自适应信号控制系统流量预测模型研究

为研究适合自适应信号控制系统的流量预测模型,利用ARIMA模型进行数据预处理的基础上,考虑高阶神经网络收敛速度慢及易陷入局部最小点的特点,通过在线调整学习率及引进动量法对其进行改进,得出基于ARIMA与改进的高阶神经网络的组合预测模型,试验表明预测的交通流量满足自适应信号控制系统实时在线多频优化的时间及精度要求.

Study on Traffic Flow Forecast Model in Adaptive Signal Control System

In order to develop traffic flow forecast model that suit to adaptive signal control system.In this paper,ARIMA model are used to pre-handle data.On-line adjust study rate and momentum are introduced to improve high-order generalized neural network(HGNN),so as to solve the problems of slow astringency and local minimum.Experimental results have shown that the precision and necessary time of prediction data is suit for adaptive signal control system to do frequent,real-time,on-line optimized.