【作者】 柴琳果；

【导师】 蔡伯根；

【作者基本信息】 北京交通大学 ， 交通信息工程及控制， 2012， 硕士

【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。更多还原

【Abstract】 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.更多还原