摘要
为有效缓解城市道路交通拥堵问题,提高路网的运载能力,设计了一套交通信号灯的动态配时系统。系统基于模糊神经网络(FNN)控制思想,定义了道路拥塞因子作为输入,使用高斯函数作为隶属度函数将其模糊化,通过乘积法求解规则可信度,最终使用加权平均法去模糊化后得到下一次的通行时长。通过VISSIM7.0进行仿真验证,基于模糊神经网络(FNN)的交通信号灯配时系统可以合理地调节信号灯通行时长,有效地降低交通拥塞情况的发生。
In order to effectively alleviate the problem of urban road traffic congestion and improve the carrying capacity of the road network,a set of dynamic timing system for traffic signal lights was designed.The system is based on the fuzzy neural network(FNN)control idea,defines the road congestion factor as input,uses Gaussian function as the membership function to fuzz it,solves the rule credibility by the product method,and finally uses the weighted average method to defuzzify it.The duration of the next pass.Through simulation verification of VISSIM7.0,the traffic signal timing system based on fuzzy neural network(FNN)can reasonably adjust the traffic time of traffic lights and effectively reduce the occurrence of traffic congestion.
作者
王晨晨
崔文旭
赵韦婷
吴琦
孙万众
吴俊华
WANG Chenchen;CUI Wenxu;ZHAO Weiting;WU Qi;SUN Wanzhong;WU Junhua(School of Computer Science,Qufu Normal University,Shandong 276826,China)
出处
《电子技术（上海）》
2021年第8期20-22,共3页
Electronic Technology
关键词
动态配时
道路拥塞因子
模糊神经网络
仿真验证
交通信号
dynamic timing
road congestion factor
fuzzy neural network
simulation verification
traffic signal
