The invention discloses an urban traffic signal control method based on accelerated iterative learning control, and the method is characterized in that the method comprises the following steps: 1), building a traffic flow model based on iterative learning control; step 2), creating a historical iteration information database for establishing and training a random forest regression model; 3) in the iteration process of the traffic flow model based on iterative learning control, performing online optimization on the learning gain of the iterative learning control by using a particle swarm algorithm; and step 4), storing the iteration data which meets the condition and is based on the iterative learning control traffic flow model into a historical iteration information data set, and continuing to perfect the historical iteration information data set. According to the method, the problems of low convergence speed and low historical data utilization rate of an iterative learning control algorithm applied to urban traffic signal control at present are solved.
本发明公开了基于加速迭代学习控制的城市交通信号控制方法,其特征在于,包括以下步骤:步骤1)、建立基于迭代学习控制的交通流模型;步骤2)、创建历史迭代信息数据库,用来建立并训练随机森林回归模型;步骤3)、在基于迭代学习控制的交通流模型进行迭代的过程中,使用粒子群算法对迭代学习控制的学习增益在线寻优;步骤4)、将满足条件的基于迭代学习控制交通流模型的迭代数据保存至历史迭代信息数据集中,继续完善历史迭代信息数据集。该方法解决了目前应用在城市交通信号控制中迭代学习控制算法收敛速度慢以及历史数据利用率低的问题。
Urban traffic signal control method based on accelerated iterative learning control
基于加速迭代学习控制的城市交通信号控制方法
2023-11-14
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
An iterative learning approach for anticipatory traffic signal control on urban networks
Taylor & Francis Verlag | 2017
|European Patent Office | 2020
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