一种交通信号自学习控制方法及仿真实现

将模糊理论和机器学习应用到交通信号控制过程中，提出了一种基于遗传算法的单路口交通信号模糊控制方法。通过对到达车辆数目的模糊分类，将不同车辆数目到达情况下的信号控制决策方案以规则集的形式存储在知识库中，在交通信号控制过程中使用遗传算法对规则集进行改进。编制该控制方法的仿真程序，对该方法的控制效果与定时控制和感应控制进行了模拟比较，仿真实验的结果说明该方法的控制效果明显优于传统控制方式。

A Self-learning Traffic Signal Control Approach and Simulation

This paper applies fuzzy theory and machine learning in the process of traffic signal control. It provides a fuzzy traffic signal control approach based on genetic algorithms for isolated intersection. Through fuzzy classifying the number of arrived cars, this paper puts decision schemes of signal control in different conditions of cars?arriving as rule-set into knowledge-database. It applies genetic algorithm to improve the rule-sets in the course of traffic signal controlling. After programming the simulation program of this control approach and simulating, this paper compares the control effect of this new approach with fixed-time control method and actuated control method. The result of simulating illustrates that the effect of the new approach is obviously better than the traditional ones.