基于人工智能的城市交通信号控制研究
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摘要
随着智能交通系统的发展,城市交通信号控制已成为最重要的研究方向之一。由于城市交通的复杂性,采用传统的控制方法已无法有效地解决交通信号控制问题,本文采用人工智能控制的方法对城市交通信号进行研究,主要包括以下几方面内容:
1.基于RBF神经网络建立了单交叉口自学习控制系统。该系统能够模拟交通警察指挥交通的思维过程,能够根据四相位交叉口各相位车辆的排队长度,对各个相位的绿信比和总的信号周期进行实时分配,并且随着交通状况的变化,可以对信号配时效果进行评价,根据车辆排队长度的变化,对信号作出调整,具有自学习功能。与传统的定时控制相比较,该系统能够更好地适应实际交通状况,提高交叉口的通行能力。
2.提出优化相序和模糊神经网络控制相结合的方法对交通干线进行实时控制,并建立了协调控制模型。该模型由两层控制器组成:交叉口控制器(下层)和协调单元控制器(上层)。下层控制器又由两个控制器组成:相序优化器和绿灯延时控制器,相序优化器用于调整交叉口的相序,绿灯延时控制器负责各个相位的绿灯延时时间。上层控制器用于调整干线的信号周期和两交叉口间的相位差,使干线上行驶的车流尽可能不遇到红灯,并且使各交叉口车辆排队长度尽可能短。仿真表明:该模型比传统的定时控制更能有效地减小平均车辆延误,验证了算法的有效性。
3.最后,对全文进行总结,并对进一步的研究提出一些展望。
With the development of ITS (Intelligent Transportation Systems), urban traffic signal control has been growing into one of the most important research aspects. As the complexity of urban transportation, traditional methods are not able to settle the problem of signal control efficiently. This paper deals with urban signal control by means of intelligent control algorithm, and focuses on the following aspects:
1. a self-learning signal control system based-on RBF Neural Network is established. This system can simulate the traffic police's experience. According to the queue length in each intersection, the system can give out both the signal cycle and the split of each intersection. Furthermore, it can evaluate the effect of the control with the changing of the traffic, and then adjust the signal. Simulation results reveal that the system can much more perfectly control the actual traffic condition and improve the passing ability of the intersection.
2. a kind of real time arterial signal control method based-on phase sequence optimization and Fuzzy Neural Networks is put forward in this paper, and a coordinated control module is established using two-stage controller: intersection controller (The lower course) and coordinated controller (The upper course). The lower course of the module consists of two controllers: phase sequencer and green-time delayer. The two controllers are used to optimize the phase sequence and adjust the green-time delay respectively. The upper course of the module is responsible for adjusting the signal cycle of the traffic trunk road and the offset between two intersections. The results of simulation prove that the proposed module has better performance than traditional fixed-time control.
3. In the end, summarizing the whole work and pointing out some content which would. be researched in the future.
1.基于RBF神经网络建立了单交叉口自学习控制系统。该系统能够模拟交通警察指挥交通的思维过程,能够根据四相位交叉口各相位车辆的排队长度,对各个相位的绿信比和总的信号周期进行实时分配,并且随着交通状况的变化,可以对信号配时效果进行评价,根据车辆排队长度的变化,对信号作出调整,具有自学习功能。与传统的定时控制相比较,该系统能够更好地适应实际交通状况,提高交叉口的通行能力。
2.提出优化相序和模糊神经网络控制相结合的方法对交通干线进行实时控制,并建立了协调控制模型。该模型由两层控制器组成:交叉口控制器(下层)和协调单元控制器(上层)。下层控制器又由两个控制器组成:相序优化器和绿灯延时控制器,相序优化器用于调整交叉口的相序,绿灯延时控制器负责各个相位的绿灯延时时间。上层控制器用于调整干线的信号周期和两交叉口间的相位差,使干线上行驶的车流尽可能不遇到红灯,并且使各交叉口车辆排队长度尽可能短。仿真表明:该模型比传统的定时控制更能有效地减小平均车辆延误,验证了算法的有效性。
3.最后,对全文进行总结,并对进一步的研究提出一些展望。
With the development of ITS (Intelligent Transportation Systems), urban traffic signal control has been growing into one of the most important research aspects. As the complexity of urban transportation, traditional methods are not able to settle the problem of signal control efficiently. This paper deals with urban signal control by means of intelligent control algorithm, and focuses on the following aspects:
1. a self-learning signal control system based-on RBF Neural Network is established. This system can simulate the traffic police's experience. According to the queue length in each intersection, the system can give out both the signal cycle and the split of each intersection. Furthermore, it can evaluate the effect of the control with the changing of the traffic, and then adjust the signal. Simulation results reveal that the system can much more perfectly control the actual traffic condition and improve the passing ability of the intersection.
2. a kind of real time arterial signal control method based-on phase sequence optimization and Fuzzy Neural Networks is put forward in this paper, and a coordinated control module is established using two-stage controller: intersection controller (The lower course) and coordinated controller (The upper course). The lower course of the module consists of two controllers: phase sequencer and green-time delayer. The two controllers are used to optimize the phase sequence and adjust the green-time delay respectively. The upper course of the module is responsible for adjusting the signal cycle of the traffic trunk road and the offset between two intersections. The results of simulation prove that the proposed module has better performance than traditional fixed-time control.
3. In the end, summarizing the whole work and pointing out some content which would. be researched in the future.
引文
铩颷1]史忠科,黄辉先等.交通控制系统导论[M].北京:科学出版社,2003.
[2]关朴芳,王进华.城市交通控制系统的简介与发展[J].福建电脑,2003,29(9):5-12.
[3]蔡文沁.我国智能交通系统发展的战略构想[J].交通运输系统工程与信息,2003,3(1):16-22.
[4]杨佩昆.智能交通运输系统体系结构[M].上海:同济大学出版社,2001,1-4.
[5]US DOT.National ITS Program Plan:Volume[R].1995.
[6]IVHS AMERICA.Strategic Plan for Intelligent Vehicle-Highway Systems in the UnitedStates[R].1992.
[7]Koutarou Katou.ITS System Architecture Development[A].Proceeding of the 5th WorldCongress on Intelligent Transport System[C].Seoul.1998.
[8]RL French,Associates.A Comparison of IVHS Progress in the United States,Europe,andJapan[J].IVHS AMERICA.1994.
[9]Robert L.French.From the 1986 Caltrans Conference to the 1995 World Congress:ADecade of Global Progress in Intelligent Transportation[A].Proceedings of the 2nd WorldCongress on ITS[C].1995.
[10]黄卫,陈里得.智能运输系统(ITS)概论[M].北京:人民交通出版社,1999,1-4.
[11]张国伍,彭宏勤.智能交通系统工程导论[M].北京:电子工业出版社,2003,4-10.
[12]严新平,吴超仲.智能运输系统原理、方法及应用[M].武汉:武汉理工大学出版社,2006,3,9-11.
[13]刘智勇等.城市交通信号控制的进展[A].第二届北京国际智能交通系统(ITS)技术研讨暨技术与产品展览会论文集[C].北京:2002,370-390.
[14]陆化普.智能运输系统[M].北京:人民交通出版社,2002.
[15]杨佩昆,张树升.交通管理与控制[M].北京:人民交通出版社,1995,86-87.
[16]王长军,卢静.基于思维过程的单交叉口多相位模糊控制算法研究[J].信息与控制,2004,2(3):14-16.
[17]黄辉先.城市交通信号优化控制方法的研究[D].西北工业大学博士论文.2000,13-15.
铩颷18]刘智勇.智能交通控制理论及其应用[M].北京:科学出版社,2003,89.
[19]孔庆华,张东煜.现代交通电子控制技术[M].哈尔滨:东北林业大学出版社,2004,17-20.
[20]翟润平,周彤梅.道路交通控制原理及应用[M].北京:中国人民公安大学出版社,2002,175-195.
[21]伍建国.平面交叉口控制策略及仿真系统研究[D]。西安公路交通大学硕士论文.2000,12-13.
[22]钟亚宜.城市交通信号的智能控制算法研究[D].北京工业大学工学硕士毕业论文.2005,14-16.
[23]Chard B M and Lines C J.TRANSYT:The latest developments[J].Traffic engineering andcontrol,1987,Vol.28.
[24]Martin PT and Hockaday S L M.SCOOT- An update[J].ITE Journal,1995,65(1):44-48.
[25]Hunt P Bet al.SCOOT- A traffic responsive method of coordinating signals[J].TRRLreport LR1014,1981,Crowthome.
[26]Hunt P Bet al.The SCOOT online traffic signal optimization technique[J].TrafficEngineering & Control 23,1982,190-192.
[27]Lowrie P R.SCATS principles,methodologies,algorithm.IEE Conf.On Road TrafficSignal[J].IEE Publication 207,England,1982,67-70.
[28]Sims A G and Finlay A B.SCATS,splits and offsets simplified(S.O.S)[J].ARRBProceedings Vol.12,Part 4,1984.
[29]Nathan H.Gartner University of Massachusetts,OPAC,Workshop on Adaptive TrafficSignal Control Systems Transportation Research Board[J].January 7,2001,WashingtonD.C.
[30]Farhad Pooran,PB Farradyne.Optimized Policies for Adaptive Control(OPAC).TRBSignal Systems Committee Adaptive Signal Control Workshop[J].January.2000,Washington D.C.
[31]Mirchandani P B and Head K L.A Real-time traffic signal control system:Architecturealgorithm and analysis[J].Transportation research part C 9,2001,415-432.
[32]Dell'Olmo P and Mirchandani P B.REALBAND:An approach for real-Time coordinationof traffic flows on networks[J].Transportation research record 1494,1995,106-116.
铩颷33]Sen S and Head K L.Controlled optimization of phases for intersection control[J].Transportation science,1997,Vol.31,No.1,5-17.
[34]Dave Miller,2070 &ITS CABINET WORKSHOP,August,2001.
[35]程海燕.智能交通信号协调控制系统研究[D].西北工业大学硕士学位论文.2006.3.
[36]史其信,熊辉.展望21世纪智能交通系统(ITS)的发展[J].黑龙江工程学院学报,Vol.15,No.1,Mar.2001,52-57.
[37]刘智勇,吴今培,李秀平,万百五.城市交通大系统递阶模糊神经网络控制[J].信息与控制,1997,26(6):441-448.
[38]李秀平,刘智勇等.平面交叉口的神经网络自学习控制方案[J].信息与控制,2001,30(1):76-79.
[39]许伦辉,衷路生等.基于神经网络实现交叉口多相位模糊控制[J].中南公路工程,2004,19(2):9-12.
[40]Dusan Teodorovic,Katarina Vukadinovic.Traffic Control and Transport Planning:A FuzzySets and Neural Networks Approach(International Series in Intelligent Technologies,13)[J].Kluwer Academic Publishers,1998.
[41]徐冬玲,方建安,邵世煌.交通系统的模糊控制及其神经网络实现[J].信息与控制,1992,21(2):74-78.
[42]闻新,周露等.Matlab神经网络仿真与应用[M].北京:科学出版社,2003.
[43]丹尼尔L.鸿洛夫,马休J.休伯.交通流理论[M].蒋瑛,任福田,肖秋生,安永溪,徐吉谦译.人民交通出版社,1983,87-95.
[44]PAPPIS C P,MAMDANI E H.A fuzzy logic controller for a traffic junction[J].IEEETrans on systems.Man and Cybernetics.1977,SMC-7(10):707-717.
[45]Nakatsuyarna M,Nagahashi H,Nishizura N,Fuzzy Logic Phase Controller for TrafficJunctions in the One-way Arterial Road[A].Proceedings of IFAC 9th Triennial WorldCongress[C].Budapest,Hugary,1994,13-18.
[46]Lee J H,Lee K M,Lee-Kwang H.Traffic control of intersection group based on fuzzy logic[A].in Proc.6th int.Fuzzy Systems Assoc World Congress[C],1995,465-468.
[47]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):99-105.
[48]沈国江,孙优贤.基于相序优化的多相位模糊交通控制[J].控制与决策,2002, 17(suppl.):654-658.
铩颷49]赵忠杰,刘小强,谢光秋.单交通路口变相位变周期信号控制[J].长安大学学报:自然科学版,2005,25(6):70-72.
[50]李瑞敏,陆化普,史其信.基于交通需求强度的路口多层模糊控制研究[J].武汉理工大学学报:交通科学与工程版,2006,30:1-4.
[51]刘智勇,梁渭清.城市交通信号控制的进展[J].公路交通科技,2003,9(12):6-11.
[2]关朴芳,王进华.城市交通控制系统的简介与发展[J].福建电脑,2003,29(9):5-12.
[3]蔡文沁.我国智能交通系统发展的战略构想[J].交通运输系统工程与信息,2003,3(1):16-22.
[4]杨佩昆.智能交通运输系统体系结构[M].上海:同济大学出版社,2001,1-4.
[5]US DOT.National ITS Program Plan:Volume[R].1995.
[6]IVHS AMERICA.Strategic Plan for Intelligent Vehicle-Highway Systems in the UnitedStates[R].1992.
[7]Koutarou Katou.ITS System Architecture Development[A].Proceeding of the 5th WorldCongress on Intelligent Transport System[C].Seoul.1998.
[8]RL French,Associates.A Comparison of IVHS Progress in the United States,Europe,andJapan[J].IVHS AMERICA.1994.
[9]Robert L.French.From the 1986 Caltrans Conference to the 1995 World Congress:ADecade of Global Progress in Intelligent Transportation[A].Proceedings of the 2nd WorldCongress on ITS[C].1995.
[10]黄卫,陈里得.智能运输系统(ITS)概论[M].北京:人民交通出版社,1999,1-4.
[11]张国伍,彭宏勤.智能交通系统工程导论[M].北京:电子工业出版社,2003,4-10.
[12]严新平,吴超仲.智能运输系统原理、方法及应用[M].武汉:武汉理工大学出版社,2006,3,9-11.
[13]刘智勇等.城市交通信号控制的进展[A].第二届北京国际智能交通系统(ITS)技术研讨暨技术与产品展览会论文集[C].北京:2002,370-390.
[14]陆化普.智能运输系统[M].北京:人民交通出版社,2002.
[15]杨佩昆,张树升.交通管理与控制[M].北京:人民交通出版社,1995,86-87.
[16]王长军,卢静.基于思维过程的单交叉口多相位模糊控制算法研究[J].信息与控制,2004,2(3):14-16.
[17]黄辉先.城市交通信号优化控制方法的研究[D].西北工业大学博士论文.2000,13-15.
铩颷18]刘智勇.智能交通控制理论及其应用[M].北京:科学出版社,2003,89.
[19]孔庆华,张东煜.现代交通电子控制技术[M].哈尔滨:东北林业大学出版社,2004,17-20.
[20]翟润平,周彤梅.道路交通控制原理及应用[M].北京:中国人民公安大学出版社,2002,175-195.
[21]伍建国.平面交叉口控制策略及仿真系统研究[D]。西安公路交通大学硕士论文.2000,12-13.
[22]钟亚宜.城市交通信号的智能控制算法研究[D].北京工业大学工学硕士毕业论文.2005,14-16.
[23]Chard B M and Lines C J.TRANSYT:The latest developments[J].Traffic engineering andcontrol,1987,Vol.28.
[24]Martin PT and Hockaday S L M.SCOOT- An update[J].ITE Journal,1995,65(1):44-48.
[25]Hunt P Bet al.SCOOT- A traffic responsive method of coordinating signals[J].TRRLreport LR1014,1981,Crowthome.
[26]Hunt P Bet al.The SCOOT online traffic signal optimization technique[J].TrafficEngineering & Control 23,1982,190-192.
[27]Lowrie P R.SCATS principles,methodologies,algorithm.IEE Conf.On Road TrafficSignal[J].IEE Publication 207,England,1982,67-70.
[28]Sims A G and Finlay A B.SCATS,splits and offsets simplified(S.O.S)[J].ARRBProceedings Vol.12,Part 4,1984.
[29]Nathan H.Gartner University of Massachusetts,OPAC,Workshop on Adaptive TrafficSignal Control Systems Transportation Research Board[J].January 7,2001,WashingtonD.C.
[30]Farhad Pooran,PB Farradyne.Optimized Policies for Adaptive Control(OPAC).TRBSignal Systems Committee Adaptive Signal Control Workshop[J].January.2000,Washington D.C.
[31]Mirchandani P B and Head K L.A Real-time traffic signal control system:Architecturealgorithm and analysis[J].Transportation research part C 9,2001,415-432.
[32]Dell'Olmo P and Mirchandani P B.REALBAND:An approach for real-Time coordinationof traffic flows on networks[J].Transportation research record 1494,1995,106-116.
铩颷33]Sen S and Head K L.Controlled optimization of phases for intersection control[J].Transportation science,1997,Vol.31,No.1,5-17.
[34]Dave Miller,2070 &ITS CABINET WORKSHOP,August,2001.
[35]程海燕.智能交通信号协调控制系统研究[D].西北工业大学硕士学位论文.2006.3.
[36]史其信,熊辉.展望21世纪智能交通系统(ITS)的发展[J].黑龙江工程学院学报,Vol.15,No.1,Mar.2001,52-57.
[37]刘智勇,吴今培,李秀平,万百五.城市交通大系统递阶模糊神经网络控制[J].信息与控制,1997,26(6):441-448.
[38]李秀平,刘智勇等.平面交叉口的神经网络自学习控制方案[J].信息与控制,2001,30(1):76-79.
[39]许伦辉,衷路生等.基于神经网络实现交叉口多相位模糊控制[J].中南公路工程,2004,19(2):9-12.
[40]Dusan Teodorovic,Katarina Vukadinovic.Traffic Control and Transport Planning:A FuzzySets and Neural Networks Approach(International Series in Intelligent Technologies,13)[J].Kluwer Academic Publishers,1998.
[41]徐冬玲,方建安,邵世煌.交通系统的模糊控制及其神经网络实现[J].信息与控制,1992,21(2):74-78.
[42]闻新,周露等.Matlab神经网络仿真与应用[M].北京:科学出版社,2003.
[43]丹尼尔L.鸿洛夫,马休J.休伯.交通流理论[M].蒋瑛,任福田,肖秋生,安永溪,徐吉谦译.人民交通出版社,1983,87-95.
[44]PAPPIS C P,MAMDANI E H.A fuzzy logic controller for a traffic junction[J].IEEETrans on systems.Man and Cybernetics.1977,SMC-7(10):707-717.
[45]Nakatsuyarna M,Nagahashi H,Nishizura N,Fuzzy Logic Phase Controller for TrafficJunctions in the One-way Arterial Road[A].Proceedings of IFAC 9th Triennial WorldCongress[C].Budapest,Hugary,1994,13-18.
[46]Lee J H,Lee K M,Lee-Kwang H.Traffic control of intersection group based on fuzzy logic[A].in Proc.6th int.Fuzzy Systems Assoc World Congress[C],1995,465-468.
[47]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):99-105.
[48]沈国江,孙优贤.基于相序优化的多相位模糊交通控制[J].控制与决策,2002, 17(suppl.):654-658.
铩颷49]赵忠杰,刘小强,谢光秋.单交通路口变相位变周期信号控制[J].长安大学学报:自然科学版,2005,25(6):70-72.
[50]李瑞敏,陆化普,史其信.基于交通需求强度的路口多层模糊控制研究[J].武汉理工大学学报:交通科学与工程版,2006,30:1-4.
[51]刘智勇,梁渭清.城市交通信号控制的进展[J].公路交通科技,2003,9(12):6-11.