车路协同环境下交通控制与诱导协同研究

发布时间：2018-04-20 13:47

本文选题：车路协同 + 交通控制　；参考：《兰州理工大学》2014年硕士论文

【摘要】：城市交通的快速发展使得许多城市都或多或少的面临着交通拥堵的问题,因此,如何保证城市交通系统的有序运行就成为了交通工程领域研究的热点。交通控制与交通诱导是解决城市交通问题的基本手段,二者之间既相互联系、缺一不可,又互相独立、各有侧重,因此,只有使二者有效的协同起来才能更好的保证交通系统的有序运行。车路协同系统是智能交通系统的重要组成部分,也是近年来快速发展起来的用于解决交通问题的有效方法,因此,将车路协同系统引入到交通控制与诱导协同的研究之中对解决交通问题、适应交通的发展速度和进一步提高交通系统的运行效能具有重要的意义。本文在车路协同环境下提出了一种基于实时车速和排队长度的交通控制与诱导协同模型来提高交通系统的效能。车路协同系统是智能交通的重要组成部分,其对道路和车辆两方面的因素进行综合考虑,拥有先进的路侧系统和车载系统,通过有线或者无线通讯方式可以方便的获取车速和排队长度等路况信息,在车速和排队长度的基础上,以路网总行程时间作为目标函数建立交通控制与诱导协同模型,同时加之相应的约束条件,对各个控制和诱导参数进行限制,以达到减小路网总行程时间和均衡路网流量的最终目标。交通控制与诱导协同模型的求解采用对问题具有良好的鲁棒性和操作简单的遗传算法进行求解,文中分析设计了遗传算法中的编码方式以及选择、变异、交叉等遗传算子,将目标函数直接转化为适应度函数,然后通过惩罚函数将协同模型中的约束条件加入到适应度函数之中,从而简化遗传算法对所求参数的求解步骤,使得模型的求解易于在仿真软件中实现。文章最后通过在Vissim微观交通仿真软件中建立了一个小型路网来模拟车路协同环境中的数据获取,小型路网由8条道路和四个交叉口组成,在路网中的每条道路上相应的断面分别设置数据检测点和排队长度计数器来采集平均车速和排队长度,通过在路网中进行路径的设置来模拟实际的交通环境中的OD交通量,然后在Matlab中利用遗传算法对协同模型进行仿真求解,经过以上的模拟和仿真后得到的结果证明了本文的方法和模型能够有效的减小路网的总行程时间和均衡路网流量。
[Abstract]:With the rapid development of urban traffic, many cities are more or less faced with the problem of traffic congestion. Therefore, how to ensure the orderly operation of urban traffic system has become a hot spot in the field of traffic engineering. Traffic control and traffic guidance are the basic means to solve urban traffic problems. Only by making the two effective cooperation can better ensure the orderly operation of the traffic system. The vehicle-road cooperative system is an important part of intelligent transportation system, and it is also an effective method to solve traffic problems developed rapidly in recent years. It is of great significance to introduce the vehicle-road coordination system into the study of traffic control and guidance coordination in order to solve traffic problems adapt to the speed of traffic development and further improve the efficiency of traffic system. In this paper, a traffic control and guidance collaborative model based on real-time speed and queue length is proposed to improve the efficiency of traffic system. The vehicle-road coordination system is an important part of intelligent transportation. It considers the road and vehicle factors comprehensively, and has advanced road-side system and vehicle-borne system. Traffic information such as speed and queue length can be easily obtained by wired or wireless communication. On the basis of speed and queue length, the traffic control and induced cooperative model is established based on the total travel time of road network as the objective function. At the same time, combined with the corresponding constraints, the control and induced parameters are restricted to achieve the ultimate goal of reducing the total travel time of the road network and balancing the network flow. The cooperative model of traffic control and guidance is solved by genetic algorithm, which has good robustness and simple operation. In this paper, genetic operators such as selection, mutation and crossover are analyzed and designed. The objective function is transformed directly into fitness function, and then the constraints in the cooperative model are added to the fitness function by penalty function, which simplifies the steps of solving the parameters obtained by genetic algorithm. The solution of the model is easy to be realized in the simulation software. In the end, a small road network is built in the Vissim microscopic traffic simulation software to simulate the data acquisition in the collaborative environment. The small road network is composed of 8 roads and four intersections. Data detection points and queue length counters are set up in each section of the road network to collect the average speed and queue length, and the OD traffic volume in the actual traffic environment is simulated by setting the path in the road network. Then genetic algorithm is used to solve the cooperative model in Matlab. The simulation and simulation results show that the proposed method and model can effectively reduce the total travel time and equilibrium network flow of the road network.
【学位授予单位】：兰州理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U495;U491.54

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