基于车路协同系统的交通仿真方法研究
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摘要
交通系统是一个随机性大、非线性、关键元素众多的典型复杂大系统,单从传统的道路和车辆的角度考虑,难以解决不断恶化的交通拥堵、事故频发、环境污染等问题。车路协同系统是利用车车、车路间的信息交互建立人、车、路一体化协同运行的智能交通运输系统,对提高交通运输系统的效率和安全性,实现交通系统的可持续性发展均具有十分重要的意义。车路协同技术在我国处于起步阶段,迫切需要对车路协同系统从理论、体系、技术等多个层次进行深入研究,尤其是基于车路协同的系统仿真方法研究将会为实际系统的研究提供理论支持和实践指导。
本文重点研究车路协同系统的交通仿真方法,使用Paramics软件建立路网,并通过系统编程器对路网关键元素的信息进行提取与控制。论文系统研究了车路协同系统交通仿真的基础理论,剖析了车路协同仿真涉及到的关键问题。由于仿真数据的获取是仿真运行的基础,系统分析了Paramics数据的提取与控制机制,研究了数据获取的两种形式,能够通过API(Application Programming Interface)应用程序接口实时获取或者仿真结束后读取仿真数据文件；针对车辆坐标转换误差问题,使用了适用的地图匹配方法予以校准。
本文选取了车路协同控制模式下典型的跟驰模型和交通信号控制模型进行深入研究。对传统的车辆跟驰模型进行了分析,针对Paramics使用的跟驰模型中存在安全距离取值不准确的问题,基于Paramics跟驰模型与车路协同数据交互原理设计了一种新的最小安全距离跟驰模型；提出了一种车路协同控制模式下基于相位差模糊调节的干线信号控制策略,以干线交叉口冲突相位车辆与干线车辆比值作为输入,相位差增益作为输出量,对相位差和干线绿波带进行优化,能够有效提高道路通行效率。
最后,设计了车路协同系统交通仿真平台,在该平台上与传统安全距离跟驰模型与相位差未调节情况下信号控制模型的仿真数据进行对比,验证了本文提出的基于车路协同的最小安全距离跟驰模型和基于车路协同的干线相位差模糊调节信号控制模型具备更好的适用性与交通控制效率。
The transport system is a typical complex system which is randomness, non-linear and has lots of key elements. Considering traditionally only form the roads and vehicles, it is difficult to solve the problems of the worsening traffic congestion, frequent accidents hair, environmental pollution, etc. CVIS is a people, vehicles, roads integration Intelligent Transport System which uses of the information between vehicle-vehicle and vehicle-road. It has very important significance to improve the efficiency and security of the transport system and to achieve the sustainable development of the transport system. The CVIS is in its infancy in China now. And we need to research from multiple levels like theory, system and technology. The study of CVIS simulation method will provide actual system theoretical support and practical guidance.
This dissertation focuses on the traffic simulation of CVIS. We use Paramics to establish a road network, and complying extracting information and controlling the key elements of the system by programmer. The paper studies the basic theory of the CVIS traffic simulation comprehensively, and analyzes the key issues related to the simulation. As the simulation data is the basis of the simulation, the paper researches the mechanisms of the data extraction and data control. There are two ways getting data, one is real-time style through the API application program and the other is to read the simulation data files after the simulation. And to the deviation in the vehicle coordinate conversion, the paper uses applicable map matching method to calibrate.
This dissertation selects the car following model and traffic signal control model in CVIS to study. Through the study of the traditional car following model and the problem that in car following model in Paramics the safe distance value is not accurate, we design of a new minimum safe distance car following model based on Paramics car following model and the data exchange in CVIS. And we proposed a signal control strategy in main road control in CVIS based on fuzzy adjust on traffic phases. We use the ratio of the car total number in non-main road to the car total number in main road as the input, the phase gain as output to improve the phase and Green Wave to improve the efficiency of road traffic. Finally, we design the CVIS traffic simulation platform and compare it with the traditional safe distance of the car following model and phase adjustment with non-fuzzy control model. Form the simulation data, we can see that the minimum safety distance car following model and the phase fuzzy control signal method based on CVIS is more efficiency.
本文重点研究车路协同系统的交通仿真方法,使用Paramics软件建立路网,并通过系统编程器对路网关键元素的信息进行提取与控制。论文系统研究了车路协同系统交通仿真的基础理论,剖析了车路协同仿真涉及到的关键问题。由于仿真数据的获取是仿真运行的基础,系统分析了Paramics数据的提取与控制机制,研究了数据获取的两种形式,能够通过API(Application Programming Interface)应用程序接口实时获取或者仿真结束后读取仿真数据文件；针对车辆坐标转换误差问题,使用了适用的地图匹配方法予以校准。
本文选取了车路协同控制模式下典型的跟驰模型和交通信号控制模型进行深入研究。对传统的车辆跟驰模型进行了分析,针对Paramics使用的跟驰模型中存在安全距离取值不准确的问题,基于Paramics跟驰模型与车路协同数据交互原理设计了一种新的最小安全距离跟驰模型；提出了一种车路协同控制模式下基于相位差模糊调节的干线信号控制策略,以干线交叉口冲突相位车辆与干线车辆比值作为输入,相位差增益作为输出量,对相位差和干线绿波带进行优化,能够有效提高道路通行效率。
最后,设计了车路协同系统交通仿真平台,在该平台上与传统安全距离跟驰模型与相位差未调节情况下信号控制模型的仿真数据进行对比,验证了本文提出的基于车路协同的最小安全距离跟驰模型和基于车路协同的干线相位差模糊调节信号控制模型具备更好的适用性与交通控制效率。
The transport system is a typical complex system which is randomness, non-linear and has lots of key elements. Considering traditionally only form the roads and vehicles, it is difficult to solve the problems of the worsening traffic congestion, frequent accidents hair, environmental pollution, etc. CVIS is a people, vehicles, roads integration Intelligent Transport System which uses of the information between vehicle-vehicle and vehicle-road. It has very important significance to improve the efficiency and security of the transport system and to achieve the sustainable development of the transport system. The CVIS is in its infancy in China now. And we need to research from multiple levels like theory, system and technology. The study of CVIS simulation method will provide actual system theoretical support and practical guidance.
This dissertation focuses on the traffic simulation of CVIS. We use Paramics to establish a road network, and complying extracting information and controlling the key elements of the system by programmer. The paper studies the basic theory of the CVIS traffic simulation comprehensively, and analyzes the key issues related to the simulation. As the simulation data is the basis of the simulation, the paper researches the mechanisms of the data extraction and data control. There are two ways getting data, one is real-time style through the API application program and the other is to read the simulation data files after the simulation. And to the deviation in the vehicle coordinate conversion, the paper uses applicable map matching method to calibrate.
This dissertation selects the car following model and traffic signal control model in CVIS to study. Through the study of the traditional car following model and the problem that in car following model in Paramics the safe distance value is not accurate, we design of a new minimum safe distance car following model based on Paramics car following model and the data exchange in CVIS. And we proposed a signal control strategy in main road control in CVIS based on fuzzy adjust on traffic phases. We use the ratio of the car total number in non-main road to the car total number in main road as the input, the phase gain as output to improve the phase and Green Wave to improve the efficiency of road traffic. Finally, we design the CVIS traffic simulation platform and compare it with the traditional safe distance of the car following model and phase adjustment with non-fuzzy control model. Form the simulation data, we can see that the minimum safety distance car following model and the phase fuzzy control signal method based on CVIS is more efficiency.
引文
[1]孙孝文.和谐交通体系构建研究[D].武汉理工大学,2007
[2]李仁涵.我国大都市交通圈发展模式的研究[D].同济大学,2007
[3]覃明贵.城市道路交通数据挖掘研究与应用[D].复旦大学,2010
[4]杨庆芳.先进的交通管理系统关键理论与方法研究[D].吉林大学,2004
[5]胡志文.道路交通安全管理社会化立法问题研究[D].湘潭大学,2005
[6]尹洪英.道路交通运输网络脆弱性评估模型研究[D].上海交通大学,2011
[7]刘爽.基于系统动力学的大城市交通结构演变机理及实证研究[D].北京交通大学,2009
[8]孙舵.道路交通流元胞自动机模型中的相变现象研究[D].中国科学技术大学,2011
[9]成卫.城市道路交通事故与交通冲突技术理论模型及方法研究[D].吉林大学,2004
[10]庄焰,胡明伟,李德宏.微观交通仿真软件PARAMICS在ITS模拟和评价中的应用[J].系统仿真学报,2005,17(7)：1655-1659.
[11]Quadstone Paramics 4.2 User Manual [K].Edinburgh, Scotland: Quadstone Ltd, 2003.
[12]Richard L Church, Val Noronha. Translating GIS Street Network Files for Use with Paramics [R].USA: Vehicle Intelligence & Transportation Analysis Laboratory, University of California, 2003.
[13]龚峻峰,余志,何兆成,欧阳祥波.从AutoCAD到Paramics的数据转换[J].公路交通科技(应用技术版),2005,11：146-149.
[14]齐锐,屈韶琳,阳琳赟.用MapX开发地理信息系统[M].北京：清华大学出版社,2003.
[15]Estrin D. Improving simulation for network research[R].Technical Report,99-702b,University of Southern California,1999.
[16]Noronha C, Tobagi F. Evaluation of Multicast Routing Algorithms for Multimedia Streams[A].In Proceedings of the IEEE International Telecommunications Symposium[C].Rio de Janeiro,Brazil,1994.
[17]卢凯,徐建闽,陈思溢.通用干道双向绿波协调控制模型及其优化求解[J].控制理论与应用,2011,28(4)：551-555.
[18]OPNET Technologies Inc. OPNET[DB/OL].http://www.mil3.com.2005-05\
[19]Alaettinoglu C, Dussa-Zieger K, Matta I. MaRS User's Manual[R].Technical Report UMIACS-TR-91-80,CS-TR-2687, Department of Computer Science, University of Maryland, College Park, 1991.
[20]余燕平.多播路由算法研究[D].杭州：浙江大学,2002.
[21]王征应.高速网络QoS路由技术的研究及路由仿真平台的研制[D].武汉：华中科技大学,2001.
[22]Kompella V P, Pasquale J C, Polyzos G C. Multicasting Routing for Multimedia Communication[J].IEEE/ACM Trans on Networking,1993,1(3):286-292.
[23]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.
[24]李志明、闰小勇.基于遗传算法的交通仿真模型参数校正方法研究[J].交通与安全,2006.4：25.31.
[25]Zhizhou Wu, Jian Sun, Xiaoguang. Calibration of VIS SIM for ShangHai Expressway Using Genetic Algorithm[C]. orlalldo, Florida:Simulation Conference,2005:2645-2648.
[26]孙剑,杨晓光,刘女：f=德.微观交通仿真系统参数校正研究[J].系统仿真学报,2007,19(1)：48—50.
[27]GARTNERNH, STAMATIADISC. Arterial based control of traffic flow in urban grid net works[J].Mathematical and Computer Modelling,2002,35(5):657-671.
[28]陈魁.试验设计与分析[M].北京：清华出版社,1996
[29]CHAUDHARYNA, MESSERCJ. Pas serIV-96,version211,user/reference manual[R].Austin: Texas A&M University System, 1996.
[30]柳伟伟,贺佳.析因设计重复测量白量的统计分析及SAS程序实例[J].中国卫生统计,2007,24(2)：146-148
[31]王晓瑜,陈国华.2k析因试验设计及其在发动机排放中的运用[J].内燃机工程,2004,25(2)：18-22.
[32]杨晓光,陈白磊,彭国雄1行人交通控制信号设置方法研究,中国公路学报,2001；4(1)：73-76
[33]孙树栋,周明.遗传算法原理及应用[M].北京：国防工业出版社,1999.
[34]陆金桂,李谦.遗传算法原理及其工程应用[M].徐州：中国矿业大学出版社.1997
[35]景春光,王殿海1典型交叉口混合交通冲突分析与处理方法,土木工程学报,2004；37(6)：98)100
[36]Binning J C, Burtenshaw q and Crabtree M, TRANSYT USER GUIDE[M].2008:TRL Software Bureau, United Kingdom.
[37]Hunt P B, Robertson D L, and Bretherton R D, The SCOOT on-line traffic signal optimization technique[J]. Traffic Engineering & Control, 1982. 23(4): p.190-192.
[38]Sims A G and Dobinson K W, The Sydney coordinated adaptive traffic (SCAT) system philosophy and benefits[J]. IEEE Transactions on Vehicular Technology, 1980.29(2): p. 130-137.
[39]全永染,城市交通控制[M].1989,北京：人民交通出版社.
[40]卢凯,徐建闽,干道协调控制相位筹模型及其优化方法[[J].中国公路学报,2008.21(1)：p.83-88.
[41]栗红强.城市交通控制信号配时参数优化方法研究[D].长春：吉林大学,2004.
[42]Webster F V, Traffic signal settings, in Road Res. Lab. Tech. Rep. No.39. 1958:
[43]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
[44]张海军,杨晓光,张钰.两种交叉口信号相位设计方法的比较[J].交通与计算机,2005,1(23)：4-6
[45]全永粲.城市交通控制[M].北京：人民交通出版社,1989.3-65
[46]杨兆升,王爽.基于Vissim仿真软件的交通信号配时研究[J].交通与计算机,2005,1(23)：8-11
[47]马建明,张海涛,任福田,等.信号交叉口交通组织与管理方案优化分析系统设计[J].中国公路学报,2001,18(5)：1-2
[48]卢凯,徐建闽,叶瑞敏.经典干道协调控制信号配时数解算法的改进[J].公路交通科技,2009,26(1)：120-124,129.
[49]王殿海,李凤,宋现敏.干线协调控制中公共周期优化方法研究[J].交通信息与安全,2009,27(5)：10-13,23.
[50]陈宁宁,何兆成,余志.考虑动态红灯排队消散时间的改进MAXBAND模型[J].武汉理工大学 学报：交通科学与工程版,2009,33(5)：843-847.
[51]卢凯,徐建闽,李轶舜.进口单独放行方式下的干道双向绿波协调控制数解算法[J].中国公路学报,2010,23(3)：95-101.
[2]李仁涵.我国大都市交通圈发展模式的研究[D].同济大学,2007
[3]覃明贵.城市道路交通数据挖掘研究与应用[D].复旦大学,2010
[4]杨庆芳.先进的交通管理系统关键理论与方法研究[D].吉林大学,2004
[5]胡志文.道路交通安全管理社会化立法问题研究[D].湘潭大学,2005
[6]尹洪英.道路交通运输网络脆弱性评估模型研究[D].上海交通大学,2011
[7]刘爽.基于系统动力学的大城市交通结构演变机理及实证研究[D].北京交通大学,2009
[8]孙舵.道路交通流元胞自动机模型中的相变现象研究[D].中国科学技术大学,2011
[9]成卫.城市道路交通事故与交通冲突技术理论模型及方法研究[D].吉林大学,2004
[10]庄焰,胡明伟,李德宏.微观交通仿真软件PARAMICS在ITS模拟和评价中的应用[J].系统仿真学报,2005,17(7)：1655-1659.
[11]Quadstone Paramics 4.2 User Manual [K].Edinburgh, Scotland: Quadstone Ltd, 2003.
[12]Richard L Church, Val Noronha. Translating GIS Street Network Files for Use with Paramics [R].USA: Vehicle Intelligence & Transportation Analysis Laboratory, University of California, 2003.
[13]龚峻峰,余志,何兆成,欧阳祥波.从AutoCAD到Paramics的数据转换[J].公路交通科技(应用技术版),2005,11：146-149.
[14]齐锐,屈韶琳,阳琳赟.用MapX开发地理信息系统[M].北京：清华大学出版社,2003.
[15]Estrin D. Improving simulation for network research[R].Technical Report,99-702b,University of Southern California,1999.
[16]Noronha C, Tobagi F. Evaluation of Multicast Routing Algorithms for Multimedia Streams[A].In Proceedings of the IEEE International Telecommunications Symposium[C].Rio de Janeiro,Brazil,1994.
[17]卢凯,徐建闽,陈思溢.通用干道双向绿波协调控制模型及其优化求解[J].控制理论与应用,2011,28(4)：551-555.
[18]OPNET Technologies Inc. OPNET[DB/OL].http://www.mil3.com.2005-05\
[19]Alaettinoglu C, Dussa-Zieger K, Matta I. MaRS User's Manual[R].Technical Report UMIACS-TR-91-80,CS-TR-2687, Department of Computer Science, University of Maryland, College Park, 1991.
[20]余燕平.多播路由算法研究[D].杭州：浙江大学,2002.
[21]王征应.高速网络QoS路由技术的研究及路由仿真平台的研制[D].武汉：华中科技大学,2001.
[22]Kompella V P, Pasquale J C, Polyzos G C. Multicasting Routing for Multimedia Communication[J].IEEE/ACM Trans on Networking,1993,1(3):286-292.
[23]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.
[24]李志明、闰小勇.基于遗传算法的交通仿真模型参数校正方法研究[J].交通与安全,2006.4：25.31.
[25]Zhizhou Wu, Jian Sun, Xiaoguang. Calibration of VIS SIM for ShangHai Expressway Using Genetic Algorithm[C]. orlalldo, Florida:Simulation Conference,2005:2645-2648.
[26]孙剑,杨晓光,刘女：f=德.微观交通仿真系统参数校正研究[J].系统仿真学报,2007,19(1)：48—50.
[27]GARTNERNH, STAMATIADISC. Arterial based control of traffic flow in urban grid net works[J].Mathematical and Computer Modelling,2002,35(5):657-671.
[28]陈魁.试验设计与分析[M].北京：清华出版社,1996
[29]CHAUDHARYNA, MESSERCJ. Pas serIV-96,version211,user/reference manual[R].Austin: Texas A&M University System, 1996.
[30]柳伟伟,贺佳.析因设计重复测量白量的统计分析及SAS程序实例[J].中国卫生统计,2007,24(2)：146-148
[31]王晓瑜,陈国华.2k析因试验设计及其在发动机排放中的运用[J].内燃机工程,2004,25(2)：18-22.
[32]杨晓光,陈白磊,彭国雄1行人交通控制信号设置方法研究,中国公路学报,2001；4(1)：73-76
[33]孙树栋,周明.遗传算法原理及应用[M].北京：国防工业出版社,1999.
[34]陆金桂,李谦.遗传算法原理及其工程应用[M].徐州：中国矿业大学出版社.1997
[35]景春光,王殿海1典型交叉口混合交通冲突分析与处理方法,土木工程学报,2004；37(6)：98)100
[36]Binning J C, Burtenshaw q and Crabtree M, TRANSYT USER GUIDE[M].2008:TRL Software Bureau, United Kingdom.
[37]Hunt P B, Robertson D L, and Bretherton R D, The SCOOT on-line traffic signal optimization technique[J]. Traffic Engineering & Control, 1982. 23(4): p.190-192.
[38]Sims A G and Dobinson K W, The Sydney coordinated adaptive traffic (SCAT) system philosophy and benefits[J]. IEEE Transactions on Vehicular Technology, 1980.29(2): p. 130-137.
[39]全永染,城市交通控制[M].1989,北京：人民交通出版社.
[40]卢凯,徐建闽,干道协调控制相位筹模型及其优化方法[[J].中国公路学报,2008.21(1)：p.83-88.
[41]栗红强.城市交通控制信号配时参数优化方法研究[D].长春：吉林大学,2004.
[42]Webster F V, Traffic signal settings, in Road Res. Lab. Tech. Rep. No.39. 1958:
[43]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
[44]张海军,杨晓光,张钰.两种交叉口信号相位设计方法的比较[J].交通与计算机,2005,1(23)：4-6
[45]全永粲.城市交通控制[M].北京：人民交通出版社,1989.3-65
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