干线协调控制的研究与设计
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摘要
交通拥堵与过饱和已成为制约城市化进程的重要因素之一。通过大规模的道路基础设施建设解决现有的交通问题是不现实的。采用先进的交通信号控制技术,进行优化信号配时,是提高现有路网的通行能力的有效途径。干线协调控制是智能交通的重要研究领域之一,本文针对干线协调控制研究的现状,在研究和开发城市交通信号控制系统的基础上,对交通信号控制中的干线协调控制技术进行了深入的研究,本文的主要工作包括:
1.干线协调控制方案的设计
深入研究了干线协调控制的理论与方法,以合肥市黄山路干线协调控制为研究背景,分析了控制方案存在的缺陷,在干线的交通流特性的调研和分析的基础上,综合了不同控制方式(单点定时控制,感应控制,绿波控制等)的特点,设计了一种基于分组的多时段干线绿波控制方案。
2.基于粒子群算法的双向干线协调控制研究
深入研究了不同情况下的干线延误模型,在此基础上,采用粒子群算法对双向干线协调控制的重要参数包括:周期、相位差和绿信比等进行协调和优化,该算法考虑具有冲突的弯道车辆的影响,有效地避免了传统绿波设计方法的不足。
3.基于柔性控制的动态单向绿波控制研究
传统的定时绿波控制无法适应交通流的变化,本文提出了一种基于柔性控制的单向绿波控制算法,该算法通过路段实时流量,结合流量-速度模型,可以根据实时交通流状况和驾驶员的情感特性对控制方案进行动态柔性调整。仿真实验证明,该算法在保证干线绿波的前提下,控制方案的变化能够适应驾驶员的心理和生理特性,体现“人性化”的特点,从而提高交通的安全性。
Traffic jams and over-saturation has been one of the major factors of restricting the urbanization process. It is unpractical to solve the existing traffic problems by large-scale construction of road infrastructure. Using advanced traffic signal control techniques to optimize the signal timing is an effective way to improve the existing road network capacity. Arterial coordination control is one of the important research areas in intelligent traffic, aiming at the present research of arterial coordination; arterial coordination control technique is studied on the basis of the research and development of urban traffic signal control system. The main work of this dissertation includes:
1. The design of arterial coordination control scheme
The theory and method of arterial coordination control are further studied. Taking as the research background the arterial coordination control of Huangshan Road, Hefei City, the drawbacks of present control scheme are analyzed, on the basis of traffic flow investigation and its characteristics analysis, a grouping multi-time arterial coordination control scheme is developed by integrating the characteristics of different control ways such as single point of timing control, sensor control and green wave control.
2. Study of two-direction trunk coordination based on particle swarm optimization
The arterial delay models in different situations are studied. Based on the delay model, particle swarm optimization algorithm is utilized to coordinate and optimize the important parameters including cycle, phase and split of the arterial coordination control. This algorithm considers the effects of conflict turning vehicles and effectively overcomes the disadvantage of traditional arterial coordination control.
.3. Research of one-way dynamic green wave flexible control
The traditional timing green wave control can not fit dynamic traffic flows. A one-way green wave flexible control algorithm is proposed. According to real-time traffic flows and the flow-velocity model, the algorithm can take a dynamic flexible regulation for the control scheme in term of the real-time traffic flow and the emotional characteristics of drivers. The simulation experimental results show that the algorithm ensures the green wave effect and makes the changes of control scheme fit the psychological and physiological characteristics of drivers. It embodies the humanization characteristics and improves traffic safety.
In final, the work of this dissertation is summarized, and the further study is indicated.
1.干线协调控制方案的设计
深入研究了干线协调控制的理论与方法,以合肥市黄山路干线协调控制为研究背景,分析了控制方案存在的缺陷,在干线的交通流特性的调研和分析的基础上,综合了不同控制方式(单点定时控制,感应控制,绿波控制等)的特点,设计了一种基于分组的多时段干线绿波控制方案。
2.基于粒子群算法的双向干线协调控制研究
深入研究了不同情况下的干线延误模型,在此基础上,采用粒子群算法对双向干线协调控制的重要参数包括:周期、相位差和绿信比等进行协调和优化,该算法考虑具有冲突的弯道车辆的影响,有效地避免了传统绿波设计方法的不足。
3.基于柔性控制的动态单向绿波控制研究
传统的定时绿波控制无法适应交通流的变化,本文提出了一种基于柔性控制的单向绿波控制算法,该算法通过路段实时流量,结合流量-速度模型,可以根据实时交通流状况和驾驶员的情感特性对控制方案进行动态柔性调整。仿真实验证明,该算法在保证干线绿波的前提下,控制方案的变化能够适应驾驶员的心理和生理特性,体现“人性化”的特点,从而提高交通的安全性。
Traffic jams and over-saturation has been one of the major factors of restricting the urbanization process. It is unpractical to solve the existing traffic problems by large-scale construction of road infrastructure. Using advanced traffic signal control techniques to optimize the signal timing is an effective way to improve the existing road network capacity. Arterial coordination control is one of the important research areas in intelligent traffic, aiming at the present research of arterial coordination; arterial coordination control technique is studied on the basis of the research and development of urban traffic signal control system. The main work of this dissertation includes:
1. The design of arterial coordination control scheme
The theory and method of arterial coordination control are further studied. Taking as the research background the arterial coordination control of Huangshan Road, Hefei City, the drawbacks of present control scheme are analyzed, on the basis of traffic flow investigation and its characteristics analysis, a grouping multi-time arterial coordination control scheme is developed by integrating the characteristics of different control ways such as single point of timing control, sensor control and green wave control.
2. Study of two-direction trunk coordination based on particle swarm optimization
The arterial delay models in different situations are studied. Based on the delay model, particle swarm optimization algorithm is utilized to coordinate and optimize the important parameters including cycle, phase and split of the arterial coordination control. This algorithm considers the effects of conflict turning vehicles and effectively overcomes the disadvantage of traditional arterial coordination control.
.3. Research of one-way dynamic green wave flexible control
The traditional timing green wave control can not fit dynamic traffic flows. A one-way green wave flexible control algorithm is proposed. According to real-time traffic flows and the flow-velocity model, the algorithm can take a dynamic flexible regulation for the control scheme in term of the real-time traffic flow and the emotional characteristics of drivers. The simulation experimental results show that the algorithm ensures the green wave effect and makes the changes of control scheme fit the psychological and physiological characteristics of drivers. It embodies the humanization characteristics and improves traffic safety.
In final, the work of this dissertation is summarized, and the further study is indicated.
引文
常云涛。基于遗传算法的城市干道协调控制。交通运输工程学报,2003 3(2)
丛爽。神经网络、模糊系统及其在运动控制中的应用。中国科学技术大学出版社,2001
高云峰。线控制相位差概率模型研究。公路交通科技,2006 8(23)
李娜。编程实现双向干线协调参数数值计算的研究。道路交通与安全,2008 4
李伟。基于线性规划的干线模糊补偿控制。公路交通科技,2007 8(24)
李江,傅晓光等。现代道路交通管理。人民交通出版社,2002.4
李宁,孙德宝等。带变异算子的粒子群优化算法。计算机工程与应用,2004.17(12)
卢凯。不同交通流状况下的交叉口信号控制策略。公路交通科技,2006.4(23)
刘建峰。城市干线交通控制方法研究[D]。西安:西北工业大学。
刘智勇。智能交通控制理论及其应用。科学出版社,2003
马凤伟,刘智勇。城市交通干线的Q一学习控制算法。五邑大学学报(自然科学版),2007 10(21)
全永燊?鞘薪煌刂啤H嗣窠煌ǔ霭嫔纾?998。
任福田,刘小明,荣建。交通工程学。人民交通出版社,2003。
史忠科。交通控制系统导论。科学出版社,2003
沈国江,孙优贤。城市交通干线递阶模糊控制及其神经网络实现[J]。系统工程理论与实践,2004.4(4)。
万绪军,陆化普。线控系统中相位差优化模型的研究。中国公路学报,2001,14(2)
王炜,高海龙,李文权。公路交叉口通行能力分析方法。科学出版社,2001
王殿海。交通流理论。人民交通出版社2002。
王俊刚。变带速干线协调控制模型研究。公路交通科技,2005 4(22)
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。同济大学学报(自然科学版),2008.7(36)
魏朗,陈荫三,饭田恭敬。城市主干线交通信号群系统控制的参数优化设计模式及其模拟系统开发[J]。交通运输工程学报,2001,1(1):72
谢晓锋,张文俊,杨之廉。微粒群算法综述。控制与决策2003.18(2)
徐海,刘石,马勇等。基于改进粒子群游优化的模糊逻辑系统自学习算法[D],计算机工程与应用,2000(7):62~63。
杨佩昆,吴兵。交通管理与控制[M]。北京:人民交通出版社,2003。
张昌禄。交通干线信号协调控制方法综述。中国人民公安大学学报(自然科学版),2007 1(51)
赵志刚,苏一丹。带自变异算子的粒子群优化算法。计算机工程与应用, 2006(13)
中国公路学会《交通工程手册》编委会。交通工程手册。人民交通出版社,1998。
中华人民共和国国家标准。城市道路交通规划设计规范。国家技术监督局,中华人民共和国建设部联合发布
朱文星,贾磊。城市交通主干路相位差优化研究。计算机工程与应用,2005,41(20)
D. Cheng, C. Messer, Z. Tian, J. Liu. Modification of Webster’s minimum delay cycle length equation based on HCM2000. The 82nd TRB Annual Meeting, Washington. D.C., 2003.
Eberhart R, Shi Yuhui. Particle swarm optimization Developments, applications and resources [A] Proc IEEE Int Conf on Evolutionary Computation[C]. Seoul, 2001. 81-86
FRANCE J, GHORBANI A A. A Multi Agent system for optimizing urban traffic[C]. Proceedings of IEEE/WIC International Conference on Intelligent Agent Technology. Beijing: IEEE, 2003:411-414.
Fukuyama Y. Fundamentals of particle swarm techniques [A]. Lee K Y, El-Sharkawi M A. Modern Heuristic Optimization Techniques with Applications to Power Systems [M]. IEEE Power Engineering Society, 2002. 45~51
F. V. Webster, Traffic Signal Settings. Road Research Technical Paper, No.39, London: Her Majesty’s Stationery Office, 1958.
Ishihara. Traffic signal networks simulator using emotional algorithm with individuality [J]. IEEE ITS Conference Proceedings USA. August 25-29, 2001.
Lee J H, Lee K M, Lee K H. Traffic control of intersection group based on fuzzy logic[A]. Proc 6th Fuzzy Systems Assoc World Congress[C]. USA: Prentice Hall PRT, 1995.
Li Xiaohong; Tan Guozhen。Urban arterial road green-wave control based on genetic algorithm。7th World Congress on Intelligent Control and Automation, WCICA'08, June 25,2008 - June 27,2008
Lu, Kai; Xu Jian-Min. Offset model for arterial road coordinate control and its optimization method。China Journal of Highway and Transport, January 2008
Lu, Kai; Huang, Jianxin; Xu, Jianmin。An new offset model for arterial road coordinate control and its optimization method。26th Chinese Control Conference, CCC 2007, July 26,2007 - July 31,2007
Parsopolulos K E, Plagianakos V P, Magoulas G D, Improving particle swarm optimizer by function“streching”[A]. Hadjisavvas N, Pardalos P. Advances in Convex Analysis and Global Optimization [M]. The Netherlands: Kluwer Academic Publishers, 2001. 445~457
R. Akcelik, Traffic Signals: Capacity and Timing Analysis. Sydeny:TRRL, 1981.
Ruimin Li; Huapu, Lu. Research of arterial traffic coordinated fuzzy control model based on genetic algorithm。6th World Congress on Intelligent Control and Automation, WCICA 2006,June 21, 2006-June 26, 2006.
Shen GJ. A traffic flow model and intelligent control technique for urban trunk road[C]. Hang Zhou: The 5th World Congress on Intelligent Control and Automation, 2004.
Shi Yuhui, Eberhart R. Parameter selection in particle swarm optimization[A]. Proc of the 7th Annual Conf on Evolutionary Programming[C]. Washington DC, 1998.591-600.
Zhang, Y.; Xie, Y.; Ye, Z. Development and Evaluation of a Multi-Agent Based Neuro-Fuzzy Arterial Traffic Signal Control System。Southwest Region Univ. Transportation Center, College Station, TX; Department of Transportation, Washington, DC. University Transportation Centers Program., 122p, Sep 2007
丛爽。神经网络、模糊系统及其在运动控制中的应用。中国科学技术大学出版社,2001
高云峰。线控制相位差概率模型研究。公路交通科技,2006 8(23)
李娜。编程实现双向干线协调参数数值计算的研究。道路交通与安全,2008 4
李伟。基于线性规划的干线模糊补偿控制。公路交通科技,2007 8(24)
李江,傅晓光等。现代道路交通管理。人民交通出版社,2002.4
李宁,孙德宝等。带变异算子的粒子群优化算法。计算机工程与应用,2004.17(12)
卢凯。不同交通流状况下的交叉口信号控制策略。公路交通科技,2006.4(23)
刘建峰。城市干线交通控制方法研究[D]。西安:西北工业大学。
刘智勇。智能交通控制理论及其应用。科学出版社,2003
马凤伟,刘智勇。城市交通干线的Q一学习控制算法。五邑大学学报(自然科学版),2007 10(21)
全永燊?鞘薪煌刂啤H嗣窠煌ǔ霭嫔纾?998。
任福田,刘小明,荣建。交通工程学。人民交通出版社,2003。
史忠科。交通控制系统导论。科学出版社,2003
沈国江,孙优贤。城市交通干线递阶模糊控制及其神经网络实现[J]。系统工程理论与实践,2004.4(4)。
万绪军,陆化普。线控系统中相位差优化模型的研究。中国公路学报,2001,14(2)
王炜,高海龙,李文权。公路交叉口通行能力分析方法。科学出版社,2001
王殿海。交通流理论。人民交通出版社2002。
王俊刚。变带速干线协调控制模型研究。公路交通科技,2005 4(22)
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。同济大学学报(自然科学版),2008.7(36)
魏朗,陈荫三,饭田恭敬。城市主干线交通信号群系统控制的参数优化设计模式及其模拟系统开发[J]。交通运输工程学报,2001,1(1):72
谢晓锋,张文俊,杨之廉。微粒群算法综述。控制与决策2003.18(2)
徐海,刘石,马勇等。基于改进粒子群游优化的模糊逻辑系统自学习算法[D],计算机工程与应用,2000(7):62~63。
杨佩昆,吴兵。交通管理与控制[M]。北京:人民交通出版社,2003。
张昌禄。交通干线信号协调控制方法综述。中国人民公安大学学报(自然科学版),2007 1(51)
赵志刚,苏一丹。带自变异算子的粒子群优化算法。计算机工程与应用, 2006(13)
中国公路学会《交通工程手册》编委会。交通工程手册。人民交通出版社,1998。
中华人民共和国国家标准。城市道路交通规划设计规范。国家技术监督局,中华人民共和国建设部联合发布
朱文星,贾磊。城市交通主干路相位差优化研究。计算机工程与应用,2005,41(20)
D. Cheng, C. Messer, Z. Tian, J. Liu. Modification of Webster’s minimum delay cycle length equation based on HCM2000. The 82nd TRB Annual Meeting, Washington. D.C., 2003.
Eberhart R, Shi Yuhui. Particle swarm optimization Developments, applications and resources [A] Proc IEEE Int Conf on Evolutionary Computation[C]. Seoul, 2001. 81-86
FRANCE J, GHORBANI A A. A Multi Agent system for optimizing urban traffic[C]. Proceedings of IEEE/WIC International Conference on Intelligent Agent Technology. Beijing: IEEE, 2003:411-414.
Fukuyama Y. Fundamentals of particle swarm techniques [A]. Lee K Y, El-Sharkawi M A. Modern Heuristic Optimization Techniques with Applications to Power Systems [M]. IEEE Power Engineering Society, 2002. 45~51
F. V. Webster, Traffic Signal Settings. Road Research Technical Paper, No.39, London: Her Majesty’s Stationery Office, 1958.
Ishihara. Traffic signal networks simulator using emotional algorithm with individuality [J]. IEEE ITS Conference Proceedings USA. August 25-29, 2001.
Lee J H, Lee K M, Lee K H. Traffic control of intersection group based on fuzzy logic[A]. Proc 6th Fuzzy Systems Assoc World Congress[C]. USA: Prentice Hall PRT, 1995.
Li Xiaohong; Tan Guozhen。Urban arterial road green-wave control based on genetic algorithm。7th World Congress on Intelligent Control and Automation, WCICA'08, June 25,2008 - June 27,2008
Lu, Kai; Xu Jian-Min. Offset model for arterial road coordinate control and its optimization method。China Journal of Highway and Transport, January 2008
Lu, Kai; Huang, Jianxin; Xu, Jianmin。An new offset model for arterial road coordinate control and its optimization method。26th Chinese Control Conference, CCC 2007, July 26,2007 - July 31,2007
Parsopolulos K E, Plagianakos V P, Magoulas G D, Improving particle swarm optimizer by function“streching”[A]. Hadjisavvas N, Pardalos P. Advances in Convex Analysis and Global Optimization [M]. The Netherlands: Kluwer Academic Publishers, 2001. 445~457
R. Akcelik, Traffic Signals: Capacity and Timing Analysis. Sydeny:TRRL, 1981.
Ruimin Li; Huapu, Lu. Research of arterial traffic coordinated fuzzy control model based on genetic algorithm。6th World Congress on Intelligent Control and Automation, WCICA 2006,June 21, 2006-June 26, 2006.
Shen GJ. A traffic flow model and intelligent control technique for urban trunk road[C]. Hang Zhou: The 5th World Congress on Intelligent Control and Automation, 2004.
Shi Yuhui, Eberhart R. Parameter selection in particle swarm optimization[A]. Proc of the 7th Annual Conf on Evolutionary Programming[C]. Washington DC, 1998.591-600.
Zhang, Y.; Xie, Y.; Ye, Z. Development and Evaluation of a Multi-Agent Based Neuro-Fuzzy Arterial Traffic Signal Control System。Southwest Region Univ. Transportation Center, College Station, TX; Department of Transportation, Washington, DC. University Transportation Centers Program., 122p, Sep 2007