平面交叉口信号模糊控制研究

发布时间：2018-04-01 16:08

本文选题：交通信号控制　切入点：人工智能　出处：《华中科技大学》2015年硕士论文

【摘要】：随着我国城市化步伐不断加快以及社会经济不断发展,城市人口大量增加,机动车数量呈爆发式增长,城市交通需求和交通基础设施之间的平衡被打破,交通拥堵日益严重,而且有从一线城市和大城市向二三线城市蔓延的趋势。单靠新建交通设施并不能解决需求和供给之间的矛盾,还应提升现有交通设施的通行能力,加强交通控制与管理,缓解交通拥堵以及相应的经济损失。传统交通信号控制采用严格的数学模型,和城市交通随机性、不确定性等特征难以吻合,本文考虑引入人工智能技术优化信号控制,借助其良好的非线性逼近能力、模拟人脑逻辑及稳定性好等优势,进一步提升交叉口服务水平。本文在了解交通信号控制发展现状与基本理论的基础上,将模糊控制理论引入进来,克服了传统基于数学模型的控制方法的缺陷,对传统模糊控制进行更深一步的优化,提出新的适应动态交通的信号控制方法,并且通过MATLAB对过程仿真,与定时控制、传统模糊控制在延误水平上进行对比,验证方法的有效性。然后进一步深化研究,针对可变相序模糊控制的不足之处,利用粒子群算法对参数进一步优化,进一步降低交叉口车辆平均延误,并通过MATLAB编程仿真验证方法有效性。最后通过案例实际应用证明了本文方法的优越性,并求取针对案例交叉口的控制器各参数对应的最优隶属度函数和模糊规则,为交叉口信号模糊控制提供一定的思路。
[Abstract]:With the accelerating pace of urbanization and the continuous development of social economy in China, the urban population is increasing greatly, the number of motor vehicles is increasing explosively, the balance between urban traffic demand and traffic infrastructure has been broken, and traffic congestion is becoming more and more serious. Moreover, there is a tendency to spread from first-tier cities and large cities to second-third-tier cities. Relying on new traffic facilities alone cannot solve the contradiction between demand and supply. We should also enhance the capacity of existing transport facilities and strengthen traffic control and management. Traditional traffic signal control adopts strict mathematical model, which is difficult to coincide with the characteristics of urban traffic randomness and uncertainty. This paper considers the introduction of artificial intelligence technology to optimize signal control. By virtue of its good nonlinear approximation ability and the advantages of simulating the human brain logic and stability, the service level of intersection is further improved. Based on the understanding of the current situation and basic theory of traffic signal control, The fuzzy control theory is introduced to overcome the defects of the traditional control method based on mathematical model, and the traditional fuzzy control is further optimized, and a new signal control method adapted to the dynamic traffic is proposed. And through the MATLAB to the process simulation, and timing control, the traditional fuzzy control in the delay level are compared to verify the effectiveness of the method. Then further research, aiming at the shortcomings of variable phase sequence fuzzy control, The particle swarm optimization algorithm is used to further optimize the parameters to further reduce the average vehicle delay at the intersection, and the effectiveness of the method is verified by MATLAB programming. Finally, the superiority of this method is proved by the practical application of a case. The optimal membership function and fuzzy rules corresponding to the parameters of the controller for the case intersection are obtained, which provides some ideas for the fuzzy control of the intersection signal.
【学位授予单位】：华中科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U491.54

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