-46.
[25]Fisk C S.Game theory and transportation systems modeling[J].Transportation Research,1984(18B):310-313.
[26]Chen O J W,Ben-Akiva M E.Game-theoretic formulation of interaction between dynamic traffic control and dynamic traffic assignment[J].Transportation Research(Record1617),1998:179-188.
[27]尹宏宾,徐建闽.道路交通控制技术[M].广州:华南理工大学出版社,2000.
[28]王锡禄.具有拓扑结构的双层规划及应用[D].大连:大连理工大学,2000.
[29]LeBlanc L J,Boyce D E.A bilevel programming algorithm for exact solution of the network design problem with user-optimal flows[J].Transportaion Research,1981(20B):259-265.
[30]Ben-Ayed O.,Bouce D.E.,Blair C.E.A general bilevel linear programming formulation of the network design problem[J].Transportation Research,1988(22B):311-318.
[31]Migadalas A,Paradalos P M,Varbrand P.Multilevel optimization algorithms and applications[M].Boston:Kluwer Academic Publishers,1998.
[32]Yang Hai,Yagar Sam.Traffic assignment and traffic control in general free way-arterial corridor systems[J].Transportaion Research,1994(28B):463-486
[33]Yin Yafeng,Lu Huapu.Dynamic network traffic signal setting model[J].Gonglu Jiaotong Keji,1997,14(3):11-16.
[34]Allsop,R.E.Some possibilities for using traffic control to influence trip destinations and route choice[C].Proceedings of the Sixth International Symposium on Transportation and Traffic Theory.Elsevier,Amsterdam,1974:345-374.
[35]Abdulaal M.,LeBlanc L.J.Continuous equilibrium network design models[J].Transportation Research,1979(13B):19-32.
[36]Yang H,Lam W H K.Optimal road tolls under conditions of queuing and congestion[J].Transportation Research,1996(30A):319-332.[37]Margarida C.Coelho.Impact of speed control traffic signals on pollutant emissions[J].Transportation Research,2005(10D):323-340.
[38]Wang S.C.,Yang H.Reserve Capacity of a signal-controlled road network[J].Transportation Research,1997(31B):397-402.
[39]吕智林,范炳全,刘娟娟,等.城市快速路网匝道与污染控制双层多目标规划模型[J].控制与决策,2006,21(1):64-67.
[40]Yang Wenguo,Guo Tiande,Gao Ziyou,et al.A bi-level programming model for the optimal velocity problems under environmental objective[J].Graduate School of the Chinese Academy of Sciences,2005,22(2):129-134.
[41]Bagley J D.The behavior of adaptive system which employ genetic and correlation algorithm[M].Dissertation Abstracts International,1967.
[42]Rosonberg R S.Simulation of genetic population with biochemical properties[D].U.S.A:University of Michigan,1967.
[43]Holland J H.Adaptation in nature and artificial system[M].MA.:MIT Press,1992.
[44]De Jong K A.An analysis of the behavior of a class adaptive systems[D].U.S.A:University of Michigan,1975.
[45]Goldberg D E.Genetic algorithm in search,optimization and machine learning[M].U.S.A.:Addison-Wesley,1989.
[46]Davis L D.Handbook of Genetic Algorithm[M].London:Van Nostrand Reinhold,1991.
[47]娄信明,盛戈皋,罗毅,等.改进遗传算法在水电站自动电压控制中的应用研究[J].电力系统自动化,2000(12):41-44.
[48]邢焕来,潘炜,邹喜华.一种解决组合优化问题的改进型量子遗传算法[J].电子学报,2007,35(10):1999-2002.
[49]柯碧辉,毛卫宁.基于遗传算法的空时综合定位方法及其在数字信号处理器上的实现[J].声学学报,2003,28(2):167-170.
[50]郑春红,焦李成.基于遗传算法的Stewart并联机器人位置正解分析[J].西安电子科技大学学报:自然科学版,2003,30(2):165-168.
[51]T.H.Back,H.P.Schwefel.Application of Genetic Algorithms[R].ftp://lumpi.Informatik.uni-dortmund.de/pub/EA/paper/ea-app.ps.gz.
[52]Forrest S.Emergent computation:self-organizing,collective,and cooperative phenomena in natural and artificial computing networks[M].North-Holland:Amerserdama,1990.
[53]Glover E,Kelly J.P.,Laguna M.Genetic algorithms and tabu search:hybrids for optimization[J].Computers and Operations Research.,1995,22(1):111-134.
[54]Poths J.C.,Giddens T.D.,Yadaw S.B..The development and evaluation for an improved genetic algorithm based on migration and artificial selection[J].IEEE Transactions on Systems,Man and Cybernetics,1997,24(1):73-56.
[55]Kazarli s S.A.,Papadakis S.E.,Theocharis J.B..Microgenetie algorithms as generalized hill-climblng operators for GA optimization[J].IEEE Transactions on Evolutionary Compuation,2001,5(3):204-217.
[56]张讲社,徐宗本,梁怡.整体退火遗传算法及其收敛充要条件[J].中国科学:E辑,1997,27(2):154-164.
[57]Prasad K.,Ranjan R.,Sahoo N.C.,et al.Optimal reconfiguration of radial distribution systems using a fuzzy mutated genetic algorithm[J].IEEE Transactions on power Delivery,2005,20(2):1211-1213.
[58]Zhang Y.,Yang Xiuxia.Design for beam-balanced system controller based on chaos genetic algorithm[C].Proceedings of International Conference on Information Acquisition,2004,448-451.
[59]唐家福,汪定伟,高振,等.面向非线性规划问题的混合式遗传算法[J].自动化学报,2000,26(3):401-404.
[60]向丽,顾培亮.一种快速收敛的混合遗传算法[J].控制与决策,2002,17(1):19-23.
[61]Tsai J.T.,Chou J.H.,Liu T.K..Tuning the structure and parameters of a neural network by using hybrid taguchi-genetic algorithm[J].IEEE Transactions on Neural Networks,2006,17(1):69-80.
[62]Leung Yiuwing,Wang Yuping.An orthogonal genetic algorithm with quantization for global numverical optimization[J].IEEE Transactions on Evolutionary Computation,2001,5(1):53-62.
[63]焦李成,王磊.免疫全局优化计算[J].西安电子科技大学学报,2000(2):68-79.
[64]Tsutsui S.,Forking Y.E Genetic algorithms:gas with search space division schemes[J].Evolutionary Computation,1997,5(1):61-80.
[65]Back T..Self-adaptation in genetic algorithms[C].Proceedings of the First European Conference on Artifical Life,Cambridge,MA:MIT Press,1991.
[66]Peter J.Angeline and K.E.Kinnear.Evolving Programmers:The Co-Evolution of Intelligent Recombination Operators.Advances in Genetic Programming 2[M].Cambridge,MA:MIT Press,1996.
[67]Goldberg D.E.,Deb K.,Korb B.Messy genetic algorithm revisited:studies in mixed size and scale[J].Complex Systems,1990(4):415-444.
[68]Goldberg D.E.Real-coded genetic algorithms,virtual alphabets,and blocking[J].Complex Systems,1991(5):139-167.
[69]Sechraudolph N.N.,Belew R.K.Dynamic parameter encoding for genetic algorithms[J].Machine Learning,1992,9(1):9-22.
[70]Whitley D.,Mathias K.,Fitzhorn P.Delta-coding:an iterative search strategy for genetic algorithms[C].Proceedings of the Fourth International Conference on Genetic Algorithms,.San Mateo,CA:Morgan Kaufmann.1991:77-84.
[71]Dorigo M.,Maniezzo V.,Colorai A..The ant system:optimization by a colony of cooperating agents[J].IEEE Transactions on System,Man and Cybernetics,Part B.1996,26(1):29-41.
[72]Dorigo M.,Maniezzo V.,Colorni A..Positive feedback as a search strategy[D].Polotecnico di Milano,IT,1991.
[73]Dorigo M..Optimization,learning and natural algorithm(in Italian)[D].Politecnico di Milano,IT,1992.
[74]Bonabeau E.,Dorigo M..Inspiration for optimization from social insect behavior[J].Nature,2000,406(6):39-42.
[75]Michael J.B.K.,Bernard B.J.,Laurent K.Ant-like task and recruitment in cooperative robots[J].Nature,2000,406(31):992-995.
[76]Jackson D.E.,Holcombe M.,W.Ratnieks F.L..Trail geometry gives polarity to ant foraging networks[J].Nature,2004,432(70):907-909.
[77]Dorigo M.,Stützle T.Ant colony optimization[M].MA:MIT Press,2004.
[78]Gambardel]a L.M.,Dorigo M..Ant-Q:a reinforcement learning approach to the traveling salesman problem[C].Proceedings of the 12~(th) International Conference on Machine Learning,1995:252-260.
[79]Dorig o M.,Gambardella L.M.Ant colony system:a cooperative learning approach to the traveling salesman problem[J].IEEE Transactions on Evolutionary Computation,1997,1(1):53-66.
[80]StUtzle T,Hoos H..Improvements on the ant system:introducing max-min ant system[C].Proceedings of the International Conference on artificial Neural Networks and Genetic Algorithms,Wien:Spirng Verlag,1997:245-249.
[81]Bullnheimer B.,Hartl R.F.,Strauss C.A new rank-based version of the ant system:a computational study[J].Central European Journal for Operations Research and Economics,1999,7(1):25-38.
[82]Cordon O..A new ACO model intergrating evolutionary computation concepts:the best-worst ant system[C].Proceedings of ANTS2000-From Ant Colonies to Artificial Ants:A Series of International Workshops on Ant Algorithms,University of Librede Bruxells,2000:22-29.
[83]Dorigo M.,Caro G.D.,Gambardella L.M..Ant algorithms for discrete optimization[J].Artificial Life,1999,5(2):137-172.
[84]Dorigo M.,Caro G.D.The ant colony optimization meta-heuristic[M].New Ideas in Optimization.UK:Mcgraw Hill,1999:11-32.
[85]Gutjahr W.J..A graph-based ant system and its convergence[J].Futrue Gener.comput.Syst.,2000,16(8):873-888.
[86]Gutjahr W.J..ACO algorithm with guaranteed convergence to the optimal solution[J].Info.Processing Lett.,2002,82(3):145-153.
[87]吴庆洪,张纪会,徐心和.具有变异特征的蚁群算法[J].计算机研究与发展,1999,36(10):1240-1245.
[88]覃刚力,杨家本.自适应调整信息素的蚁群算法[J].信息与控制,2002,31(3):198-210.
[89]王颖,谢剑英.一种自适应蚁群算法及其仿真研究[J].系统仿真学报,2002,14(1):31-33.
[90]吴斌,史忠植.一种基于蚁群算法的TSP问题分段求解算法[J].计算机学报,2001,24(12):1328-1333.
[91]熊伟清,余舜杰,赵杰煌.具有分工的蚁群算法及其应用[J].模式识别与人工智能,2003,16(3):328-332.
[92]张徐亮,张晋斌.基于协同学习的蚁群电缆敷设系统[J].计算机工程与应用,2000(5):181-182.
[93]庄昌文,范明饪,李春辉,等.基于协同工作方式的一种蚁群布线系统[J].半导体学报.1999,20(5):400-406.
[94]Dorigo Marco,Gambardella,Luca Maria.Ant colonies for the traveling salesman problem[M].Belgium:Biosystems,1997,43(2):73-81
[95]Maniezzo V,Dorigo M,Colornia.The ant system applied to the quadratic assignment problem[R].Belgium:Universite de Bruxelles,1994.
[96]Colornia,Dorigo M,Maniezzo V,et al.Ant system for job-shop scheduling[J].Belgian Journal of Operations Research,Statistics and Computer Science,1994,34(1):39-53.
[97]胡小兵,黄席樾.蚁群优化算法及其应用[J].计算机仿真,2004,24(5):81-85.
[98]张素兵,刘泽民.基于蚂蚁算法的分级QoS路由调度方法[J].北京邮电大学学报,2000,23(4):12-15.
[99]张素兵,刘泽民.基于蚂蚁算法的时延受限分布式多播路由研究[J].通信学报,2001,22(3):71-74.
[100]王颖,谢剑英.一种基于蚁群算法的多媒体网络多播路由算法[J].上海交通大学学报,2002,36(4):526-531.
[101]Caro G.D.,Dorig o M.Ant net:A mobile agents approach to adaptive routing[R].Belgium:Universit Libre de Bruxelles,,1997.
[102]程紫润,傅大放.信号交叉口汽车尾气附加排放量分析[J].公路交通科技,1993, 10(3):67-71.
[103]Willam R,McShane,Roger P Roess.Traffic engineering[M].New Jersey:American Prentice-Hall company,1990.
[104]Jeroslow R G.The polynomial hierarchy and a simple model for competitive analysis]J].Mathematical Programming,1985,32(2):146-164.
[105]Bard J F.Some properties of the bilevel programming problem]J].Optimization Theory and Applications,1991,68(2):371-378.
[106]Hansen P,Lammard B,Saward G.New branch-and-bound rules for linear bilevel programming]J].SIAM Journal of scientific and Statistical Computing,1992,13(5):1194-1217.
[107]Vicente L N,Savavd G,Judice J J.Descent approaches for quadratic bilevel programming [J].Optimization Theory and Applications,1994,81(2):379-399.
[108]Edmunds T.A.,Bard J.F.Algorithms for nonlinear mathematical bilevel programs[J].IEEE Transprotation Systems,Man,and Cybernetics,1991,21(1):83-89.
[109]Jeroslow R G.The polynomial hierarchy and a simple model for competitive analysis]J].Mathematical Programming,1985,32(2):146-164.
[110]高自友,宋一凡,四兵锋.城市交通连续平衡网络设计--理论与方法[M].北京:中国铁道出版社,2000.
[111]Bard J.R.Practical bilevel optimization:algorithms and applications]M].Boston:Kluwer Academic Publishers,1998.
[112]Bard J F,Moore J T.A branch and bound algorithm for the bilevel programming problem]J].SIAM Journal of Scientific and statistical Computing,1990,11(2):281-292.
[113]Shimizu K,Lu M.A global optimization method for the Stackelberg problem with convex functions via problem transformations and concave programming]J].IEEE Trans.Systems,Man,and Cybernetics,1995,25(12):1635-640.
[114]Amat J.F.,McCal B.A representation and economic interpretation of two level programming problem]J].Operational Research Society,1981,32(9):783-792.
[115]Yin Yafeng.Genetic-algorithms-based approach for bilevel programming models]J].Transportation Engineering,2000,126(2):115-120.
[116]周明,孙树栋.遗传算法原理及应用[M].国防工业出版社,1999.
[117]Schaffer,J.D.,Caruana R.A,Eshelman L.J.A study of control Parameters affecting online performance of genetic algorithms for function optimization]C].Proceedings of the third international conference on Genetic algorithms table of contents,Los Altos(USA):Morgan Kaufmann Publish,1989:51-60.
[118]李敏强,徐博艺,寇纪淞.遗传算法与神经网络的结合[J].系统工程理论与实践,1999,19(2):65-69.
[119]吴庆洪,张纪会,徐心和.具有变异特征的蚁群算法[J].计算机研究与发展,1999,36(10):1240-1245.
[120]Quinlan J.Combining instance-based and model-based learning[C].Proceedings of the 10~(th)International Conference on Machine Learning,MA:Morgan Kaufmann Prublishers,1993:236-243.
[121]Transportation Research Board.Highway Capacity ManuaI(HCM)[M].Special Report 209,Washington D C:TRB,National Research Council,1985.
[122]Transportation Research Board.Highway Capacity Manual(HCM)[M].Special Report 209,Washington D C:TRB,National Research Council,2000.
[123]孙剑,杨晓光.微观交通仿真模型系统参数校正研究--以VISSIM的应用为例[J].交通与计算机,2004,22(3):3-6.
[124]减利林,贾磊,罗永刚.交通干线相邻交叉口动态协调控制研究[J].公路交通科技,2007,24(7):103-106.
[125]万绪军,陆化普.线控系统中相位差优化模型的研究[J].中国公路学报,2001,14(2):99-102.
[126]杨晓光,曾松,杭明升.中国城市道路交通实时自适应控制与管理系统研究[J].交通运输工程学报,2001(2):74-77.
[127]臧利林.城市交通信号优化控制算法研究[D].济南:山东大学,2007.
[128]雷英杰,张善文,李续武,等.MATLAB遗传算法工具箱及应用[M].西安:西安电子科技大学出版社,2005.
[129]罗隆福.基因控制遗传算法的理论与应用研究[D].长沙:湖南大学,2001.
[130]姜昌华.遗传算法在物流系统优化中的应用研究[D].上海:华东师范大学,2007.