基于模糊控制的单交叉口信号控制方法与算法研究
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摘要
伴随着经济的高速增长,城市化水平正在迅速提高,居民的经济、文化活动日益频繁。这就导致了城市交通需求的快速增长。而随着城市交通的飞速发展,同时也带来了一系列问题如城市交通拥挤、交通延误、交通事故和交通污染等。交叉口是城市道路的交汇点,在道路网和交通流中起着十分重要的作用,上述问题也往往突出表现在交叉口处,因此对交叉口信号控制方法进行深入研究十分必要。由于交通系统是一种非线性的、时变的、滞后的大系统,采用传统的控制方法很难得到满意的效果,而模糊控制不需要建立精确的数学模型,因此本课题将采用这一理论对交叉口进行控制。
本文以单交叉路口主干道信号控制作为研究对象,以减少车辆平均延误作为控制目标,检测各进口车道上的车队长度,利用模糊时间序列预测下一周期各方向的交通流,根据交通流数据为下一周期选择合适的配时方案,即由专家经验和数学推导确定的最优相序安排。在一个周期内,将实时检测到的交通流作为自适应模糊控制器的输入参量,得到各通行相位的绿灯延时。运用了MATLAB对其控制效果进行仿真分析,结果表明该算法可有效降低通行车辆在交叉口的平均延误时间,使交叉口的通行能力提高。然后再分别利用蚁群算法和遗传算法对模糊控制器进行优化,对优化过的模糊控制器在不同的交通条件下进行了仿真,并与未经过优化的模糊控制器在相同的交通环境下对车辆的控制进行比较。仿真结果表明蚁群算法与模糊控制相结合的智能控制技术,更有效地降低通行车辆在交叉口的平均延误时间,更能适应复杂多变的交通环境。
With rapid economic growth, the level of urbanization is improved and t, economic and cultural activities of residents have become increasingly frequent. This led to the rapid urban growth in demand for transport. However, with the rapid development of urban traffic, it also brings some issues such as urban traffic congestion, traffic delays, traffic accidents and traffic pollution. Intersection is the point of intersection of Urban Roads, and plays an important role in the road network and traffic flow. Therefore, it is necessary for the intersection signal control method to in-depth study. Because the transport system is a nonlinear, time-varying, lagging large-scale systems, traditional control methods are very difficult to get satisfactory results. Fuzzy control does not need to build a precise mathematical model, so it will be used to control intersection in this paper.
In this paper, the reseaching subject is the single-intersection signal control method in main road, and control objective is to reduce the vehicle average delays. Based on detecting the length of vehicles in every import, the traffic flows of the next cycle will be predicted using fuzzy time series theory. According to predicted data of traffic flow, suitable allocation program for the next cycle is selected. That is, the optimal phase sequence arrangement is determined by the expertise and mathematical derivation. In a cycle, real-time traffic flows are inputing parameters of adaptive fuzzy controller, and the green light delays of the passage phase are outputing parameters . Then to optimize the fuzzy controller using respectively ant colony algorithm and genetic algorithms, MATLAB is used to simulate the optimized fuzzy controller under the different traffic environments, comparing with the general fuzzy controller which worked under the same traffic environment. The results indicates the algorithm can effectively reduce the average delay time of vehicles in the intersection, Correspondingly enhance the passing capacity of the intersection.
本文以单交叉路口主干道信号控制作为研究对象,以减少车辆平均延误作为控制目标,检测各进口车道上的车队长度,利用模糊时间序列预测下一周期各方向的交通流,根据交通流数据为下一周期选择合适的配时方案,即由专家经验和数学推导确定的最优相序安排。在一个周期内,将实时检测到的交通流作为自适应模糊控制器的输入参量,得到各通行相位的绿灯延时。运用了MATLAB对其控制效果进行仿真分析,结果表明该算法可有效降低通行车辆在交叉口的平均延误时间,使交叉口的通行能力提高。然后再分别利用蚁群算法和遗传算法对模糊控制器进行优化,对优化过的模糊控制器在不同的交通条件下进行了仿真,并与未经过优化的模糊控制器在相同的交通环境下对车辆的控制进行比较。仿真结果表明蚁群算法与模糊控制相结合的智能控制技术,更有效地降低通行车辆在交叉口的平均延误时间,更能适应复杂多变的交通环境。
With rapid economic growth, the level of urbanization is improved and t, economic and cultural activities of residents have become increasingly frequent. This led to the rapid urban growth in demand for transport. However, with the rapid development of urban traffic, it also brings some issues such as urban traffic congestion, traffic delays, traffic accidents and traffic pollution. Intersection is the point of intersection of Urban Roads, and plays an important role in the road network and traffic flow. Therefore, it is necessary for the intersection signal control method to in-depth study. Because the transport system is a nonlinear, time-varying, lagging large-scale systems, traditional control methods are very difficult to get satisfactory results. Fuzzy control does not need to build a precise mathematical model, so it will be used to control intersection in this paper.
In this paper, the reseaching subject is the single-intersection signal control method in main road, and control objective is to reduce the vehicle average delays. Based on detecting the length of vehicles in every import, the traffic flows of the next cycle will be predicted using fuzzy time series theory. According to predicted data of traffic flow, suitable allocation program for the next cycle is selected. That is, the optimal phase sequence arrangement is determined by the expertise and mathematical derivation. In a cycle, real-time traffic flows are inputing parameters of adaptive fuzzy controller, and the green light delays of the passage phase are outputing parameters . Then to optimize the fuzzy controller using respectively ant colony algorithm and genetic algorithms, MATLAB is used to simulate the optimized fuzzy controller under the different traffic environments, comparing with the general fuzzy controller which worked under the same traffic environment. The results indicates the algorithm can effectively reduce the average delay time of vehicles in the intersection, Correspondingly enhance the passing capacity of the intersection.
引文
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