干道信号协调控制优化方法

Method to Optimize Traffic Signal Coordination on Urban Trunk Road

针对干道信号协调控制中控制策略及其控制优化参数设置上存在的问题,考虑到智能控制中Sugeno模糊推理所具有的复杂系统动态性能表达能力和神经网络控制方法所具有的学习能力,结合两者的优势建立了1种基于模糊神经网络的交通信号协调控制模型,实现对绿信比与相位差的优化,使用微观交通仿真软件Vissim对干线交通进行了仿真研究,仿真结果表明,该方法能更为有效地减小平均车辆延误。

The existing problems of control strategy and the settings of optimum control parameters in arterial signal coordination motivate this study. This paper considers the capability of Sugeno fuzzy reasoning in explaining complex dynamic system and the learning ability of neural network control method, and establishes a traffic signal coordination control model based on the combination of the two-fuzzy neural network. The model is used to optimize the signal split and offset. The microscopic traffic simulation software VISSIM is used to simulate and check the performance of the system along an urban arterial road. The simulation results clearly show that this method can effectively reduce average vehicle delay.