基于模糊控制的单路口信号控制研究

发布时间：2018-02-22 05:57

本文关键词： 智能交通控制单路口模糊控制遗传算法　出处：《北京交通大学》2014年硕士论文　论文类型：学位论文

【摘要】：单路口是构成城市路网的基本元素,单路口控制影响着整个城市交通控制系统的运行效果。传统的路口控制方法已经发展的比较成熟,但是面临日趋复杂的交通问题表现的越来越力不从心；另一方面智能控制方法越来越多的用于城市交通控制,取得了较好的控制效果,因此本文开展了模糊控制方法在单路口控制中的应用研究。本文的主要工作如下：以车辆排队长度、平均车辆延误为性能指标,使用经典模糊控制方法控制多相位单路口,并利用Matlab进行仿真,将其控制效果与传统的路口控制方法进行比较。将一种改进的两级模糊控制方法应于单路口控制,该方法包含三个模糊控制模块：红灯模块、绿灯模块和决策模块。红灯模块与绿灯模块首先分别对相应车流进行处理,然后决策模块将二者的结果进行处理得出最终的控制策略。该方法优化了一般两级模糊的控制方式,解决了经典模糊控制中无相序变化对控制效果的影响以及模糊规则不容易确定的问题,改进了交通相位跳变的方式。论文使用Matlab在不同交通流状态下进行仿真并与经典模糊控制进行对比验证了其有效性。设计了一种改进的采用遗传算法优化模糊规则的模糊控制方法,该方法使用遗传算法对模糊规则进行有选择的优化,降低了不合理规则出现的几率,提高了遗传算法的收敛速度,使模糊控制具了有一定的实时学习能力。使用Matlab在不同的交通流下进行仿真实验,并与之前的两种模糊控制方法进行了对比,最终结果验证了该方法的有效性。
[Abstract]:Single intersection is the basic element of urban road network. Single intersection control affects the operation effect of the whole urban traffic control system. On the other hand, the intelligent control method is more and more used in urban traffic control, and has achieved better control effect. Therefore, the application of fuzzy control method in single intersection control is studied in this paper. The main work of this paper is as follows:. Taking the queue length and average vehicle delay as the performance index, the classical fuzzy control method is used to control the multi-phase single intersection. The simulation is carried out by using Matlab, and the control effect is compared with the traditional intersection control method. An improved two-level fuzzy control method is applied to single intersection control. The method consists of three fuzzy control modules: red light module, green light module and decision module. Then the decision module processes the two results to get the final control strategy. This method optimizes the general two-level fuzzy control method. The problem of the influence of the unordered change on the control effect and the difficulty of determining the fuzzy rules in the classical fuzzy control are solved. In this paper, Matlab is used to simulate the traffic phase jump in different traffic flow states, and compared with the classical fuzzy control, its effectiveness is verified. An improved fuzzy control method using genetic algorithm to optimize fuzzy rules is designed. The genetic algorithm is used to optimize fuzzy rules selectively, which reduces the probability of unreasonable rules. The convergence speed of genetic algorithm is improved, and the fuzzy control has certain real time learning ability. The simulation experiment with Matlab under different traffic flow is carried out, and compared with the two fuzzy control methods. The results show that the method is effective.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U491.54

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