计算机技术与发展2017，Vol.27Issue(1)：169-172,4.DOI:10.3969/j.issn.1673-629X.2017.01.038
基于ARIMA和小波神经网络组合模型的交通流预测
Traffic Flow Prediction Based on Hybrid Model of ARIMA and WNN
摘要
Abstract
Aimed at the limitation of low prediction accuracy at the present stage of city road traffic,a prediction method is proposed based on Hybrid Autoregressive Integrated Moving Average ( ARIMA) and Wavelet Neural Network ( WNN) to predict traffic flow. Using the good linear fitting ability of ARIMA and the strong nonlinear mapping ability of WNN,the traffic flow time series are considered to be composed of a linear autocorrelation structure and a nonlinear structure. ARIMA model is used to predict the linear component of traffic flow time series and the wavelet neural network model is applied to the nonlinear residual component prediction. The simulation results show that the hybrid model can produce more accurate prediction than that of single model,which improves prediction accuracy of traffic flow prediction,and it’ s an efficient method.关键词
交通流预测/差分自回归滑动平均模型/小波神经网络/组合模型Key words
traffic flow prediction/ARIMA model/wavelet neural network/hybrid model分类
交通工程引用本文复制引用
成云,成孝刚,谈苗苗,周凯,李海波..基于ARIMA和小波神经网络组合模型的交通流预测[J].计算机技术与发展,2017,27(1):169-172,4.基金项目
国家自然科学基金资助项目(61401236) （61401236）
南京邮电大学引进人才项目(NY214005) （NY214005）
