基于改进动态递归神经网络的交通量短时预测方法  

Short-term traffic flow prediction method based on improved dynamic recurrent neural network

作　　者：杨庆芳[1,2] 张彪[2] 高鹏[2] 

机构地区：[1]吉林大学汽车仿真与控制国家重点实验室,长春130022 [2]吉林大学交通学院,长春130022

出　　处：《吉林大学学报（工学版）》2012年第4期887-891,共5页Journal of Jilin University:Engineering and Technology Edition

基　　金：'863'国家高技术研究发展计划项目(2009AA11Z208)

摘　　要：针对已有基于改进动态递归神经网络预测方法的不足,并充分考虑交通流本身所存在的复杂性、非线性和不确定性特点,提出了一种基于可变增益Elman神经网络的交通量短时预测方法。该方法通过引入一个基于实时误差分析的可变增益因子,实现了网络的实时更新。通过长春市人民大街的实测数据对方法进行了验证。试验结果表明,本文方法在网络收敛时间和预测精度方面均优于已有的基于Elman神经网络的预测模型。To deal with the deficiencies of the existing prediction models based on dynamic recurrent neural network, considering the characteristics of the traffic flow itself such as complexity, nonlinearity and uncertainty, a short-term traffic flow prediction method using Elman neural network with variable gain was proposed. The real-time updates of the network were achieved by introducing a variable gain factor based on the real-time error analysis. The method was verified by the field measured data from Renmin Street, Changchun City. The results show that comparing with the existing Elman network based models the proposed method is superior on the aspects of convergence time and prediction accuracy.

关 键 词：交通运输工程 交通流量预测 动态递归网络 可变增益因子 

分 类 号：U491.2[交通运输工程—交通运输规划与管理]