城市区域交通信号控制方法及仿真研究

发布时间：2018-10-09 17:53

【摘要】：近年来,世界经济的飞速发展促进了城市化的步伐,随之而来的是城市交通拥堵的问题,这个问题制约了城市经济的发展,因此,缓解交通拥堵和提高交通运输能力已经是迫在眉睫的事情。而传统的控制方法在处理复杂的交通问题方面己经显的无能为力。为此,需要一些新的技术和手段从根本上来解决交通问题,智能交通系统是目前解决这一问题的有效方法。以城市区域的交通信号控制为研究对象,利用模糊逻辑、神经网络等智能控制方法,对城市交通信号控制展开研究,以缓解城市交通拥堵,提高交通运输效益。对城市单交叉口多相位的模糊控制进行了分析研究,在此基础上,结合交通流的特点设计了一种单交叉口两级模糊控制方法。在单交叉口信号控制的基础上,综合考虑多个交叉口之间的关联,设计了一种针对区域内多交叉口的协调模糊控制方法,该方法以交叉口车辆的平均延误作为信号控制的性能评价指标,综合考虑相邻交叉口之间道路上车辆的排队长度、各交叉口车辆的平均达到率等因素,以此来决定相位的切换顺序和绿灯分配时间,仿真结果验证了协调模糊控制的有效性。以交通流数据为基础,利用模糊神经网络对城市区域多交叉口的信号控制进行建模。模糊神经网络由模糊逻辑和神经网络有机结合而成,吸取了二者的优势,弥补了各自的缺点,有效地提高了整个系统对知识的学习和表达能力。仿真结果表明将其应用于区域多交叉口的信号控制取得了比较好的效果。
[Abstract]:In recent years, the rapid development of the world economy has promoted the pace of urbanization, followed by the problem of urban traffic congestion, which restricts the development of urban economy. It is urgent to alleviate traffic congestion and improve transportation capacity. However, the traditional control method has been powerless in dealing with complex traffic problems. Therefore, some new technologies and methods are needed to solve the traffic problem fundamentally. Intelligent Transportation system is an effective method to solve this problem. Taking the traffic signal control in the urban area as the research object, using the intelligent control methods such as fuzzy logic and neural network, the paper studies the urban traffic signal control in order to alleviate the urban traffic congestion and improve the transportation efficiency. Based on the analysis and study of multi-phase fuzzy control of urban single intersection, a two-stage fuzzy control method is designed based on the characteristics of traffic flow. On the basis of single intersection signal control and considering the correlation between multiple intersections, a coordinated fuzzy control method for multi-intersection in the region is designed. In this method, the average delay of vehicle at intersection is taken as the performance evaluation index of signal control, and the factors such as the queue length of vehicles on the road between adjacent intersections and the average rate of achievement of vehicles at each intersection are considered synthetically. The phase switching order and the green light allocation time are determined. The simulation results show the effectiveness of the coordinated fuzzy control. Based on the traffic flow data, the fuzzy neural network is used to model the signal control of multiple intersections in urban areas. Fuzzy neural network is an organic combination of fuzzy logic and neural network, which absorbs the advantages of both, makes up for their shortcomings, and effectively improves the ability of the whole system to learn and express knowledge. The simulation results show that the application of this method to regional multi-intersection signal control has achieved good results.
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U491.54

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