摘要
为了提高高速道路交通流量预测的准确性,针对交通流量数据具有周期性和随机波动性双重特性,提出基于小波分析的分数阶灰色和ARMA组合预测模型.首先根据交通流量数据的周期性特征,提取出周期分量,然后利用小波变换将提取周期分量后的剩余分量分解为高频部分和低频部分,高频部分采用分数阶灰色预测,低频部分采用ARMA预测,最后将剩余分量预测值和周期分量进行累加,得到最终的预测结果.仿真结果表明,组合模型可以提高短时交通流量预测的精度,对改善交通情况有着实际意义.
In order to improve the accuracy of traffic flow forecasting,a fractional gray and ARMA combination forecasting model based on wavelet analysis was proposed according to the dual characteristics of traffic flow,which was periodic and stochastic.Firstly,the periodic component was extracted according to the periodic characteristics of the traffic flow data.Then the wavelet transform was used to decompose the remaining components into high frequency part and low frequency part.The high frequency part adopted the fractional gray prediction and the low frequency part adopted ARMA prediction.Finally,the residual component prediction value and the periodic component were accumulated to obtain the final prediction value.The simulation results demonstrate that this combination model can predict the short-term traffic flow more accurately,which is of great significance to the improvement of the traffic.
作者
刘百秋
钟晓玲
赵敏
LIU Baiqiu;ZHONG Xiaoling;ZHAO Min(College of Information Science and Technology, Chengdu University of Technology, Chengdu, Sichuan 610059, China;School of Electrical Engineering and Information, Sichuan University, Chengdu, Sichuan 610065, China)
出处
《宜宾学院学报》
2018年第12期51-55,110,共6页
Journal of Yibin University
基金
四川省交通科技项目(2013c7-1)
关键词
高速道路
短时交通流量预测
分数阶
ARMA
小波变换
周期分量
express way
short-term traffic flow forecast
fractional order
ARMA
wavelet transform
periodic component
